April 25th, 2011, 03:42 PM
Let’s say, state A is the opposite of state B and state B is the opposite of state A. Normally if each of the state has an opposite results of themselves to an extent that the result of a mathematical method in state A can be obtain in state B and the result of a mathematical method in state B can also be obtain in state A.
-Is this in relation with quantum entanglement in any way?
Sorry, I can't understand what you're saying well enough to answer that question.
April 25th, 2011, 06:35 PM
No.Originally Posted by Mixter
Quantum entanglement can actually be stated in a clear well-defined manner.
I mean for every given input, the result in A is opposite that of B and the result in B is opposite that of A. At a point, the result in B is achieved in A without shifting to B and the result in A is achieved in B also without shifting to A.
What does that suppose to mean literally? Is it related with quantum entanglement in any way?
Still can't understand what you're talking about.
In the new algorithm; A and B are two parts of the same entity such that a change in one is instantly reflected in the other and the other immediately fixes its results to the opposite value.
Please, I need someone who can help to review the new algorithm.
Anybody who is interested to assists the project can PM me. Thanks.
What algorithm? You haven't given enough information here to make sense of what you've said.
May 24th, 2011, 08:36 AM
Yes it might not make sense like you have thoughts, but that is what is happening here and that is exactly what I got!
Please I am a novice you just have to bear with me. I only claimed that it was genuine. I mean it provides the missing architecture in quantum entanglement and would not affect the existing structure.
It is an algorithm that produces two opposite results for every given input. The result in each part is not arbitrary; it’s precise and has to do with two systems of indistinguishable part which is either half integer or an integer. Though it can be reformulated so that it appears naturally.
Looking at the phenomenon from the architecture that brought about the algorithm or the math, is either that one would have to follow correctly the path of the interchanging state duality or the path of the rate of change in movement. But it is impossible to follow correctly the two paths at the same time.
However, this is an exact solution to quantum entanglement and the possibility that something can emerge from nothing. It is the password to Ai and physical immortality.
After it had been properly review we would look for a publisher though I have got one already then I will pay who ever is involved.
How about you start by clearly defining what it is?
Seriously. Nothing you've said here makes any sense. "An algorithm that produces two opposite results for any input" could simply be something like. I'm not entirely sure you know what the word algorithm means, or you're not using the way it's normally used.
It is basically about movement and set. Something that I will call three-dimensional universal model…
THE ABSTRACT:
Given that x and y are set of all numbers where x is fixed but arbitrary such that Q, R and S are different constant quantities producing corresponding plus, minus and zero values emitted from a source just like three subatomic particles –Pions.
The simulation of the actual algorithm for Q and S:
z = 0.5(x - y)
-z = 0.5(y - x)
The simulation of the actual algorithm for R:
0.5(x - y + y - x) = 0 or 0.5(y - x + x - y) = 0
Although 0.5(x - y) and 0.5(y - x) are not the actual algorithm but can give a simulation of the actual algorithm simply because the algorithms are different by turning conversely the parameters for measurement and by doing so Q and R can apparently collapse to zero when they co-exist. But for the actual algorithm that I have with me, though not included in this abstract, the algorithm used is the same all through for Q, R and S producing correspondent plus and minus values and zeros without turning conversely the parameters for measurement within each scale. Yet it is capable to produce two opposite results ‘plus and minus values’ and obviously, the opposite results of that ‘plus and minus values’ in the other scale when it is the converse. So therefore, Q and R cannot collapse to zero when they coexist in the actual algorithm simply because the algorithm would not be the converse of the other but would be the same all through in a scale.
Owing to this, there is no problem now about the converse effect on a scale to the other scale in as much “and if and only if” the algorithm responsible for each of the scale would be valid. That is to say that the actual algorithm can exist in two different forms and the two forms would still be valid where one would be the converse of the other to produce two different scales.
Here ‘x’ is the input.
P.T.O: Without turning scale A to scale B or vise versa each of the scale later pretends to turn to the other.
Scale A:
Q implies 0.5(x - y) = -z to z
R implies 0.5(x - y + y - x) = 0
S implies 0.5(y - x) = z to -z
Conversely,
Scale B:
Q implies 0.5(y - x) = z to -z
R implies 0.5(y - x + x - y) = 0
S implies 0.5(x - y) = -z to z
This is the co-existence of two different quantities Q and S where quantity R is the correlation between them producing zero for every output of plus and minus values.
Thus for every given input x, you will see that a change in one scale is instantly reflected in the other scale and the other scale immediately fixes its results to the opposite value.
After this we would write a simple computer program of the actual algorithm that will test whether it is valid or not.
THE PARADOX:
From observation in the actual algorithm; scale A does not coexist with scale B in any way or vice versa and no collapse here. They are separated faster than the speed of light transmission of information. In one reality, it’s like one would only exist if the other should exist and this takes place faster than anything. Nevertheless, both seem to exist together.
So we can start building its blocks! The advantage is that it would be faster and can be considered cheaper. Thank you.
Your 'algorithms' are just simple equations, and are used nonsensically. R is always 0 as written and doesn't tell you anything. Q and S are not properly defined and definitely don't equal "-z to z" whatever that's supposed to mean. Also, A and B aren't defined at all.
Let’s assume that x = 2.0 and y = 5.0
For the simulation of the actual algorithm for Q and S:
z = 0.5(x - y)
-z = 0.5(y - x)
P.T.O: AB and BA is just a symbolic representation of the actual algorithm.
Scale A:
Q compute with the actual algorithm AB will imply that:
0.5(2.0 – 5.0) = -1.5 to give 1.5 when x = 2.0 and y = -1.0
R compute with the actual algorithm AB will imply that:
0.5(5.0 – 2.0) = 1.5 to give -1.5 when x = 2.0 and y = -1.0
Scale B:
Q compute with the actual algorithm BA will imply that:
0.5(5.0 – 2.0) = 1.5 to give -1.5 when x = 2.0 and y = -1.0
R compute with the actual algorithm BA will imply that:
0.5(2.0 – 5.0) = -1.5 to give 1.5 when x = 2.0 and y = -1.0
For the simulation of the actual algorithm for R:
0.5(x - y + y - x) = 0 or 0.5(y - x + x - y) = 0
When you apply this will always gives 0.
You will observe that in the simulation of the actual algorithm the parameters for measurement are the converse of the other within each scale but in the actual algorithm it isn’t that way e.g.
In the simulation of the actual algorithm:
Scale A:
Q computes with algorithm AB to give +/- values
R computes with algorithm AB, BA to give 0 values
S computes with algorithm BA to give -/+ values
Scale B:
Q computes with algorithm BA to give -/+ values
R computes with algorithm BA, AB to give 0 values
S computes with algorithm AB to give +/- values
In the actual algorithm (though not included in this abstract)
Scale A:
Q computes with algorithm AB to give +/- values
R computes with algorithm AB to give 0 values
S computes with algorithm AB to give -/+ values
Scale B:
Q computes with algorithm BA to give -/+ values
R computes with algorithm BA to give 0 values
S computes with algorithm BA to give +/- values
So with this A and B are defined.
Either you have no idea what you're talking about, or you still aren't giving enough information to make sense, or, more likely, both. :?
What in the world makes you think that is related to quantum entanglement?
All you did was come up with some random variables and plug in values that resulted in 2 different values on opposite sides of the number line and the one in-between (0). Also if you disregard the order, scales A and B are EXACTLY the same.
Mixter, I think you are misusing the word "algorithm". Your thoughts are probably insightful but it's hard to tell what you mean to say, because you keep using that word and it doesn't mean what you seem to think it means. Please try and choose another word if you can, perhaps one that describes better what you're trying to tell us.
The word "model" comes to mind, but I'm not sure if that's the one you want or not.
This is a new concept that has never existed on pages of text books. I think you have to bear with me from my oddball angle. I don’t mean to disseminate information in such manner but I am translating it directly the way it appears. If you have any other better translation other than this please let know and if what I have been saying is intriguing, I think what I am trying to say is that:
Q, R and S represent three different constant quantities respectively i.e. certain three different numbers that would not be changed so remains the same but would be able to transform itself from one position to another determined by some sequence of operations.
Assuming for example a symbolic representation ‘CA’ or conversely AC is the actual algorithm, a simple equation or a mathematical method in any way you may want to call it, is used with each of the Q, R and S to perform some sequence of operations producing opposite results with zeros values respectively.
In the exact solution (though not included in this abstract)
Scale A:
Q computes with CA to give minus (–) values when x > y and plus (+) values when x < y
R computes with CA to give 0 values
S computes with CA to give plus (+) values when x > y and minus (–) values when x < y
And conversely,
Scale B:
Q computes with AC to give plus (+) values when x > y and minus (–) values when x < y
R computes with AC to give 0 values
S computes with AC to give minus (–) values when x > y and plus (+) values when x < y
Thus without turning scale A to scale B or vise versa obviously, the results in each of the scale PRETENDS to exist in the other AT A POINT.
Now considering a real life scenario, I don’t think that A simultaneously exists with B or vice versa because A is the converse of B while B is the converse of A and both of them cannot exist together at the same moment unless we are crazy enough to believe that they do.
Also in EACH scale you will see that Q and S coexist but in opposite directions while R is apparently the point of correlation or the link between them.
But practically, quantity Q and S cannot re-coexist conversely to form BOTH A and B. If they do, it would be absurd. Yet they seem to re-coexist, conversely forming two different parts A and B just because the method involves can exist in two ways.
Such weird behavior suggests that there existence is so close that it’s hard to distinguish because of the great speed involved so that you hardly notice the difference. Like one turns to the other and the other turns to one at a great speed faster than the speed of light transmission of information. Overall function must be antis metric with respect to exchange of the two indistinguishable A and B. They are in some way faster than light link!
