Circuit lower bounds from learning-theoretic approaches

被引:0
|
作者
Kawachi, Akinori [1 ]
机构
[1] Tokyo Inst Technol, Dept Math & Comp Sci, Meguro Ku, 2-12-1 Ookayama, Tokyo 1528552, Japan
关键词
Boolean circuit lower bounds; Exact learning; PAC learning; Statistical query learning; TIME; COMPLEXITY; SIZE; QUERIES; NP; ALGORITHMS; NETWORK; PROOFS;
D O I
10.1016/j.tcs.2018.04.038
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
An important open problem in computational complexity theory is to prove the size of circuits, namely, Boolean circuit lower bounds, necessary to solve explicit problems. We survey learning-theoretic approaches to proofs of Boolean circuit lower bounds in this paper. In particular, we discuss how to prove circuit lower bounds in uniform classes by assuming (or constructing) circuit-learning algorithms in several settings, such as the exact, probably approximately correct (PAC), and statistical query learning models. (C) 2018 Elsevier B.V. All rights reserved.
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页码:83 / 98
页数:14
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