Constructivizing Membership Proofs in Complexity Classes

1997 ◽  
Vol 08 (04) ◽  
pp. 433-442 ◽  
Author(s):  
V. Arvind

A computational problem is said to have the Ptime self-witnessing property if we can design a Turing machine code M such that if the problem is polynomial-time computable, then M actually encodes a polynomial-time algorithm for it. This notion captures constructivizing proofs of membership in P. In this paper we define and study analogous notions of self-witnessing corresponding to other complexity classes like DLOG, PSPACE, and NC. In particular, we show that logspace self-reducible sets are DLOG self-witnessing and wdq-self-reducible sets are PSPACE self-witnessing. As a consequence of this we derive that for any complexity class [Formula: see text], if [Formula: see text] then [Formula: see text] is constructively equal to DLOG. Likewise, we show that is PSPACE = EXP then PSPACE is constructively equal to EXP. We also show connections between the self-witnessing property and self-helping and program checking

Author(s):  
Frank Vega

P versus NP is considered as one of the most important open problems in computer science. This consists in knowing the answer of the following question: Is P equal to NP? The precise statement of the P versus NP problem was introduced independently by Stephen Cook and Leonid Levin. Since that date, all efforts to find a proof for this problem have failed. Another major complexity class is P-Sel. P-Sel is the class of decision problems for which there is a polynomial time algorithm (called a selector) with the following property: Whenever it’s given two instances, a “yes” and a “no” instance, the algorithm can always decide which is the “yes” instance. It is known that if NP is contained in P-Sel, then P = NP. We claim a possible selector for 3SAT and thus, P = NP.


10.29007/v68w ◽  
2018 ◽  
Author(s):  
Ying Zhu ◽  
Mirek Truszczynski

We study the problem of learning the importance of preferences in preference profiles in two important cases: when individual preferences are aggregated by the ranked Pareto rule, and when they are aggregated by positional scoring rules. For the ranked Pareto rule, we provide a polynomial-time algorithm that finds a ranking of preferences such that the ranked profile correctly decides all the examples, whenever such a ranking exists. We also show that the problem to learn a ranking maximizing the number of correctly decided examples (also under the ranked Pareto rule) is NP-hard. We obtain similar results for the case of weighted profiles when positional scoring rules are used for aggregation.


2002 ◽  
Vol 50 (8) ◽  
pp. 1935-1941 ◽  
Author(s):  
Dongning Li ◽  
Yong Ching Lim ◽  
Yong Lian ◽  
Jianjian Song

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