On this note each A or B is assigned to a NUMBER LINE and when moving from a negative infinity to a positive infinity or either way round. One of this infinity on the number line, and as well with their corresponding values must be REAL while the other parts would be NON-EXISTENT ILLUSSION or can be refer to as NOTHING because A and B can not simultaneously exist together.
Since numbers can be run from a negative infinity to a positive infinity on the number line and vice versa. Based on this fact, it reveals that numbers can emerge or can be RUN from non-existent illusion showing that something can emerge from nothing.
THE CHOICE MECHANISM:
Which one would be the truth let say between AC and CA if both of them can exist in two forms? –But certainly not simultaneously together bringing us to a choice mechanism for artificial intelligence.
Also, computer should be able to choose its own truth e.g. between CA and AC when they are in superposition.
I wonder the one that it will eventually choose as its own truth. If it chooses one path even though it is random then it has A CHOICE MECHANISM else it wouldn’t have chosen any one either.
So, A and B are symbolic representations of possibilities and that is the way I see it.
If you have any support or other better translation and contradicting opinion let me know with your reasons.
Are you perhaps trying to describe the collapse of the wave-function in quantum mechanics?
SpeedFreek, what does it looks like and why? Entanglement is a strange world.
All I can be sure in the concept is that when A or B is measured, a change in one is instantly reflected in the other and the other immediately fixes its results to the opposite value for every given input x.
If this is in a way related to quantum entanglement then we can as well publish it and create the model (a universal model of related features)
I need someone or group of expert that can help to assist and review the concept. Anyone interested can let me know.
Please you can send me a private message. Thank you for your participation.
Ouch!
No, listen. You seem to be making a, how shall I put it, very naive attempt at describing what we already know about entanglement with your own simple mathematics.
It doesn't work like that.
To put it bluntly, a good start would be to study the actual mathematics of quantum mechanics.
http://en.wikipedia.org/wiki/Mathema...ntum_mechanics
And you do know entanglement works backwards through time, don't you?
I'd also like to point out that when you have numbers in a computer, or on paper, it's trivial to make two equations change at the same time by having them depend on the same variables (unless you want to argue about the time it takes all the electrons in the computer to move around). As described, this algorithm does absolutely nothing interesting.
Numbers and equations like this don't implicitly carry meaning, especially when applied to physics. They're only meaningful if you can use them to predict new things.
MagiMaster, I am getting some facts please come along you are making sense.
SpeedFreek, though sound to be a very naïve attempt… but information is very crucial may be its just some simple variables.
I would want us to consider the following reasons why the actual mathematics cannot be revealed now:
First, the abstract for ‘A and B’ was structured properly to describe everything all, if the abstract of the concept does not make sense then the actual mathematics that serves as the blueprint certainly would not.
Second, the architecture of the concept that resulted to the abstract works backwards through time it’s like DEJAVU but typically attributes of a class are encapsulated it would not make sense if we can’t see the revelation about its results.
Third, in essence what I actually wanted to prove is that something can evolve from nothing -a non-existent illusion. If that can be seen clearly enough, I will release the actual mathematics or else it will be of no value.
Fourth, having had a good enough reason to show that what I am saying is right. In case of any DOUBT we can test through a COMPUTER PROGRAM, the actual mathematics that I have writing with JAVA.
ABSRACTIONS FOR EVERY GIVEN INPUT X:
This is the basis of the concept. The idea is that if you “jiggle” either A or B over here, you’ll automatically “jiggle” one of them over there simultaneously, so that a change in one automatically affects the other.
So when A and B are measured, a change in one is instantly reflected in the other and the other immediately fixes its results to the opposite value for every given input x.
For A, since this is possible;
Q implies (-z) to give (z)
R implies 0 to give 0
S implies (z) to give (-z)
Then for B, this is also possible;
Q implies (z) to give (-z)
R implies 0 to give 0
S implies (-z) to give (z)
According to the math A and B exist simultaneously based on strong premises, because the result in one is possible in the other and the algorithm concern is possible in two different ways.
Thus WITHOUT turning A to B or vise versa, the results in A and the result in B PRETENDS to exist AT A POINT in themselves.
Note that the corresponding plus or minus values from each Q and S that pretends to turn or “to give” the other is REAL while the one that is being giving birth to would be VIRTUAL because A doesn’t turn to B and B doesn’t turn to A.
ANALYSIS:
On this note each A or B is assigned to a NUMBER LINE and when moving from a negative infinity to a positive infinity or either way round. One of this infinity on the number line, and as well with their corresponding values must be REAL while the other parts would be NON-EXISTENT ILLUSSION or can be refer to as NOTHING because both A and B exist simultaneously so we do not turn A to B. They only pretend to turn to each other. Reason is that A is the converse of B while B is the converse of A and both of them cannot exist simultaneously from THE SAME source. Humanly speaking it’s impossible but the math says that it was right.
Since numbers can be run from a negative infinity to a positive infinity on the number line and vice versa. Based on this fact, it reveals that numbers can evolve or can be RUN from non-existent illusion showing that something can evolve from nothing.
Please I need to be sure.
Does the abstract show that something can evolve from nothing i.e. numbers evolving from non-existent illusion? …for now, that is the only thing I can be sure of.
From observation (though not compulsory):
Communication would be from right hand side of A to left hand side of B and from left hand side of B to right hand side of A passing through a non-existent illusion. Also from left hand side of B to the right hand side of A and from the right hand side of A to the left hand side of B passing through a non-existent illusion.
PARADOX:
Though if it is possible that A and B can exist simultaneously according to the math, there might not be a spooky action but how would we know this since the other one would always be there and can always be mistaken for a spooky action.
However I think A and B must have been using a certain belief system. So, one part would exist if and only if the other part should exist just like a spooky action but they use the mathematics to communicate.
Well, I think it's safe to answer the title question: No.
It looks like the ravings of a lunatic.
The subsequent posts do nothing to mitigate that impression.
Well thank you for all your comments.
I ‘d like to point out that in the ACTUAL mathematics there are two possible equations by having them depend on the same variables, so a change in one is instantly reflected in the other while the other would immediately fixes its results to the opposite value.
So I wonder if these two equations can change at the same time, or one after the other.
Though I think it would be one after the other but which one would come first under the same conditions? This could lead to something very interesting…
Also, I want to use this phenomenon to CONQUER the pirating of software later in the future.
The abstract of the concept is structured properly to describe everything all, if it does not make sense then the actual mathematics that serves as the blueprint certainly would not.
Any help from any body in a way or help from experts would be appreciated.
As you can see the work is already completed all i need now is a proper review of the concept.
To begin with, you might want to read up on how entanglement actually works.
Second, good luck using it to CONQUER software anything. That would fall under quantum computing, and regardless of whether you could invent new DRM schemes for a quantum computer, anyone with the hardware to run it could begin inventing quantum schemes to crack it. BTW, quantum computers are not yet practical.
There is no doubt about that more intensive reading on quantum entanglement would be involved but I would not directly use quantum computers to conquer the pirating of soft ware to stop piracy, although the features would be part of the basics in which quantum computers would be built upon.
When I say pirating… I mean replicating... So that to copy sound, visuals, pictures and software in general from any device whatsoever would be impossible unless they are granted the privilege to do so. By so doing, intellectual properties can be protected.
Since unit of electronic signal would be unique and distinct for every compartment. What I needed is just to code the “anything” that could evolve from any electronic signals with my so called algorithms. Therefore without having need for a quantum computer they can still be run via different terminals, computers, television, radio, phones etc… using various existing devices like flash drives, hard disc, writable and rewritable disc, audio and video tapes and what have you.
The technique is simple; after I must have superposition the two states A and B from “anything of such”, a spooky action would nullify them. Hence they will both collapse.
I think that should come second if I would have to publish the concept.
Presently, the PROOF for the actual algorithm is working with floating point numbers and can correct floating point error. I have written a program with java using the actual algorithm to show that what I have been saying is correct as follows.
SUMMARY OF THE MODEL FOR THE ACTUAL ALGORITHM TESTED ON FLOATING POINT NUMBERS
First of all the actual algorithm is any formula that satisfy the coexistence of Q, R and S. While any formula that does not satisfy the condition for the coexistence of Q, R and S is the simulation of the actual algorithm tagged “simulation” and such formula would apparently make the plus and minus value absurd, ambiguous or pointless on the number line.
The condition for coexistence of Q, R and S without collapsing to zero:
This is only possible if and only if plus (+) and minus (-) values comes from the same algorithm but different functions Q, R and S otherwise when they coexist they can collapse to zero. Floating point accuracy is very SENSITIVE enough to observe changes. It enables us to test how genuinely it is and shows that the condition is possible because when a whole number or half of a whole number is input via floating point numbers in the model plus and minus values always give the same amount. As a result, they have THE SAME algorithm but when a decimal number that is not half of a whole number is input they don’t always gives the same amount. In this case they have DIFFERENT functions. So Q, R and S can coexist without collapsing to zero.
To test for the actual algorithm and the condition for coexistence of Q, R and S without collapsing to zero in a given scale A or B.
P.T.O (R will always gives zero value)
When x = 2 and y = -13
Simulation -z: -7.5
Actual algorithm -z: -7.5
Simulation z: 7.5
Actual algorithm z: 7.5
When x = 0.1 and y = -13
Simulation -z: -6.550000000745058
Actual algorithm -z: -6.550000000000001
Simulation z: 6.550000000745058
Actual algorithm z: 6.55
When x = 3.8 and y = -13
Simulation -z: -8.399999976158142
Actual algorithm -z: -8.399999999999999
Simulation z: 8.399999976158142
Actual algorithm z: 8.4
This also shows the coexistence of plus and minus values.
Thus simulation is the normal output of the floating point from command prompt while the actual algorithm corrects the floating point error on that same command prompt.
