Advice for semifeasible sets and the complexity-theoretic cost(lessness) of algebraic properties

Informally put, the semifeasible sets are the class of sets having a polynomial-time algorithm that, given as input any two strings of which at least one belongs to the set, will choose one that does belong to the set. We provide a tutorial overview of the advice complexity of the semifeasible sets. No previous familiarity with either the semifeasible sets or advice complexity will be assumed, and when we include proofs we will try to make the material as accessible as possible via providing intuitive, informal presentations. Karp and Lipton introduced advice complexity about a quarter of a century ago.18 Advice complexity asks, for a given power of interpreter, how many bits of "help" suffice to accept a given set. Thus, this is a notion that contains aspects both of descriptional/informational complexity and of computational complexity. We will see that for some powers of interpreter the (worst-case) complexity of the semifeasible sets is known right down to the bit (and beyond), but that for the most central power of interpreter—deterministic polynomial time—the complexity is currently known only to be at least linear and at most quadratic. While overviewing the advice complexity of the semifeasible sets, we will stress also the issue of whether the functions at the core of semifeasibility—so-called selector functions—can without cost be chosen to possess such algebraic properties as commutativity and associativity. We will see that this is relevant, in ways both potential and actual, to the study of the advice complexity of the semifeasible sets.

[1]  Alan T. Sherman,et al.  An Observation on Associative One-Way Functions in Complexity Theory , 1997, Inf. Process. Lett..

[2]  C. Jockusch Semirecursive sets and positive reducibility , 1968 .

[3]  Patrick C. Fischer,et al.  Computations with a restricted number of nondeterministic steps (Extended Abstract) , 1977, STOC '77.

[4]  L. A. Hemaspaandra,et al.  Algebraic Properties for Deterministic Selectivity , 2001 .

[5]  Till Tantau,et al.  Partial information classes , 2003, SIGA.

[6]  Leonard M. Adleman,et al.  Two theorems on random polynomial time , 1978, 19th Annual Symposium on Foundations of Computer Science (sfcs 1978).

[7]  Jörg Rothe,et al.  If P != NP Then Some Strongly Noninvertible Functions Are Invertible , 2001, FCT.

[8]  Timothy J. Long,et al.  Quantitative Relativizations of Complexity Classes , 1984, SIAM J. Comput..

[9]  Lane A. Hemaspaandra,et al.  Optimal Advice , 1996, Theor. Comput. Sci..

[10]  Lane A. Hemaspaandra,et al.  Computing Solutions Uniquely Collapses the Polynomial Hierarchy , 1996, SIAM J. Comput..

[11]  Seinosuke Toda,et al.  PP is as Hard as the Polynomial-Time Hierarchy , 1991, SIAM J. Comput..

[12]  Alan L. Selman Some Observations on NP, Real Numbers and P-Selective Sets , 1981, J. Comput. Syst. Sci..

[13]  Cristian S. Calude,et al.  Journal of Universal Computer Science , 1994, J. Univers. Comput. Sci..

[14]  Lane A. Hemaspaandra,et al.  Theory of Semi-Feasible Algorithms , 2002, Monographs in Theoretical Computer Science An EATCS Series.

[15]  Mitsunori Ogihara,et al.  P-selective sets, and reducing search to decision vs. self-reducibility , 1993, [1993] Proceedings of the Eigth Annual Structure in Complexity Theory Conference.

[16]  Edith Hemaspaandra,et al.  P-Selektive Sets and Reducing Search to Decision vs Self-Reducibility , 1996, J. Comput. Syst. Sci..

[17]  Alan L. Selman,et al.  Reductions on NP and P-Selective Sets , 1982, Theor. Comput. Sci..

[18]  Ker-I Ko On Self-Reducibility and Weak P-Selectivity , 1983, J. Comput. Syst. Sci..

[19]  Lane A. Hemaspaandra,et al.  A Note on Linear-Nondeterminism, Linear-Sized, Karp-Lipton Advice for the P-Selective Sets , 1998, J. Univers. Comput. Sci..

[20]  Patrick C. Fischer,et al.  Refining Nondeterminism in Relativized Polynomial-Time Bounded Computations , 1980, SIAM J. Comput..

[21]  Juris Hartmanis,et al.  On Isomorphisms and Density of NP and Other Complete Sets , 1977, SIAM J. Comput..

[22]  Ker-I Ko The Maximum Value Problem and NP Real Numbers , 1982, J. Comput. Syst. Sci..

[23]  H. Landau On dominance relations and the structure of animal societies: III The condition for a score structure , 1953 .

[24]  Juris Hartmanis,et al.  On isomorphisms and density of NP and other complete sets , 1976, STOC '76.

[25]  Lane A. Hemaspaandra,et al.  The Complexity Theory Companion , 2002, Texts in Theoretical Computer Science An EATCS Series.

[26]  Jie Wang,et al.  Nondeterministically Selective Sets , 1995, Int. J. Found. Comput. Sci..

[27]  Alan L. Selman,et al.  Qualitative Relativizations of Complexity Classes , 1985, J. Comput. Syst. Sci..

[28]  Jörg Rothe,et al.  Creating Strong, Total, Commutative, Associative One-Way Functions from Any One-Way Function in Complexity Theory , 1999, J. Comput. Syst. Sci..

[29]  Alan T. Sherman,et al.  Associative one-way functions: a new paradigm for secret-key agreement and digital signatures , 1993 .

[30]  Lane A. Hemaspaandra,et al.  Computing Solutions Uniquely Collapses the Polynomial Hierarchy , 1994, SIAM J. Comput..

[31]  Alan L. Selman,et al.  P-Selective Sets, Tally Languages, and the Behavior of Polynomial Time Reducibilities on NP , 1979, ICALP.

[32]  Lane A. Hemaspaandra,et al.  Algebraic Properties for Selector Functions , 2004, SIAM J. Comput..

[33]  Richard J. Lipton,et al.  Some connections between nonuniform and uniform complexity classes , 1980, STOC '80.

[34]  Uwe Schöning,et al.  Complexity and Structure , 1986, Lecture Notes in Computer Science.