Logic at Botik'89 Symposium on logical foundations of computer science
On search decision and the efficiency of polynomial-time algorithms
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
The history and status of the P versus NP question
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
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On a class of O(n2) problems in computational geometry
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Separating Nondeterministic Time Complexity Classes
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Holographic Proofs and Derandomization
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Time-space lower bounds for satisfiability
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Parameterized Complexity Theory (Texts in Theoretical Computer Science. An EATCS Series)
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SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
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Computational Complexity: A Modern Approach
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The Complexity of Satisfiability of Small Depth Circuits
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Combinatorial PCPs with Efficient Verifiers
FOCS '09 Proceedings of the 2009 50th Annual IEEE Symposium on Foundations of Computer Science
The time complexity of constraint satisfaction
IWPEC'08 Proceedings of the 3rd international conference on Parameterized and exact computation
Improved simulation of nondeterministic turing machines
MFCS'10 Proceedings of the 35th international conference on Mathematical foundations of computer science
A full derandomization of schöning's k-SAT algorithm
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TAMC'11 Proceedings of the 8th annual conference on Theory and applications of models of computation
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The P vs NP problem arose from the question of whether exhaustive search is necessary for problems with short verifiable solutions. We still do not know if even a slight algorithmic improvement over exhaustive search is universally possible for all NP problems, and to date no major consequences have been derived from the assumption that an improvement exists. We show that there are natural NP and BPP problems for which minor algorithmic improvements over the trivial deterministic simulation already entail lower bounds such as NEXP is not in P/poly and LOGSPACE is not equal to NP. These results are especially interesting given that similar improvements have been found for many other hard problems. Optimistically, one might hope our results suggest a new path to lower bounds; pessimistically, they show that carrying out the seemingly modest program of finding slightly better algorithms for all search problems may be extremely difficult (if not impossible). We also prove unconditional superpolynomial time-space lower bounds for improving on exhaustive search.