FLOATING POINT NUMBERS RESPONDING TO STIMULUS…
STIMULUS IS THE TRUNCATIONS THE SEQUENCE HAS RECEIVED.
Observation: (The effect of the different quantities Q, R and S)
In the actual algorithm;
1. When a whole number or half of a whole number is input plus and minus values always give the same amount meaning that they must have had THE SAME algorithm.
2. When a decimal number that is not half of a whole number is input they don’t always give the same amount. In this case they have DIFFERENT functions e.g. Q, R and S. Otherwise in the actual algorithm, plus and minus values would have simultaneously results to the same amount even when a decimal number is input. So the algorithm in the actual algorithm for each Q, R and S is THE SAME and when a decimal number is input, the TRUNCATIONS are caused by differences in their quantities in regards to Q, R and S.
Inference: Q and S coexist in opposite directions while R is apparently the point of correlation or the link between them.
Conclusion: In the actual algorithm, plus and minus values comes from the same algorithm but different functions. Therefore Q, R and S are valid and are homogenous so can coexist without collapsing to zero.
THIS PROVES THAT THE ACTUAL ALGORITHM IS VALID.
Therefore when A and B are measured, a change in one is instantly reflected in the other and the other immediately fixes its results to the opposite value without collapsing.
I'm not quite sure what you think is so magical about floating point numbers, or even if you understand what that term actually means.
Floating point numbers is computer’s realization techniques. They does not actually exist in a real life scenario they have a seemingly nondeterministic behavior and are generated by computers based on what they feel and the reason why they do this is ultimately best known to them. There is a lot to learn about floating point numbers.
Computers cannot use fractions directly using a binary base so uses decimal base as floating point numbers to interpret them. As a result of the difference between the decimal numbers that we use and computer's binary numbers small errors in floating-point arithmetic can sum up to be a big error especially when operations are perform numbers of time. This can lead to loss of significance.
I know that since this actual algorithm is aware of the arbitrary precision floating point numbers and it’s capable to round up or truncate them. These show that the actual algorithm including Q, R and S are valid. So it can also be use as an approximation algorithm mechanism on floating point numbers and a self-awareness system.
FIRST APPLICATION OF THE ACTUAL ALGORITHM (It shows that something can evolve from nothing).
Before anything we must first of all understand that there is no way to deal with this weird behavior other than numbers evolving from non-existent illusions because the math says two possible equations are possible having them depend on the same variable. Otherwise, general mathematics is wrong!
THE CONCEPT:
When A and B are measured, a change in one is instantly reflected in the other and the other immediately fixes its results to the opposite value for every given input x.
THE ABSTRACT:
Given that x and y are set of all numbers where x is fixed but arbitrary for every x and y that is chosen. Such that Q, R and S are different constant quantities producing corresponding plus, minus and zero values emitted from a source.
Here ‘x’ is the input.
The simulation of the actual algorithm for Q and S:
±z = 0.5(x - y)
±z = 0.5(y - x)
The simulation of the actual algorithm for R:
0.5(x - y + y - x) = 0 or 0.5(y - x + x - y) = 0
Although 0.5(x - y) and 0.5(y - x) are not the actual algorithm but can give a simulation of the actual algorithm simply because the algorithms are different by turning conversely the parameters for measurement and by doing so Q and R can apparently collapse to zero when they co-exist.
PLEASE READ VERY CAREFULLY.
HOW THE ACTUAL ALGORITHM IS STRUCTURED.
1. Since there are two possible equations by having them depend on the same variables.
2. And there is only one possible scale on the number line i.e. is either “plus and minus” or “minus and plus” on a number line.
3. While numbers is normally from negative infinity to positive infinity on the number line.
Therefore let A simultaneously exists with B.
For A, since this is possible;
Q implies (-z)-----------REAL to (z)----------- ILLUSION
R implies 0--------------REAL to 0------------ ILLUSION
S implies (z)------------REAL to (-z)-----------ILLUSION
Conversely,
Then for B, this is also possible;
Q implies (z)-------------REAL to (-z)-----------ILLUSION
R implies 0---------------REAL to 0-------------ILLUSION
S implies (-z)------------REAL to (z)------------ILLUSION
This is logical. A can clearly turn to B.
For A, since this is possible;
Q implies (-z)-----------REAL
R implies 0--------------REAL
S implies (z)------------REAL
And conversely,
Then for B, this is also possible;
Q implies (z)-------------REAL
R implies 0---------------REAL
S implies (-z)------------REAL
When A and B are measured, a change in one is instantly reflected in the other and the other immediately fixes its results to the opposite value for every given input x.
According to the math A and B exist simultaneously based on strong premises, because the result in one is possible in the other and the algorithm concern is possible in two different ways.
Thus WITHOUT turning A to B or vise versa, the results in A and the result in B PRETENDS to exist AT A POINT in themselves.
THE ONLY POSSIBLE SOLUTION IS THAT:
In A; when coming from a negative infinity before reaching the positive infinity on the number line of (y). The correspondent “PLUS and MINUS” values for Q and S respectively are ILLUSIONS. While correspondent “MINUS and PLUS” values after reaching the positive infinity and there after are REAL.
In B; when coming from a negative infinity before reaching the positive infinity on the number line of (y). The correspondent “MINUS and PLUS” values for Q and R respectively are ILLUSIONS. While the correspondent PLUS and MINUS values after reaching the positive infinity and there after are REAL.
CONCLUSION: since numbers is normally from a negative infinity to a positive infinity on the number line. Based on this fact, it reveals that numbers can evolve from non-existent illusion showing that SOMETHING can evolve from NOTHING.
PRACTICALLY:
In A; one SPIN would be represented by the correspondent “MINUS and PLUS” value while the other SPIN represented by correspondent “PLUS and MINUS” value would be illusions.
In B; another SPIN would be represented by “PLUS and MINUS” values while the other SPIN represented by correspondent “MINUS and PLUS” values would be an illusion.
By doing so we would have one spin in A and an opposite spin in B making two spin in opposite directions.
From observation (though not compulsory):
Communication would be from right hand side of A to left hand side of B and from left hand side of B to right hand side of A passing through a non-existent illusion. Also from left hand side of B to the right hand side of A and from the right hand side of A to the left hand side of B passing through a non-existent illusion.
Let’s come to think of it, if it happens to be the only algorithm that has ever explained quantum entanglement to this stage.
Can we go a head and publish the actual algorithm if this is its simulation?
Is there no way this is related to quantum entanglement, does it mean no relationship at all?
Clearly there’s some sort of relationship between the two different A and B. We’re simply creating a way for us to perceive the Oneness that’s already present.
HERE IS A GENERAL OVERVIEW OF HOW THE ACTUAL ALGORITHM WORKS.
In the actual algorithm, the measurement perform on spatially separated parts have an instantaneous influence on the other and the other would immediately fix its results to the opposite value.
The spooky action in quantum entanglement would simply be as a result of two opposite different equations that depend on the same variables. Thereby, a change in one is instantly reflected in the other while the other would immediately fix its results to the opposite value. The act of measuring a superposition system or stateless system set the state of the system into two states A and B. Otherwise when you do not measure, they spread out.
After measurement, A and B does not simultaneously exist together.
Thus a state would solve and get value at the same moment for the other state to solve and get its own value like in the architecture that I have.
This leaded me to design a path in which they must have followed. This path is the architecture of the actual algorithm in my concept and it has a pattern. That is “in the architecture of the actual algorithm”, it is impossible to follow both the path of the movement of the pattern and the path of its position at the same moment. For example; if you count the next step from 7 as 8 it would loose its position and gain movement but if you count the next step from 7 at where you suppose to count 9 it would gain its position and loose its movement. Also the more steps you take, the further to achieve either the movement or the position of the pattern.
Therefore, quantum entanglement can be interchanging of two states. The result in each part is not arbitrary; it’s precise and has to do with two different systems of indistinguishable part which is either half integer or an integer. Though, it can be reformulated so that it appears naturally.
Basically the math is all about movement and set. I mean the movement of certain different quantities Q, R and S passing through what does not exist to existence.
Though, I am aware that quantum entanglement actually works backwards through time. As well as the architecture of the actual algorithm works backwards through time. In the architecture that brought the actual algorithm, the past state of existence always keeps itself as part of the present state just like a memory but I’m not too sure if that is a past for the state to go backwards through time.
The graphical representation of the math is a conic section analysis of the hyperbolic appearance of circles for example.
Having known that x and y are set of all numbers where x is fixed but arbitrary for every x and y that is chosen. Such that Q, R and S are different constant quantities producing corresponding plus, minus and zero values emitted from a source.
The simulation of the actual algorithm for Q and S:
±z = 0.5(x - y)
±z = 0.5(y - x)
This is not the actual algorithm but can give a simulation of the actual algorithm.
Pleae note that an equation that does not truncate or roundup arbitrary precision floating point numbers when decimal number is input is not genuine e.g. The actual algorithm approximate 8.399999976158142 to 8.4, also truncate 6.550000000745058 to 6.55 etc…as shown in the earlier post. This is the only algorithm that has ever done this on arbitrary precision floating point numbers!
This also confirms the actual algorithm is valid.
THE SEQUENCE: For either A or B.
When chosen index number or input is 1. (x = 1 and y = indexes; that is all possible numbers on the number line)
…coming from a negative infinity.
Index(y): Expansion (Value):
-3...............2.0, 0, -2.0
-2...............1.5, 0, -1.5 (Non-existent illusion region or can be considered as Invisible)
-1...............1.0, 0, -1.0
0............... 0.5, 0, -0.5
1................0.0, 0, 0.0 HERE IS THE TURNING POINT------------------------------
2................-0.5, 0, 0.5
3................-1.0, 0, 1.0 (Reality region)
4................-1.5, 0, 1.5
5................-2.0, 0, 2.0
... going to a positive infinity
And conversely,
…coming from a negative infinity.
