Showing posts with label uncountability. Show all posts
Showing posts with label uncountability. Show all posts

Sunday 14 July 2019

Continuum Hypothesis and Uncountability

After setting the foundation on set theory by working on number sets and proving that the set R of real numbers is larger than the set N of natural numbers, Georg Cantor started working on a puzzling issue that was later called the Continuum Hypothesis. This deals with the following question “Is there a set of cardinality larger that set N and smaller that set R (the continuum)?”
Cantor believed that the answer to the question is negative but he did not managed to prove it and no one else did. So 150 years later this belief is formed as a hypothesis and it is still open, for most people.
Perhaps the most complete article on the hypothesis may be found here, it explains in details the whole issue its implications and the progress so far.
Researchers mainly use logic and axiomatic set theory on their attempts to prove the hypothesis. The research efforts focus on applications of the Zermelo-Fraenkel axioms with the axiom of choice; known as ZFC axiomatic system. The problem on this approach is that the hypothesis has been proven by Gödel and Cohen to be independent of ZFC. Since then, the scientific community has been divided in two parts. Scientists like Cohen believed that independence imply undecidability and the hypothesis has no answer. Others, like Gödel, believed that the ZFC system is not appropriate of resolving the hypothesis and some new system of axioms should be adopted.
This post is about some remarks that I have about the hypothesis and the ZFC application on it. The belief that ZFC independence results undecidability clearly implies that the ZFC system is universal on the sense that the properties of every possible set may be effectively described by the system. As ZFC is an axiomatic system and some of its axioms rely on intuition its universality should not be taken for granted.
For instance, the well-ordering theorem that is equivalent to the axiom of choice states that in every non-empty set there is one least element based on some property of the set. If we consider a set of uncomputable numbers how can we claim that any element of the set is the least element? It would be fruitful to investigate new axiomatic systems or try to provide stronger evidence about the validity of the currently used axioms.
Now let us suppose that the hypothesis is false and there is set H the cardinality of which lays between N and R. Set H is uncountable and its elements are uncomputable.
Set H is uncountable as it is larger than N. Considering the Turing machine model as a universal model of computation and as the set of all Turing machines is countable there are more elements in H than Turing machines so there are elements of H that do not correspond to a Turing machine and as a result they are uncomputable. There are uncomputable numbers in R as well and these considerations imply that a system or a theory that could be used to prove or disprove the existence of H should be able to handle uncomputable numbers and their properties.

Friday 9 October 2015

Against the idea of a computable universe

The computable universe hypothesis, states that every operation and phenomenon in the universe is computable. So if you can describe all the phenomena in state s1 of some part of the universe (or of the whole universe), then you can compute the physical operations in any state si that is a successor of s1.
An equivalent hypothesis is that every set of physical operations can be simulated by a Turing machine or an automaton. This means that there is a Turing or some other computational machine that will accept as input the data that describe these operations and will output the result of them and then it will halt. The two hypotheses are equivalent as the simulation of every operation by a Turing machine implies the computability of the transition from state s1 to any state si.
These hypotheses also support the idea that the universe is deterministic. The transition from s1 to si is computable and as a result predictable which makes it deterministic.
There are many papers and textbooks that propose theories that are based on these hypotheses, there are also many that oppose such ideas. My opinion is against the idea of a computable universe, the reasons for this are: randomness, incompleteness and uncomputability.

Randomness


The existence of randomness in physical processes remains a debate among scientists. Einstein rejected it, as he believed that universe is deterministic and stating that "God does not play dishes"; a phrase that marked the debate. The truth is that there are many indications that some physical operations must be random.
Steven Hawking provides some very convincing arguments in one of his lectures. According to Hawking the way the universe evolves seems to be not deterministic, moreover the existence of extreme objects in it like the black holes and the Uncertainty Principle makes accurate computations and predictions impossible. Although in computer science we can only use pseudo-random operations in order to approach true randomness, in other fields true randomness seems to be present. Quantum mechanics build probabilistic models in order to study quantum phenomena that are believed to evolve truly randomly.
 

Incompleteness


Any not trivial system of logic is incomplete. This is a very simplified description of the incompleteness theorem stated and proven by Kurt Godel which is one the most important theorems in science.
Any system physical or not that operates under certain laws may be efficiently described by mathematics and more specifically by some system of mathematical logic using propositions and axioms. If the system of logic is not trivial (is not very simple) then there are true statements in it that may not be proven as true; the system is incomplete. This results that there are phenomena that may not be computed by mathematics.
This lecture of Hawking approaches incompleteness theorem from a physicist perspective reaching the same conclusions.
The issue that we face is that in order to compute some mathematical object O we need to use or to build some proper mathematical structure. But no matter how this structure will be it will always be incomplete, so a part of O will not be able to be proven using our mathematics.
 

