Logical depth for reversible Turing machines with an application to the rate of decrease in logical depth for general Turing machines

08/28/2019 ∙ by Paul MB Vitanyi, et al. ∙ Centrum Wiskunde & Informatica 0

The logical depth of a reversible Turing machine equals the shortest running time of a shortest program for it. This is applied to show that the result in L.F. Antunes, A. Souto, and P.M.B. Vitányi, On the Rate of Decrease in Logical Depth, Theor. Comput. Sci., 702(2017), 60–64 is valid notwithstanding the error noted in Corrigendum P.M.B. Vitányi, Corrigendum to "On the rate of decrease in logical depth" by L.F. Antunes, A. Souto, and P.M.B. Vitányi [Theoret. Comput. Sci. 702 (2017) 60–64], Theoret. Comput. Sci., https://doi.org/10.1016/j.tcs.2018.07.009 . /

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1 Introduction

A book on number theory is difficult, or ‘deep.’ The book lists a number of difficult theorems of number theory. However, it has very low Kolmogorov complexity, since all theorems are derivable from the initial few definitions. Our estimate of the difficulty, or ‘depth,’ of the book is based on the fact that it takes a long time to reproduce the book from part of the information in it. The existence of a deep book is itself evidence of some long evolution preceding it.

The logical depth of a (finite) string is related to complexity with bounded resources and measures the tradeoff between program sizes and running times. Computing a string  from one of its shortest programs may take a very long time, but computing the same string from a simple “rint \verb$(x)$’’rogram of length about bits takes very little time.

Logical depth as defined in [4]

for a string comes in two versions: one based on the compressibility of programs of prefix Turing machines and the other using the ratio between algorithmic probabilities with and without time limits. Since both are approximately the same (

[5, Theorem 7.7.1] based on [4, Lemma 3]) it is no restriction to use the compressibility version.

The used notions of computability, resource-bounded computation time, self-delimiting strings, big-O notation, and Kolmogorov complexity are well-known and the properties, notations, are treated in [5].

2 Preliminaries

All Turing machines in this paper are prefix Turing machines. A prefix Turing machine is a Turing machine with a one-way read-only program tape, an auxiliary tape, one or more work tapes and an output tape. All tapes are linear one-way infinite and divided into cells capable of containing one symbol out of a finite set. Initially the program tape is inscribed with an infinite sequence of 0’s and 1’s and the head is scanning the leftmost cell. When the computation terminates the sequence of bits scanned on the input tape is the program. For every fixed finite contents of the auxiliary tape the set of programs for such a machine is a prefix code (no program is a proper prefix of another program). Let be the standard enumeration of prefix Turing machines. A universal prefix Turing machine simulates every prefix Turing machine given its index number. We also require it to be optimal which means that the simulation program is as short as possible. We choose a reference optimal universal prefix Turing machine and call it .

The prefix Kolmogorov complexity is based on the prefix Turing machine similar to the (plain) Kolmogorov complexity based on the (plain) Turing machine. Let be finite binary strings. The prefix Kolmogorov complexity of with auxiliary is defined by

If is a binary string of length then . Restricting the computation time resource is indicated by a superscript giving the allowed number of steps, usually denoted by . The notation means that within steps. If the auxiliary string is the empty string , then we usually drop it. Similarly, we write for . The string is a shortest program for if and . A string is -incompressible if .

3 Reversible Turing Machines

A Turing machine behaves according to a finite list of rules. These rules determine, from the current state of the finite control and the symbol contained in the cell under scan, the operation to be performed next and the state to enter at the end of the next operation execution.

The device is (forward) deterministic. Not every possible combination of the first two elements has to be in the set; in this way we permit the device to perform no operation. In this case we say that the device halts. Hence, we can define a Turing machine by a transition function.

Definition 1.