Index(y): Expansion (Value):
-3...............-2.0, 0, 2.0
-2...............-1.5, 0, 1.5 (Non-existent illusion region or can be considered as Invisible)
-1...............-1.0, 0, 1.0
0................-0.5, 0, 0.5
1................0.0, 0, 0.0 HERE IS THE TURNING POINT------------------------------
2................0.5, 0, -0.5
3................1.0, 0, -1.0 (Reality region)
4................1.5, 0, -1.5
5................2.0, 0, -2.0
... going to a positive infinity
The graphical representation of this sequence is a Conic Section analysis of the hyperbolic appearance of circles.
The hyperbola as a circle on the ground seen in perspective while gazing down slightly, showing circle's tangents as asymptotes.
The portion above the horizon in the graph is normally invisible.
Also in the gragh, where x < y before it touches the origin 0 is a singularity zone.
PARADOX:
Even though the actual algorithm shows that two opposite different equations can depend on the same variables so they can coexist and simultaneously give A and B but this would be absurd on the number line.
Therefore, I think quantum entanglement (A and B) must have been using a certain belief system. One part would exist if and only if the other part should exist just like a spooky action while they use two identical equations to communicate.
Based on this, if something is allowed to separate the superposition of A and B they will always end up with two areas of concentration that would be impossible to locate at the same moment. The existence of one would make the other to exist as a spooky action making it impossible to locate the two of them at the same moment.
The question is which one would exist first between A or B for example spin up or spin down?
These overall predictions can be tested to verify the actual algorithm also to verify quantities Q, R and S that forms a better theory that incorporate both quantum and relativity.
As I mentioned before, the spooky action at a distance has been shown to work backwards through time. (A delayed choice quantum eraser - Kim et al., 2000)
How does your algorithm deal with this?
If you had me before, this is where you lost me.by computers based on what they feel and the reason why they do this is ultimately best known to them.
In practical terms what I mean is not why… but how they react in such manner and tend to choose that path is not clear.
This is a technical question.
For the sake of clarity, the actual algorithm is a simple equation while what answers your question is the architecture that brought about that equation or the actual algorithm which is different from its simulation as stated earlier.
Now the whole thing lies in the architecture but that could shift our attention from the actual algorithm which is the most important thing for now.
Anyway back to your question, it would work backwards through time because there is a drive “a visualization process” that would be active if and only if the features concern is able to read itself at a certain point in space and by doing so it would carry all the possible existing solutions along side with itself for the next spooky action.
For example abstract of the architecture:
For A;
Q implies -0.5
R implies 0
S implies +0.5
Conversely,
For B; (spooky action)
Q implies +0.5
R implies 0
S implies -0.5
Let’s assume A start first. For A;
1st action:
Q implies -0.5
R implies 0
S implies +0.5
2nd action (B turns 180 degree):
Q implies -1.0
R implies 0
S implies +1.0
3rd action:
Q implies -1.5
R implies 0
S implies +1.5
4th action (B turns 180 degree): making 360 degree from the 2nd action.
Q implies -2.0
R implies 0
S implies +2.0
And so on…
PLEASE READ VERY CAREFULLY.
I’m sorry there was some mistake in the earlier post before now.
In either A or B, the total graph (that is including both reality when y > 0 and illusion when y < 0) can be traced to a hexagram.
When A simultaneously exists with B, the graph is absurd.
I have corrected some few errors in the previous post as follows:
Assuming a symbolic representation ‘CA’ or conversely AC is the actual algorithm, a simple equation or a mathematical method in any way you may want to call it, is used with each of the Q, R and S to perform some sequence of operations producing zeros values respectively.
In the simulation of the actual algorithm;
Scale A:
Q computes with CA to give minus (–) values when x > y and plus (+) values when x < y
R computes with CA, AC to give 0 values
S computes with AC to give plus (+) values when x > y and minus (–) values when x < y
And conversely,
Scale B:
Q computes with AC to give plus (+) values when x < y and minus (–) values when x > y
R computes with AC, CA to give 0 values
S computes with CA to give minus (–) values when x < y and plus (+) values when x > y
You will observe that in the simulation of the actual algorithm the parameters for measurement are the converse of the other within each scale but in the actual algorithm it isn’t so for example.
PREMISES: The spooky action in quantum entanglement would simply be as a result of two opposite different equations that depend on the same variables. Thereby, a change in one is instantly reflected in the other while the other would immediately fix its results to the opposite value.
Therefore;
In the exact solution .i.e. the actual algorithm (though not included in this abstract);
Scale A:
Q computes with CA to give minus (–) values when x < y and plus (+) values when x > y
R computes with CA to give 0 values
S computes with CA to give plus (+) values when x < y and minus (–) values when x > y
And conversely,
Scale B:
Q computes with AC to give plus (+) values when x < y and minus (–) values when x > y
R computes with AC to give 0 values
S computes with AC to give minus (–) values when x < y and plus (+) values when x > y
According to the exact solution A and B simultaneously exist based on strong premises, because the result in one is possible in the other and the algorithm concern is possible in two different ways. Simply as a result of two opposite different equations that depend on the same variables.
Thus WITHOUT turning A to B or vise versa, the results in A and the result in B PRETENDS to exist AT A POINT in themselves.
THE ONLY POSSIBLE SOLUTION IS THAT:
For A;
Q implies (-z) -----------REAL to (z) ----------- ILLUSION
R implies 0--------------REAL to 0------------ ILLUSION
S implies (z) ------------REAL to (-z) -----------ILLUSION
Conversely,
For B;
Q implies (z) -------------REAL to (-z) -----------ILLUSION
R implies 0---------------REAL to 0-------------ILLUSION
S implies (-z) ------------REAL to (z) ------------ILLUSION
This is logical. So that A can turn conveniently to B.
Therefore,
For A;
Q implies (-z) -----------REAL
R implies 0--------------REAL
S implies (z) ------------REAL
And conversely,
Then for B, this is also possible;
Q implies (z) -------------REAL
R implies 0---------------REAL
S implies (-z) ------------REAL
When A and B are measured, a change in one is instantly reflected in the other and the other immediately fixes its results to the opposite value for every given input x.
CONCLUSION: Since numbers is normally from a negative infinity to a positive infinity on the number line. Based on this fact, it reveals that numbers can evolve from non-existent illusion showing that SOMETHING can evolve from NOTHING.
When we say nonexistence there must still be an observer otherwise how would we ever know that it never exist. Something must still be moving through only that it’s beyond reach. Nonexistence is absolutely nothing; nothing existed, not space, time, matter, or energy – nothing and something must have been moving through to observe it.
Basically it is all about movement and set. I mean the movement of certain different quantities Q, R and S passing through what does not exist to existence.
THE SEQUENCE: For either A or B.
Given that x and y are set of all numbers where x is fixed but arbitrary for every x and y that is chosen. Such that Q, R and S are different constant quantities producing corresponding plus, minus and zero values emitted from a source.
The simulation of the actual algorithm for Q and S: Note R always implies zero.
±z = 0.5(x - y)
±z = 0.5(y - x)
When chosen index number or input is 1. (x = 1 and y = indexes; that is all possible numbers on the number line)
…coming from a negative infinity.
Index(y): Expansion (Value):
-3...............2.0, 0, -2.0
-2...............1.5, 0, -1.5 (Non-existent illusion region or can be considered as Invisible)
-1...............1.0, 0, -1.0
0............... 0.5, 0, -0.5
1................0.0, 0, 0.0 HERE IS THE TURNING POINT------------------------------
2................-0.5, 0, 0.5
3................-1.0, 0, 1.0 (Reality region)
4................-1.5, 0, 1.5
5................-2.0, 0, 2.0
... going to a positive infinity
and conversely,
…coming from a negative infinity.
Index(y): Expansion (Value):
-3...............-2.0, 0, 2.0
-2...............-1.5, 0, 1.5 (Non-existent illusion region or can be considered as Invisible)
-1...............-1.0, 0, 1.0
0................-0.5, 0, 0.5
1................0.0, 0, 0.0 HERE IS THE TURNING POINT------------------------------
2................0.5, 0, -0.5
3................1.0, 0, -1.0 (Reality region)
4................1.5, 0, -1.5
5................2.0, 0, -2.0
... going to a positive infinity
The graphical representation of this sequence is a Conic Section analysis of the hyperbolic appearance of circles.
The hyperbola as a circle on the ground seen in perspective while gazing down slightly, showing circle's tangents as asymptotes.
The portion above the horizon in the graph is normally invisible.
In the graph, where x is represented in the index y as {0, 0, 0} < index y (some numbers on the index) before it touches origin 0 on the index number is a singularity zone.
PARADOX:
Even though the actual algorithm shows that two opposite different equations can depend on the same variables so they can coexist and simultaneously give A and B but this would be absurd on the number line.
Therefore, I think quantum entanglement (A and B) must have been using a certain belief system. One part would exist if and only if the other part should exist just like a spooky action while they use two identical equations to communicate.
Based on this, if something is allowed to separate the superposition of A and B they will always end up with two areas of concentration that would be impossible to locate at the same moment. The existence of one would make the other to exist as a spooky action making it impossible to locate the two of them at the same moment.
The question is which one would exist first between A or B for example spin up or spin down?
The actual algorithm approximate 8.399999976158142 to 8.4, also truncate 6.550000000745058 to 6.55 etc…as shown in the earlier post.
This is the only algorithm that has ever truncate or roundup arbitrary precision
floating point numbers! This also confirms that the actual algorithm is valid.
These overall predictions can be tested to verify the actual algorithm also to verify quantities Q, R and S that forms a better theory that incorporate both quantum and relativity.
tl;dr but...
