The density and complexity of polynomial cores for intractable sets

Let A be a recursive problem not in P. Lynch has shown that A then has an infinite recursive polynomial complexity core. This is a collection C of instances of A such that every algorithm deciding A needs more than polynomial time almost everywhere on C. We investigate the complexity of recognizing the instances in such a core, and show that every recursive problem A not in P has an infinite core recognizable in subexponential time. We further study how dense the core sets for A can be, under various assumptions about the structure of A. Our main results in this direction are that if P ≠ NP, then NP-complete problems have polynomially nonsparse cores recognizable in subexponential time, and that EXPTIME-complete problems have cores of exponential density recognizable in exponential time

Kolmagorav Complexity, Complexity Cores, and the Distribution of Hardness

Problems that are complete for exponential space are provably intractable and known to be exceedingly complex in several technical respects. However, every problem decidable in exponential space is efficiently reducible to every complete problem, so each complete problem must have a highly organized structure. The authors have recently exploited this fact to prove that complete problems are, in two respects, unusually simple for problems in expontential space. Specifically, every complete problem must have unusually small complexity cores and unusually low space-bounded Kolmogorov complexity. It follows that the complete problems form a negligibly small subclass of the problems decidable in exponential space. This paper explains the main ideas of this work

The Quantitative Structure of Exponential Time

Recent results on the internal, measure-theoretic structure of the exponential time complexity classes E = DTIME(2^linear) and E2 = DTIME(2^polynomial) are surveyed. The measure structure of these classes is seen to interact in informative ways with bi-immunity, complexity cores, polynomial-time many-one reducibility, circuit-size complexity, Kolmogorov complexity, and the density of hard languages. Possible implications for the structure of NP are also discussed

Instance complexities of hard and weakly hard problems

This thesis investigates the instance complexities of problems that are hard or weakly hard for exponential time under polynomial time, many-one reductions. It is shown that almost every instance of almost every problem in exponential time has essentially maximal instance complexity. It follows that every weakly hard problem has a dense set of such maximally hard instances. This extends the theorem, due to Orponen, Ko, Schöning and Watanabe (1994), that every hard problem for exponential time has a dense set of maximally hard instances. Complementing this, it is shown that every hard problem for exponential time also has a dense set of unusually easy instances

On the structure of intractable sets

There are two parts to this dissertation. The first part is motivated by nothing less than a reexamination of what it means for a set to be NP-complete. Are there sets in NP that in a mathematically meaningful sense should be considered to be complete for NP, but that are not NP-complete in the usual sense that every set in NP is ≤q[subscript]spmP-reducible to it? We define a noneffective binary relation that makes precise the notion that the complexity of A is polynomially related to the complexity of B, This relation yields new completeness and hardness notions for complexity classes, and we show that there are sets that are hard for NP that are not NP-hard in the usual sense. We also show that there are sets that must be considered to be complete for E that are not even ≤q[subscript]spTP-complete for E;In a certain way, hardness and completeness with respect to the relation we define is related to the notion of almost everywhere (a.e.) complexity, and so we initiate this study by first investigating this notion. We state and prove a deterministic time hierarchy theorem for a.e. complexity that is as tight as the Hartmanis-Stearns hierarchy theorem for infinitely often complexity. This result is a significant improvement over all previously known hierarchy theorems for a.e. complex sets. We derive similar, very tight, hierarchy theorems for sets that cannot be a.e. complex for syntactic reasons, but for which, intuitively, a.e. complex notions should exit. Similar results are applied to the study of P-printable sets and sets of low generalized Kolmogorov complexity;The second part of this study deals with relativization. Does the fact that DTIME(O (n)) ≠ NTIME(n) help in leading us to a proof that P ≠ NP? Does one imply the other? We seek evidence that this is a hard . We construct an oracle that answers this question in the affirmative, and we construct an oracle that answers this question in the negative. We conclude that the result that DTIME(O (n)) ≠ NTIME(n) does not imply P ≠ NP by recursive theoretic techniques;Finally, we study the relationships between P, NP, and the unambiguous and random time classes UP, and RP. Questions concerning these relationships are motivated by complexity issues to public-key cryptosystems. We prove that there exists a recursive oracle A such that P[superscript]A ≠ UP[superscript]A≠ NP[superscript]A, and such that the first inequality is strong, i.e., there exists a P[superscript]A-immune set in UP[superscript]A. Further, we constructed a recursive oracle B such that UP[superscript]B contains an RP[superscript]B-immune set. As a corollary we obtain P[superscript]B ≠ RB[superscript]B≠ NP[superscript]B and both inequalities are strong. By use of the techniques employed in the proof that P[superscript]A≠ UP[superscript]A≠ NP[superscript]A, we are also able to solve an open problem raised by Book, Long and Selman

The complexity and distribution of computationally useful problems

The solutions of certain natural decision problems such as the halting problem and the boolean satisfiability problem contain large amounts of useful information about computation that is highly organized and readily available to efficient computational processes. Such problems are computationally useful. This dissertation investigates the complexity and distribution of these computationally useful problems. The main results of this dissertation are of the following three general types. (1) Useful problems contain highly organized information. (2) Very useful problems are so highly organized that they are unusually simple and hence rare. (3) Useful problems are, as a whole, not rare and thus are not necessarily simple;A result of type (1) is proven in Chapter 3. Bennett recently extended algorithmic information theory to include a notion of computational depth that appears to quantify the level of organization in binary strings and sequences. The main result of Chapter 3 states that every weakly useful sequence is strongly deep. (A sequence x is weakly useful if a non-negligible set of recursive problems are decidable within a fixed recursive time bound when given access to x.);Results of type (2) are presented in Chapters 4 and 5. These results say that the ≤[subscript]sp m P-complete problems for E = DTIME(2[superscript] linear) and the ≤[subscript]sp m p/poly-complete problems for ESPACE = DSPACE(2[superscript] linear) are unusually simple and hence rare. Complete problems are very useful because every problem in E or ESPACE is efficiently decidable when given access to one of these problems;Chapter 6 develops a result of type (3). This result says that the weakly ≤[subscript]sp m P-complete problems for E and ESPACE are not rare and hence are not necessarily simple. Weakly complete problems are useful because every problem in a non-negligible subset of E or ESPACE is efficiently decidable when given access to one of these problems;The above results (and others along the way) are obtained through a systematic investigation of the measure-theoretic structure of complexity classes