A reversible Turing machine [3, 2] is a Turing machine that is forward deterministic (any Turing mchine as defined is) but also backward deterministic, that is, the transition function has a single-valued inverse. The details of the formal definition are intricate [3, 2] and need not concern us here. This definition extends in the obvious manner to multitape Turing machines.

In [3] for every 1-tape ordinary Turing machine a 3-tape reversible Turing machine is constructed that emulates in linear time such that with input the output is . The reversible Turing machine that emulates is called .

Definition 2.

Let be a string and a nonnegative integer. The logical depth of at significance level , is

the least number of steps to compute by a -incompressible program.

Theorem 1.

The logical depth of a string at significance level for reversible Turing machines is equal to

the least number of steps to compute by from a program of length at most .

Proof.

Since a reversible Turing machine is backwards deterministic, and an incompressible program cannot be computed from a shorter program, the length of an incompressible program for can only be the length of a shortest program for . The logical depth at significance is then the least number of steps to compute by from a program of length . ∎

4 The Rate of Decrease of Logical Depth

In [1, Section 4 ] it is assumed that, for all , the string is the only incompressible string such that . That is, logical depth according to Theorem 1 is used. However, this assumption is wrong for general Turing machines in that for many there may be an incompressible string with such that . The computation of may be faster than that of . For example, the function from to the least number of steps in a computation for an incompressible string may be computable. The argument in the paper is, however, correct for the set of reversible Turing machines. These Turing machines are a subset of the set of all Turing machines [3, 2] and emulate them in linear time. This implies the correctness of [1, Theorem 2] as we shall show.

Lemma 1.

Let be defined by

Then is not computable and grows faster than any computable function.

Proof.

If a function as in the lemma were computable, then for an of length  we could run emulating [3] forward for steps on all programs of length . Among those programs that halt within steps, we could select the programs which output . Subsequently, we could select from that set a program of minimum length, say . Such a program has length since is emulating . This would imply that would be computable. But the function is incomputable [6, 5]: contradiction. Therefore cannot be computable. Since this holds for every function majoring , the function must grow faster than any computable function. ∎

Corollary 1.

The set of reversible Turing machines is a subset of the set of all Turing machines. The emulation of by is linear time for all binary inputs by [3]. Therefore, replacing in the lemma by changes to . Hence the lemma holds with replaced by and by . This gives us [1, Lemma 1] and therefore [1, Theorem 2] (the Busy Beaver upper bound is proved as it is in [1]):

Theorem 2.

The function

grows faster than any computable function but not as fast as the Busy Beaver function.

References

  • [1] L.F. Antunes, A. Souto, and P.M.B. Vitányi, On the Rate of Decrease in Logical Depth, Theor. Comput. Sci., 702(2017), 60–64.
  • [2] H.B. Axelsen and R. Glück, A simple and efficient universal reversible Turing machine, Proc. 5th Int. Conf. Language and Automata Theory and Applications, Lecture notes in computer science, Vol. 6638, Springer, 2011, 117–128.
  • [3] C.H. Bennett, Logical reversibility of computation, IBM J. Research and Development, 17:6(1973), 525–532.
  • [4] C.H. Bennett, Logical depth and physical complexity, pages 227–257 in The Universal Turing Machine A Half-Century Survey, R. Herken Ed., Oxford University Press, 1988.
  • [5] M. Li and P.M.B. Vitányi, An Introduction to Kolmogorov Complexity and Its Applications, Springer, New York, 2008.
  • [6] A.N. Kolmogorov, Three approaches to the quantitative definition of information. Problems Inform. Transmission, 1:1(1965), 1–7.
  • [7] P.M.B. Vitányi, Corrigendum to “On the rate of decrease in logical depth” by L.F. Antunes, A. Souto, and P.M.B. Vitányi [Theoret. Comput. Sci. 702 (2017) 60–64], Theoret. Comput. Sci., https://doi.org/10.1016/j.tcs.2018.07.009 (In Press, Corrected Proof).