If it's not clear how a computer chooses one algorithmic path over another, then you don't understand computers. They are completely deterministic (unlike QM) and always take exactly the path that their programmers tell them to.In practical terms what I mean is not why… but how they react in such manner and tend to choose that path is not clear.
Don't get me wrong. Trying to figure out exactly why your OS just blue-screened can be a pain, but it's still only doing what its (in this case, many) programmers programmed.
tl;dr but...
If it's not clear how a computer chooses one algorithmic path over another, then you don't understand computers. They are completely deterministic (unlike QM) and always take exactly the path that their programmers tell them to.In practical terms what I mean is not why… but how they react in such manner and tend to choose that path is not clear.
Don't get me wrong. Trying to figure out exactly why your OS just blue-screened can be a pain, but it's still only doing what its (in this case, many) programmers programmed.
Okay I have heard you.
What are your views on this?
When x = 2 and y = -13
Simulation -z: -7.5
Actual algorithm -z: -7.5
Simulation z: 7.5
Actual algorithm z: 7.5
When x = 0.1 and y = -13
Simulation -z: -6.550000000745058
Actual algorithm -z: -6.550000000000001
Simulation z: 6.550000000745058
Actual algorithm z: 6.55
When x = 3.8 and y = -13
Simulation -z: -8.399999976158142
Actual algorithm -z: -8.399999999999999
Simulation z: 8.399999976158142
Actual algorithm z: 8.4
Why is it that when x is a whole number both the simulation and the actual algorithm have the same digits but only to have different digits when x is a decimal number?
Do you have any idea how floating point numbers work?
Basically, the computer keeps the numbers in scientific notation, with a fixed number of digits after the dot. It actually keeps one or two more digits than it shows though, and assumes that if everything else comes up 0, those digits are just errors, so it can show whole numbers as whole numbers. (Rough overview. If you want, I can go into more detail.)
Though i get your point but you haven’t provided the answer to my question. I mean for example:
Assuming AC is the actual algorithm (conversely, it can be CA):
For x = 2 and y = -13
When Q compute with AC = -7.5
When R compute with AC = 0
When S compute with AC = 7.5
For x = 0.1 and y = -13
When Q compute with AC = -6.550000000000001
When R compute with AC = 0
When S compute with AC = 6.55
For x = 3.8 and y = -13
When Q compute with AC = -8.399999999999999
When R compute with AC = 0
When S compute with AC = 8.4
Why is it that when x is a whole number the results in both Q and S have the same digits but tend to have different digits when x is a decimal number?
Since the result is the same when x is a whole number only that the sign changes, they suppose to have the same result even when x is a decimal number.
I can't answer your question because you haven't defined your algorithm in clear enough terms for me to understand what you're trying to do. However, without regards to your question, you seem to have some misunderstandings of how floating point numbers work. There's a chance that once you understand floating point math better, you'll be able to answer the question yourself.
No.
It's clearly stated there the abstract is clear enough and this is exactly what is happening in the actual algorithm.
What I mean is that assuming AC is the actual algorithm (conversely, it can be CA):
For x = 2 and y = -13
When Q compute with AC = -7.5
When R compute with AC = 0
When S compute with AC = 7.5
For x = 0.1 and y = -13
When Q compute with AC = -6.550000000000001
When R compute with AC = 0
When S compute with AC = 6.55
For x = 3.8 and y = -13
When Q compute with AC = -8.399999999999999
When R compute with AC = 0
When S compute with AC = 8.4
Why is it that when x is a whole number the results in both Q and S have the same digits but tend to have different digits when x is a decimal number?
Since the result is the same when x is a whole number only that the sign changes, they suppose to have the same result even when x is a decimal number.
The purpose of the so called floating point accuracy is just to prove that Q, R and S is valid. Also that it is possible for Q and S to coexist without apparently collapsing to zero .i.e. -8.399999999999999 + 8.4 = 1.7763568394002505E
and -6.550000000000001 + 6.55 = -8.881784197001252E
Therefore Q, R and S are homogenous so they can coexist without resulting to zero. Then from there something can come out from nothing e.g. like numbers evolving from non-existent illusions as shown earlier.
That is what I am trying to prove for now using two opposite different equations that depend on the same variables. Thereby, a change in one is instantly reflected in the other while the other would immediately fix its results to the opposite value. The actions can be comparing very abstractly with quantum entanglement.
1. Since Q and S gives two opposite results when x is a whole number.
This simply shows that they exist together and they are not the same quantities but different quantities.
2. Since Q and S gives different results when x is a decimal number.
This confirms that they are different quantities. Otherwise, they wouldn’t have given different results under the same condition. So when a decimal number is input for x, floating point accuracy is very sensitive enough to recognize the differences in them.
Therefore, Q and S can coexist where R that always gives zero with that same algorithm would be the correlation between them.
Let’s hope that there must have been a constant that is infinitely so small that even, the computer can not recognize when x is a whole number. Instead, when x is a whole number, Q and S would have always given different results because they are different quantities.
Now, if the universe is three dimensional we can trace it back to a single point!
What are you saying no to? There was no question in my post.
Also, it's obviously not clear enough, since I still have no idea what this "algorithm" of yours is trying to do.
Plus, it still seems like you're getting floating point errors and trying to make them out to be something significant.
Often they're programmed to roll virtual dice whenever they face a situation where two options are equally valid. You see it the most in AI's, like chess playing programs for example, because it may just so happen that two available moves have an exactly identical strategic value (according to whatever algorithm the program is using), and you wouldn't want the program to just hang and do nothing.tl;dr but...
If it's not clear how a computer chooses one algorithmic path over another, then you don't understand computers. They are completely deterministic (unlike QM) and always take exactly the path that their programmers tell them to.In practical terms what I mean is not why… but how they react in such manner and tend to choose that path is not clear.
Don't get me wrong. Trying to figure out exactly why your OS just blue-screened can be a pain, but it's still only doing what its (in this case, many) programmers programmed.
The other reason is because the unpredictability makes the AI harder to anticipate.
I can say no to anything that is wrong without any question being asked. What is wrong here is that you do not have to know math before you can discern that your mother is not equal to your father neither is your father is equal to your mother but here two different quantities Q and S are equal to themselves in opposite direction.
Q, R and S are homogeneous. They can coexist without apparently resulting to zero where R always gives zero and it is the correlation between Q and S. This is also the coexistence of plus and minus values without apparently resulting to zero.
I think you get what I am trying to say.
Normally if the results from Q and S are added up they give zero for example:
The simulation of the actual algorithm on floating point numbers would be
-6.550000000745058 + 6.550000000745058 = 0
Using the calculator we’ll have -6.55 + 6.55 = 0
However, for the actual algorithm they could give something though closer to nothing but at least greater than zero for example:
-8.399999999999999 + 8.4 = 1.7763568394002505E
And -6.550000000000001 + 6.55 = -8.881784197001252E
As you can see the actual algorithm does not give zero with floating point numbers in the addition of plus and minus values when a decimal number is input for x. Therefore, Q, R and S will not result to zero when they coexist in a real life scenario. Precise they are different quantities so why should they even though they emerge from the same source.
Notwithstanding, I am getting floating point errors and I am trying to make them significant because they are.
Once again if these abstract does not make sense then the actual algorithm that serves as the blueprint certainly would not. So it would be of no value to give out the actual algorithm and also to give out the different numbers designated for Q, R and S respectively. Q, R and S are certain different numbers.
In general, this is a model of a mathematical method that proves that in arithmetic and geometric growth mechanism all numbers can evolve from nothing i.e. numbers evolving from non-existent illusion. That is to say that at every chosen index number or input (x) in the coexistence of plus and minus values there exist a turning point of their values designated as {0, 0, 0} e.g. if x = 5 then at index 5 on the number line we would get {0, 0, 0} and if x = 79876 then at index 79876 on the number line we would also get {0, 0, 0}.
Note: Each set of zero is designated to Q, R and S respectively e.g. assuming x is n then at index n on the number line:
Q gives 0
R gives 0
S gives 0
We should not deceive ourselves including me myself. In fact, we have to be blunt and say the way it appears to be. I still don’t see any reason why all I have been saying does not portray what they are.
Cleverusername, I have been looking forward to this for years. It seems as if a real dice in computer programs is not presently in existence for now because a computer program will always be based on the math that described it and by so doing one would still be able to predict the results. Ai that can be predictable is not absolute. An absolute AI is not predictable.
The actual algorithm for the concept shows that a real dice is now possible in computer programs because there are two possible different equations by having them depend on the same variables and I wonder the one that computer would eventually choose as its own truth. If it chooses one path even though it is random that means it has a choice mechanism instead it wouldn’t have chosen any one either.
That is exactly also where I am going. The whole concept is an artificial law of nature. We shouldn’t expect the laws governing the universe that it must be complex. Information is very crucial.
@kojax, True, but even the dice are deterministic, at least most of the time. I suppose I should have added the caveat that the deterministic computer can ask outside forces to provide some nondeterminism, but I don't think the OPs algorithm does that.
@Mixter, No. Floating point errors are not significant.
Consider a system that uses 3 floating decimal digits, always truncating intermediate results.
Now, multiply the fourth root of 5 (1.495348781), four times.
- 1.495 * 1.495 * 1.495 * 1.495 = 2.235 * 1.495 * 1.495 = 3.341 * 1.495 = 4.994
Now, do the same thing, but add some parenthesis:
- (1.495 * 1.495) * (1.495 * 1.495) = 2.235 * 2.235 = 4.995
Do these algorithms give a different answer? Yes. Why? Because they use floating point numbers, with are only approximations. The floating point numbers computers use are the same way, only they use ~30 binary digits instead of 3 decimal digits. The size of the errors you're getting are consistent with floating point errors and cannot be taken as significant. Use something like Mathematica or MATLAB or something else with arbitrary precision numbers or symbolic methods and if the errors don't go away, you might have something interesting.
