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Which problems have strongly exponential complexity? (English) Zbl 1006.68052
Summary: For several NP-complete problems, there have been a progression of better but still exponential algorithms. In this paper, we address the relative likelihood of sub-exponential algorithm for these problems. We introduce a generalized reduction that we call Sub-Exponential Reduction Family (SERF) that preserves sub-exponential complexity. We show that Circuit-SAT is SERF-complete for all NP-search problems, and that for any fixed \(k\geq 3\), \(k\)-SAT, \(k\)-Colorability, \(k\)-Set Cover, Independent Set, Clique, and Vertex Cover, are SERF-complete for the class SNP of search problems expressible by second-order existential formulas whose first-order part is universal. In particular, sub-exponential complexity for any one of the above problems implies the same for all others.
We also look at the issue of proving strongly exponential lower bounds for AC\(^0\), that is, bounds of the form \(2^{\Omega(n)}\). This problem is even open for depth-3 circuits. In fact, such a bound for depth-3 circuits with even limited (at most \(n^\varepsilon\)) fan-in for bottom-level gates would imply a nonlinear size lower bound for logarithmic depth circuits. We show that with high probability even random degree 2 GF(2) polynomials require strongly exponential size for \(\Sigma^k_3\) circuits for \(k= o(\log\log n)\). We thus exhibit a much smaller space of \(2^{O(n^2)}\) functions such that almost every function in this class requires strongly exponential size \(\Sigma^k_3\) circuits. As a corollary, we derive a pseudorandom generator (requiring \(O(n^2)\) bits of advice) that maps \(n\) bits into a larger number of bits so that computing parity on the range is hard for \(\Sigma^k_3\) circuits. Our main technical lemma is an algorithm that, for any fixed \(\varepsilon> 0\), represents an arbitrary \(k\)-CNF formula as a disjunction of \(2^{\varepsilon n}\) \(k\)-CNF formulas that are sparse, that is, each disjunct has \(O(n)\) clauses.

MSC:
68Q25 Analysis of algorithms and problem complexity
68Q17 Computational difficulty of problems (lower bounds, completeness, difficulty of approximation, etc.)
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