Uncomputability


The theoretical background of uncomputability in Computer Science is related to Godel’s incompleteness theorem and states that there are instances of computational problems that may not be computed by a machine or a human. Computational procedures may be simulated by Turing Machines that perform them and then halt, so we usually state that a problem P is uncomputable if there is not a Turing Machine that may compute all the instances of P and then halt. Equivalently, if you input some data in a Turing Machine you may not know if it will halt or not.
The foundation for this was set by Alan Turing before the first general purpose computer was ever built.
Its affect in programming is something that we experience every day. No matter how hard have you worked on a piece of software you can never be sure that it will work as expected.
A parallelism in physics that I have thought is included in the question 
“How can you be sure that for operation H there is a Turing Machine that will halt on input of the description of H?”
The above arguments may seem to be weak but there is a stronger one for which I talked about in a previous post.
Causation (or else causality) refers to the cause-effect relation that may exist between two events or phenomena. The causal inference problem regards the determination of causation between events.
In my previous post I present a proof on which Causal Inference is logically and computationally undecidable. Relations among phenomena and physical processes are important factors in the structure of the universe. The Causal Inference undecidability results that there are physical phenomena for which we may not decide or compute if they are related to each other or not. This makes a part of the universe uncomputable and unexplained using logical arguments.

Sunday 19 July 2015

Proving uncountability without diagonalization



In this post, I propose a proof of the uncountability of the set of real numbers without using the famous diagonalization method of Georg Cantor. My proof is based on the Church-Turing thesis which states that any computable operation may be computed or simulated by a Turing Machine. So, if a set of mathematical objects is countable a counting operation may be performed and there is a Turing Machine that may compute it.

Definition. Let S be an ordered set of discrete objects. If S is countable then there is Turing Machine M which on input of the descriptions of objects a and b it outputs c which is a representation of the number of elements in S between objects a and b.

If we may count the elements between a and b (or else the distance of a and b in S) then we may count the whole set, as a and b may be any objects. This definition avoids a direct connection of the set N of natural numbers to S, so it differs from diagonalization, yet as I show below it reaches the same conclusions on natural, rational and real numbers. Output c has to be a number and not the symbol of infinity or anything similar, otherwise we cannot consider it as a counting result. First let’s test this definition on natural and rational numbers.

Theorem 1. Set N of natural numbers and set Q or rational numbers are countable.

Proof for N. Set N may be defined as an ordered set of linear structure. For every i we place in the i-th place of N number i. Let a and b be the representations of two natural numbers where a represents a smaller value than b and M a TM that computes their distance. In order to make the presentation more simple and intuitive and without loss of generality, let M be a 2-tape machine and a, b and c be represented in unary numeral system. Then we print a in tape-1 and b in tape-2. For any “1” printed in tape-1 M erases one “1” that is printed in tape-2. Finally a representation of c is left in tape-2 and this is the output.

For instance, on a = 8 and b = 13 we print
tape-1 : #11111111######
tape-2 : #1111111111111#
After running the configuration of M we get
tape-1 : #11111111######
tape-2 : #11111#########

Proof for Q. Let’s first define Q as an ordered set. It is known that Q may be defined as the Cartesian product of sets X and Y, where X and Y are equal to N / {0}. For every ordered pair (x, y) of the product there is corresponding number q which is in Q and stands that q = y / x. Using this definition we may create an ordered set of linear structure equivalent to Q.
Set Q may be structured as an ordered set of subsets. Initially we place numbers “0” and “1 /1”, then for every i > 2 we place in the i-th place of the set the subset of Q that for all its elements stands x+y=i. By placing in ascending order the elements within each subset we create an ordered set of linear structure with all the elements of Q. On this procedure we end up with a representation of Q that is commonly used in literature.
On input of the descriptions of a and b, TM M using the above procedure creates the ordered subset of Q that contains a and b and then computes their distance by simply counting the elements between them one by one.
The ordered set of linear structure that is subset of Q and contains a and b is computable as it may be constructed algorithmically as shown above and is finite as I will show next.
Let a=y1/x1 and b=y2/x2, then between a and b there is a finite number of subsets d= (x2+y2)-(x1+y1). For each subset stands that i=x+y and is finite as there is a finite number of combinations that may generate i given that i, x, y belong in N.

Cartesian product

1/1   1/2   1/3   1/4 ……
2/1   2/2   2/3   2/4 ……
3/1   3/2   3/3   3/4 ……
4/1   4/2   4/3   4/4 ……
………………………..

Order of elements

 1       2       4       7  ……
 3       5       8      11 ……
 6       9      12     14 ……
10     13     15     16 ……
………………………...


Q as set of subsets

(0), (1/1), (1/2, 2/1), (1/3, 2/2, 3/1), (1/4, 2/3, 3/2, 4/1), (… 2/4, 3/3, 4/2 …), (… 3/4, 4/3 …), (… 4/4 …)


Q as an ordered set of linear structure

0, 1/1, 1/2, 2/1, 1/3, 2/2, 3/1, 1/4, 2/3, 3/2, 4/1, …, 2/4, 3/3, 4/2, …, 3/4, 4/3, …, 4/4 …


Theorem 2. The set R of real numbers is uncountable.

Proof. It is known that there is no algorithmic procedure which may produce the numbers in R. That is because most of numbers in R are uncomputable. 
Let's consider a description d for the numbers in R so that they may be represented in TM M. If there is procedure implemented in M that structures R as an ordered set then M computes uncomputable numbers which is a contradiction. As a result R may not be structured as an ordered set and is not countable by M.