Please don’t get me wrong. What I am saying is that floating point error enables us to observe that Q and S are different numbers or quantities. That was the reason why the results are different when we use the same algorithm for both of them.
For example at index number 13 on the number line:
When x = 3.8
If Q gives -8.399999999999999 then S would give 8.4 and R would give 0
When x = 0.1
If Q gives -6.550000000000001 then S would give 6.55 and R would give 0.
Whereas when x = 2
If Q gives -7.5 then S would give 7.5 and R would give 0.
Also when x = 9
If Q gives -11.0 then S would give 11.0 and R would give 0.
Having not seen what Q and S are. The difference in their results for when x is a decimal number reveals to us that Q and S are different quantities or different numbers. Otherwise they would have resulted into the same figures even when a decimal number is input for x.
So floating point error enables us to see this and since they would always give two opposite results plus and minus values respectively for every input of x. Therefore, they can coexist and obviously we should not expect them to result to zero when they are added up since they are not the same quantities though they might come from the same source.
This reveals to us that Q, R and S are valid but the overall results are not mathematically smooth. Therefore in other to resolve this problem before us, the only logical solution which is visible and practical to us is that they evolve from non-existent illusions based on the fact that two opposite different equations depend on the same variables as shown in the earlier post. Thereby, a change in one is instantly reflected in the other while the other would immediately fix its results to the opposite value which the actions can be comparing very abstractly with quantum entanglement.
You can see that floating point errors can be made significant.
In regards with your suggestion I will also try it on Mathematica or MATLAB and see how it works. Thanks you for those analysis I really appreciate it.
The problem is, if the source of the difference is floating point error, then it has nothing to do with your algorithm or what you're trying to model. It is an error that exists solely because you are using a convenient approximation to real numbers and therefore is not real or significant.
If you have access to Mathematica, use the SetPrecision function to crank up the number of digits. If the error keeps getting smaller as the precision goes up, then you can be sure it doesn't really exist and only shows up because you're using approximations. If it levels off, there may be something else there. I'm sure MATLAB can do the same thing, but I don't know which function to use. (If your algorithm is simple enough, Wolfram Alpha might work.)
Either way, the best thing to do would be to examine things symbolically, with no representation errors. Whether that's doable depends on how complex your algorithm is.
Sorry for the late reply.
When tested on MATLAB gives the following results:
>> format long e
>> format compact
>> 1.495 * 1.495 * 1.495 * 1.495
ans =
4.995336750625001e+000
Now, do the same thing, but add some parenthesis:
>> (1.495 * 1.495) * (1.495 * 1.495)
ans =
4.995336750625001e+000
As well as:
>> fprintf('answer is %1.10e\n',1.495 * 1.495 * 1.495 * 1.495 )
answer is 4.9953367506e+000
Also, do the same thing, but add some parenthesis:
>> fprintf('answer is %1.10e\n',(1.495 * 1.495) * (1.495 * 1.495) )
answer is 4.9953367506e+000
>> format long e
>> format compact
Note R always produces 0 for every result from Q and S.
---------------------------------------------------------------
When x = 2.0
Q produces:
>>
ans =
-7.500000000000000e+000
S produces:
>>
ans =
7.500000000000000e+000
----------------------------------------------------------------
When x = 3.8
Q produces:
>>
ans =
-8.399999999999999e+000
S produces:
>>
ans =
8.400000000000000e+000
----------------------------------------------------------------
When x = 3.8
Q produces:
>> fprintf('value of z is %1.10e\n',-z)
value of -z is -8.4000000000e+000
S produces:
>> fprintf('value of z is %1.10e\n',z)
value of z is 8.4000000000e+000
------------------------------------------------------------
When x = 3.8
Q produces:
>> fprintf('value of z is %1.30e\n',-z)
value of -z is -8.399999999999998600000000000000e+000
S produces:
>> fprintf('value of z is %1.30e\n',z)
value of z is 8.400000000000000400000000000000e+000
-----------------------------------------------------------
Basically, the assumption is if Q and S are different numbers and they always produce the same opposite results they can coexist.
The idea is that if Q and S are different numbers such that under the same condition they produce the same results let say Q produce -7.5 and S produce +7.5 therefore Q and S exist together but since they produce different result when a decimal number is input e.g. Q produce -8.399999976158142 and S produce 8.4 This confirms that Q and S are actually different numbers therefore they can coexist. So the assumption is right!
And if I show the actual number for Q, R and S with their simple algorithm mechanism you will see that Q, R and S are actually different numbers. So they are homogeneous.
This is also a time that artificial intelligence would be absolute and human would coexist with them.
In a layman language way as follows:
Holistically, the variables concern in this math would make identical copying of systems, software hacking to be impossible because the entangled pair would instantly know that its pair had been tampered with. For example, if photon 1 and 2 are entangled because of their anti-spin correlation.
Let’s consider that;
If not measured and observed:
A (photon 1) imply {0, 0, 0} and B (photon 2) imply {0, 0, 0}
If measured and observed:
A (photon 1) imply {-0.5, 0, 0.5} and B (photon 2) imply {0.5, 0, -0.5}
Measuring one member of the pairs tells us what spin the other member would have if it were also measured.
Now the question is, can we measure and observe both pair simultaneously?
You missed something here. The 1.495 example only works if you're using 3 floating decimal digits. Increasing the digits here, by typing it in to a calculator or program for example, messes up the example.Sorry for the late reply.
When tested on MATLAB gives the following results:
>> format long e
>> format compact
>> 1.495 * 1.495 * 1.495 * 1.495
ans =
4.995336750625001e+000
Now, do the same thing, but add some parenthesis:
>> (1.495 * 1.495) * (1.495 * 1.495)
ans =
4.995336750625001e+000
As well as:
>> fprintf('answer is %1.10e\n',1.495 * 1.495 * 1.495 * 1.495 )
answer is 4.9953367506e+000
Also, do the same thing, but add some parenthesis:
>> fprintf('answer is %1.10e\n',(1.495 * 1.495) * (1.495 * 1.495) )
answer is 4.9953367506e+000
I don't see anywhere where you showed that the differences aren't floating point errors. Specifically, printf does not change how many digits are calculated. It only changes how many are printed.
>> format long e
>> format compact
Note R always produces 0 for every result from Q and S.
---------------------------------------------------------------
When x = 2.0
Q produces:
>>
ans =
-7.500000000000000e+000
S produces:
>>
ans =
7.500000000000000e+000
----------------------------------------------------------------
When x = 3.8
Q produces:
>>
ans =
-8.399999999999999e+000
S produces:
>>
ans =
8.400000000000000e+000
----------------------------------------------------------------
When x = 3.8
Q produces:
>> fprintf('value of z is %1.10e\n',-z)
value of -z is -8.4000000000e+000
S produces:
>> fprintf('value of z is %1.10e\n',z)
value of z is 8.4000000000e+000
------------------------------------------------------------
When x = 3.8
Q produces:
>> fprintf('value of z is %1.30e\n',-z)
value of -z is -8.399999999999998600000000000000e+000
S produces:
>> fprintf('value of z is %1.30e\n',z)
value of z is 8.400000000000000400000000000000e+000
-----------------------------------------------------------
Basically, the assumption is if Q and S are different numbers and they always produce the same opposite results they can coexist.
The idea is that if Q and S are different numbers such that under the same condition they produce the same results let say Q produce -7.5 and S produce +7.5 therefore Q and S exist together but since they produce different result when a decimal number is input e.g. Q produce -8.399999976158142 and S produce 8.4 This confirms that Q and S are actually different numbers therefore they can coexist. So the assumption is right!
And if I show the actual number for Q, R and S with their simple algorithm mechanism you will see that Q, R and S are actually different numbers. So they are homogeneous.
This is also a time that artificial intelligence would be absolute and human would coexist with them.
In a layman language way as follows:
Holistically, the variables concern in this math would make identical copying of systems, software hacking to be impossible because the entangled pair would instantly know that its pair had been tampered with. For example, if photon 1 and 2 are entangled because of their anti-spin correlation.
Let’s consider that;
If not measured and observed:
A (photon 1) imply {0, 0, 0} and B (photon 2) imply {0, 0, 0}
If measured and observed:
A (photon 1) imply {-0.5, 0, 0.5} and B (photon 2) imply {0.5, 0, -0.5}
Measuring one member of the pairs tells us what spin the other member would have if it were also measured.
Now the question is, can we measure and observe both pair simultaneously?
Checking with Google, it looks like you need to get the Multiple Precision Toolbox to be able to do this with MATLAB. (It doesn't seem to have arbitrary precision built in.)
Thank you MagiMaster.
There is power in measurement what ever you measured is what goes on. The accuracy of a number is a measure of how close an approximation is to its actual value.
The math is; the facts are. The equation that is responsible for these differences must be close to that of the underlying computer’s floating point arithmetic because two different numbers Q and S “even though they are different numbers” always produce the same results but in opposite direction when a whole number is input for x. Since this is the case for when x is a whole number, if Q and S aren’t different numbers there wouldn’t have been differences now revealed by floating point errors when a decimal number is input for x.
Floating point error expose that Q and S are different quantities when x is a decimal number and this apparently has nothing to do with the model.
Now the question is, can we measure and observe both pair simultaneously?
Floating point errors only say that the floating point approximation is causing errors. You can't conclude anything other than that.
So, what is stopping them I mean “Q and S” not to have the same digit when x is a decimal number since they have equal digit when x is a whole number?Floating point errors only say that the floating point approximation is causing errors. You can't conclude anything other than that.
Q, R and S are actually different numbers therefore they can coexist without collapsing where R that always produces zero is the correlation between them.
If I publish it you will see what numbers they are.
It doesn't matter what the numbers are and I'm getting tired of repeating myself.
All you've shown is that your algorithm suffers from floating point errors. That is, errors caused solely by the use of the floating point approximation. When using whole numbers of small enough magnitude, the errors are small enough for the computer to get rid of. For other numbers (and you can test this with a large whole number, like 3457 or something) it can't.
This proves nothing about anything physical as the real world does not using floating point arithmetic.
I still do not satisfy with that answer not even up to 1%.
You have not answer my question at all.
Why is it that we have equal number of digit in both sides when a whole number is input for x but now different number of digit in both sides when a decimal number is input for x? It shouldn't be so unless one is crazy. The number of digit should also be the same in both side even when x is a decimal number since it is the same for whole number. Then after this it can suffers from floating point errors whatever. That means there is a difference some where and must be caused by something which i identify as Q and S.
Also, I know that floating point numbers does not exist but it very obvious they are significant in this case.
This is a million dollar question.
Come to think of it assuming z is emitted from a source to produce two opposite pair of results via floating point numbers by each member having EQUAL number of digits when a whole number is input for x. If that same z, under the same condition, produces two opposite pair of results by each member having DIFFERENT number of digits when a decimal number is input for x. Then this shows clearly that there is a difference some where otherwise there is nothing stopping them to have EQUAL number of digit in both sides even when a decimal number is input for x. Just like when a whole number is input for x.
Now if z could be split into two parts let say Q and S to form different quantities or different numbers respectively. In this way the difference is yet obvious but reasonable; leaving us to OBSERVE that there is a difference when using an exact floating point approximation algorithm which floating point error allow us to see.
Therefore what does not exist is made to exist at a point in space.
Apart from this, any other explanation on why the digit aren't the same when x is a decimal number is currently not known and it is ultimately best known to floating point numbers.
However, my view on this is that the difference is as a result of the equation responsible which floating point error only allows us to see. Otherwise if it were as a result of the floating point error the results must have been different as well when a whole number is input for x since they are what they are meant to be. So this is the only way floating point can interpret Q and S in a smaller scale by showing that Q and S are different also when a decimal number is input for x.
In fact, if there is no sort of difference some where along the line. The number of digit would always remains the same and such difference definitely would not come from floating point errors since they are not tempered with.
So floating point error in this context enables us to see that P and Q are different numbers which can unites this is seen when a whole number is input for x, they give equal digit in both side.
In regards to the after math what does not exist is made to exist.
Look, I'm done. I've explained this repeatedly and you just don't want to get it. You want your idea to be something, and it isn't.
- Floating point numbers aren't special.
- They aren't related to anything real.
- Floating point errors prove nothing.
- And you won't take the steps I suggested that would prove these are solely floating point errors. (You did open MATLAB, but like I said, printf doesn't cut it.)
There is nothing more to be said.
Yes, those errors were caused solely by the use of the floating point approximation agreed but the floating point approximation should be uniform, proportional,
consistence and constant in both sides unless you do not believe again in what you know.
Newton's laws of motion states that:
To every action there is always an equal and opposite reaction.
Every action is accompanied by a reaction of equal magnitude but opposite direction.
Also the forces of two bodies on each other are always equal and are directed in opposite.
Can you write a simple equation that when x is 3.8 via floating point numbers
it gives (-8.399999999999999 and 8.4)
and when x is 2 it gives (-7.5 and 7.5).That is what i am trying to say.
It's new no one has ever come up with such equation before. Don’t be skeptical you are trying to deny the truth of revelation.
Yet, there is always arbitrary differences in both sides so if printf doesn't cut off shouldn’t be a big deal and those steps that you suggested doesn’t matter since we are using the available evidence to prove a point.
I need someone who has something tangible to offer.
If any one is interested they can contact me through mark01gate@yahoo.com
What is it for me to say again? It is left for us to decide.
Thanks.
July 6th, 2011, 04:19 AM
it depends on the what both states A and B actually are. for instance;
is state A iscold
and state B is hot, then no.
but if they have a connection to somthing in the quantum genre, then yes.
e.g two oppisite dimensions. (for instance)
July 6th, 2011, 11:36 AM
Aah! This topic had been moved here… while superdeterminism is… It’s just throwing our hands up in the air.
What I am trying to say is, let consider that two equations are derived from a certain arithmetic and geometric growth mechanism. Such that each of the equation uses the same variable x to computes their results. So that for every given input x, there is an equal and opposite result from both equation.
If a whole number is input for x via floating point numbers, the result from both equations would be exactly the same.
For example, when x is 7.0 via floating point numbers;
One of the equations would give (-3.0)
The other equation would give (3.0)
Whereas if a decimal number is input for x via floating point numbers, the result from both equations would appear to be different.
For example, when x is 7.2 via floating point numbers;
One of the equations would give (-2.9000000000000004)
The other equation would give (2.8999999999999986)
This should not be so because the result from both equations should always be proportional.
Newton's laws of motion states that:
To every action there is always an equal and opposite reaction.
Every action is accompanied by a reaction of equal magnitude but opposite direction.
The forces of two bodies on each other are always equal and are directed in opposite.
Since the results from both equations via floating point numbers is different for when x is a decimal number. Therefore it shows clearly that the two set of equations are not the opposite of one another and they are not the same thing. So the arbitrary difference from both equations is NOT as a result of floating point approximations but as a result of the equation that is concern.
I thereby recognize the difference as a result of the two different numbers or different quantities Q and S which shares a certain algorithm also to give equal opposite results in a certain arithmetic and geometric growth mechanism.
On this note, floating point numbers allows us to observe that there is a difference in the two set of equation which is as a result of different numbers or different quantities Q and S.
Thus Q and S can coexist without apparently collapse to zero.
This forms the basis of the discursion that there are two possible equations by having them depend on the same variables, so a change in one is instantly reflected in the other while the other would immediately fixes its results to the opposite value. The actions can be comparing very abstractly with quantum entanglement. So I wonder if these two equations can change at the same time, or one after the other.
Though I think it would be one after the other but which one would come first under the same conditions? This could lead to something very interesting.
July 7th, 2011, 11:49 PM
I know I said I was done, but I've just got to ask why you think this statement is logical. "There are floating point errors, therefore the differences are not caused by the use of floating point numbers." That makes no sense. You are giving floating point numbers properties they don't have. I've already shown you how floating point numbers are not associative while real numbers are.
July 8th, 2011, 11:33 AM
This is a real big deal. I mean business I’m not here for fun you are always welcome.I know I said I was done, but I've just got to ask why you think this statement is logical. "There are floating point errors, therefore the differences are not caused by the use of floating point numbers." That makes no sense. You are giving floating point numbers properties they don't have. I've already shown you how floating point numbers are not associative while real numbers are.
This helped reduce the almost mystical reputation that floating-point computation had for seemingly nondeterministic behavior.
There are floating point errors and what am still saying is that the differences are not caused by the use of floating point numbers but are caused by the equation that is concern.
The statement is relevant but not actually relevant in this sense because we can also learn that floating point numbers are not associative while real numbers are. For example when you take only one part of the results you can see that when x = 7.2 we can have -2.9000000000000004 and not like -2.9 on calculator
I understand the way you felt about it. I once felt that way too before I could immediately realize that Q and S (that is where the two equations where derived from) were different quantities yet they could produce equal and opposite results when they make use of a certain algorithm together.
The floating point numbers solely are just doing there own. Errors will appear normally because we are using floating point numbers but the results from both equations are now different for when x is a decimal number.
Does floating point numbers made them different? No. Simply because the result from both equation are equal when x is a whole number.
Note: The floating point computation must have recognized that the two equations are different before there can ever be a different result from the two equations. This is obvious! I think this alone answer your question.
Interestingly since there are differences when x is a decimal number as a result, it can even be that when x is a whole number, the floating point errors (which show the difference) are just too small enough that the computer cannot recognize. Therefore, like you have said if that is the case then something must be there.
Look, do you expect a*b*c*d to give the same answer as c*d*b*a? In real numbers, of course they would. With floating point numbers, they might not. Would you say that the difference here is due to the different algorithm, or due to the use of floating point numbers?
You say the differences are caused by the equations, but you need to prove that, and you can't do that with floating point numbers. In no case is a computer simulation considered proof. It may be used as a part of a proof, but not by itself.
Why the issue is difficult is that there is no equation that has ever done this because two set of equation are involved in this case which they must always give equal and opposite results at every given number.
It appears that the two set of equation are different. So automatically they would produce different set of results when dealing with a decimal number in floating-point computation. Therefore the two set of equation are different that was the reason why the difference can only be possible not that it was because of floating point numbers.
From my observation so far they will give the same answer. If you have any practical examples show it. The last example of yours does not give expected results with matlab and even with Java and if that tend to be right like you’ve said then I would agree that it may be used as a part of a proof but not by itself.
Of course it is presently part of the proof. Though until I publish the actual math. Then we could see that Q and S are two possible certain CONSTANT whole numbers by having them depend on the same variables, so a change in one is instantly reflected in the other while the other would immediately fix its results to the opposite value for every given input x. The actions can be comparing very abstractly with quantum entanglement.
R is a certain whole number that is CONSTANT also but it always gives zero for any given x.
So the math is ready for publish I just want us to see its importance otherwise it makes no sense if I release it out.
Fine. Using IEEE 32-bit floats (what Java and C++ calls a float), and using Java:
I think anyone could agree that x and y would be the same if they were calculated with real numbers, but running this gives:Code:public class Main { public static void main(String[] args) { float x = 0.0f; float y = 0.0f; for(int i = 1; i <= 200; ++i) x += 1.0f / i; for(int i = 200; i >= 1; --i) y += 1.0f / i; System.out.println(x); System.out.println(y); } }
The difference exists solely because I'm using floating point numbers. The two algorithms are theoretically the same, but floating point numbers are just an approximation, and not always a nice one.Code:5.878032 5.8780313
BTW, as a computer scientist, someone trained to know how this stuff works, I can tell you that the second answer is more correct (less error). Can you work out why?
You’re just complicating your matter. Instructions are provided to perform arithmetic and comparison. Mere observing your stuff, one part (x) is fine while the other part (y) is making use of the left over errors processed by the first part from the floating point numbers.
Therefore, can you use just only ONE variable x or y for the two instructions?
Like the one have done; when x = 7.2
Code:
-2.9000000000000004
2.8999999999999986
The results must obey traditional floating-point numbers interpretation for example
when I tried your previous examples on MATLAB it did not work e.g.
Using 1.495:
1.495 * 1.495 * 1.495 * 1.495
Code:
4.995336750625001e+000
Add some parenthesis:
(1.495 * 1.495) * (1.495 * 1.495)
Code:
4.995336750625001e+000
Using: 1.495348781
1.495348781* 1.495348781* 1.495348781* 1.495348781
Code:
5
Add some parenthesis:
(1.495348781* 1.495348781) * (1.495348781* 1.495348781)
Code:
5
Also, when I tried it with JAVA via java complier on command prompt I got the same results.
Are these not floating point numbers?
These are the only available evidence and we are using them to prove a point. So I don’t know where you've got yours from.
Can you see now that you are the one giving them properties they don’t have.
The previous example I gave was made to work with a specific version of floating point numbers (4 decimal digits). Computers use 24 binary digits. They also take some extra steps to round off numbers that come close enough to whole numbers, or other nice numbers.
I'll try to find a simpler example, since you don't seem to be capable of understanding any of the one's I've given so far, but I don't have time right now.
MagiMaster, you are a very knowledgeable person no doubt about that and if there is someone I respected so much it must be you.
I salute your courage but from your java program (++i) and (--i) aren’t the same. They have different limit before the program is executed so they are not appropriate tools to be used.
To avoid such cases we are expected to make use of just only one variable x in this case.
OVER ALL SUMMARY;
The two set of my equations are NOT the same and floating point numbers proved it right e.g. also in respect to Q and S from numerous examples;
When x = 12.0 at index 7.
Code:
-2.0
2.0
When x = 4.6 at index 7.
Code:
-1.2000000000000002
1.1999999999999993
Since no one can come up with ANY real evidence other than this to show that the differences are not as a result of floating point numbers but as a result of the equation that is concern. Owing to this fact there can be something else there and this may be interesting.
Therefore, my verdict is that floating point numbers can be used as the proof. In case of any doubt, the actual mathematics is ready for publishing so that the whole world could see. Everything is based on revelation and it has come to pass.
I derived the two set of equations that can be tested on floating point numbers from arithmetic and geometric growth mechanism.
THE ARITMETIC AND GEOMETRIC GROWTH MECHANISM.
(ABSTRACT).
This is a model of a mathematical method that proves that in arithmetic and geometric growth mechanism all numbers can evolve from nothing. (Nothing is attributed to non-existent illusion here).
What it postulates: It postulates that at every chosen index number or input in the coexistence of plus and minus values there exists a turning point of their values designated as {0, 0, 0} e.g. from Q, R and S for example;
When x = 12.0 at index 12.
Code:
0
0
0
When x = 4.6 at index 4.6
Code:
0
0
0
VIRTUAL EQUATION OR IMAGE OF THE TWO SET OF EQUATIONS.
Given that x and y are set of all numbers where x is fixed.
The virtual equation or image of the actual equation:
z = 0.5(x - y)
-z = 0.5(y - x)
When x = y then at y there lies a turning point referencing 0 designated as {0, 0, 0}
y implies index number i.e. all numbers on the number line.
Although the two set of equations would not produce the actual results because they are OPPOSITE or can be considered THE SAME thing but they can give a simulation of the actual results.
NOTE: Q, R and S are certain constant positive different whole numbers.
PLEASE READ VERY CAREFULLY. If the followings results are the format of the actual results how would you resolve the problem that posed between A and B.?
SCALE A: ------------------------ POSITIVE INFINITY ------------- NEGATIVE INFINITY
Q with the actual algorithm AC gives (-z) ---- reality region to (z) ---- non-existent illusion region
R with the actual algorithm AC gives (0) ---- reality region to (0) ---- non-existent illusion region
S with the actual algorithm AC gives (z) ---- reality region to (–z) ---- non-existent illusion region
And conversely which is also possible.
SCALE B: ----------------------- POSITIVE INFINITY -------------- NEGATIVE INFINITY
Q with the actual algorithm CA gives (z) ---- reality region to (-z) ---- non-existent illusion region
R with the actual algorithm CA gives (0) ---- reality region to (0) ---- non-existent illusion region
S with the actual algorithm CA gives (-z) ---- reality region to (z) ---- non-existent illusion region
The problem that posed between A and B: Without turning each of the scale they pretend to turn to themselves this way:
SCALE A:
Q with the actual algorithm AC gives (-z) ------- reality region
R with the actual algorithm AC gives (0) -------- reality region
S with the actual algorithm AC gives (z) -------- reality region
And conversely which is also possible.
SCALE B:
Q with the actual algorithm CA gives (z) ------- reality region
R with the actual algorithm CA gives (0) ------- reality region
S with the actual algorithm CA gives (-z) ------- reality region
Since number is usually run from a negative infinity to a positive infinity.
Therefore numbers can evolve from nothing as non-existent illusion.
This confirms that Q, R and S are homogeneous so they coexist.
CONCLUSION:
Two possible equations can depend on the same variables, so a change in one is instantly reflected in the other and the other would immediately fix its results to the opposite value for every given input x. The actions can be comparing very abstractly with quantum entanglement.
PARADOX:
If the two states A and B are superposition I wonder the state that computer would eventually choose as its own truth leading to a choice mechanism for artificial intelligence AI. Thus AI would be absolute through this process.
Out of curiosity, I want to know if there is any other better interpretation that resolves the problem that posed between A and B because the math is right. If there is someone that has a logical solution that is preferable than this it would be welcomed.
If there is no logical solution that is preferable that mean this is the exact solution that resolve this problem. The logic demands that something can emerge from nothing.
This says: start from 1, count up to 200, adding 1/i to the total each step.Code:for(int i = 1; i <= 200; ++i) x += 1.0f / i;
This says: start from 200, count down to 1, adding 1/i to the total each step. ++i and --i aren't the same, but they're being used to do the same thing.Code:for(int i = 200; i >= 1; --i) y += 1.0f / i;
If you were using real numbers, these would give the same results. The difference is in the use of floating point numbers and how the truncation error (a specific type of floating point error) accumulates.
Edit: If that still doesn't convince you, here's an even more direct example:
It adds the numbers, then subtracts out the same numbers. Should be 0 right? Instead it gives:Code:public class Main { public static void main(String[] args) { float x = 0.0f; for(int i = 1; i <= 1000; ++i) x += 1.0f / i; for(int i = 1; i <= 1000; ++i) x -= 1.0f / i; System.out.println(x); } }
Code:0.00000643
Why is this in New Hypotheses?
It belongs really in Computer Science, as in "How Computers do math"
All the computer science is me trying to explain why floating point errors can't be taken to mean anything in Mixter's new hypothesis.
Flaws;
Flaws;
Oh do I say all that. I thought I was referring to what I can use to determine the limit of my java compiler by just trying to explain my self better.
Regardless of their states, ++i and --i are expected to give the same results which they do not. Of course this can be accepted but only not with oneness. It lacks the ability for a variable to utilize two different states simultaneously because we need to be sure if they are unified or not. I think that is the essence of the basics.
This is technical and very sensitive issue.
The problem is that the two states ++i and --i cannot run simultaneously on the same counter. Either counts up first or counts down. At first instance, this has defiled the order. So, the ability for both ++i and --i to make use of one variable instantaneously is impossible.
In fact, The Halting Problem, the Space in between the Time needed to compute the other state should be considered. We have to be sure if the two states actually coexist or we are doing something else.
This is artificial intelligent.
Can you provide just only one variable x or y for the two states?
Obviously, the answer is no.
In other to solve such problem like this we are expected to provide one variable for the two states once and ONLY once which I have done in the actual equation.
Therefore the examples you gave do not solve the problem but allow us to have a deep insight. I like it thanks.
A little bit modified.
Halting problem; the two states ++i and --i cannot run simultaneously on the same counter. Either counts up first or counts down.
(ABSTRACT)
Given the input twice, P ≠ NP: Space in between the Time needed to compute the other state is being considered. So there is no oneness.
When x = 0.0 and y = 0.0
Code:
5.878032
5.8780313
These are errors caused solely by the use of the floating point approximation.
Unlike if and only x = 0.0
For example;
Given the input once and only once, P = NP:
When x = 12.0 at index 7.
Code:
-2.0
2.0
When x = 4.6 at index 7.
Code:
-1.2000000000000002
1.1999999999999993
These are errors caused by the equations derived from the coexistence of different quantities Q, R and S. Thus, the differences aren't floating point errors.
Therefore a variable can occupy two different states simultaneously when input once and only once.
USING A VIRTUAL EQUATION OR IMAGE OF THE TWO SET OF EQUATIONS.
Given that x and y are set of all numbers where x is fixed.
The virtual equation or image of the actual equation:
z = 0.5(x - y)
-z = 0.5(y - x)
What its postulates:
Input x and y. if y = x output {0,0,0} 'halts' else loop forever e.g.
using the virtual equation or image of the actual equation:
z = 0.5(x - y)
-z = 0.5(y - x)
When x = 12.0 and y = 12 (i.e. x at index 12).
Halts;
Code:
0
0
0
Else loop forever.
When x = 4.6 and y = 4.6 (i.e. x at index 4.6).
Halts;
Code:
0
0
0
Else loop forever.
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