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In computer science, particularly the study of approximation algorithms, an L-reduction ("''linear reduction''") is a transformation of optimization problems which linearly preserves approximability features; it is one type of approximation-preserving reduction. L-reductions in studies of approximability of optimization problems play a similar role to that of polynomial reductions in the studies of computational complexity of decision problems. The term ''L reduction'' is sometimes used to refer to log-space reductions, by analogy with the complexity class L, but this is a different concept. ==Definition== Let A and B be optimization problems and cA and cB their respective cost functions. A pair of functions ''f'' and ''g'' is an L-reduction if all of the following conditions are met: * functions ''f'' and ''g'' are computable in polynomial time, * if ''x'' is an instance of problem A, then ''f''(''x'') is an instance of problem B, * if ''y' '' is a solution to ''f''(''x''), then ''g''(''y' '') is a solution to ''x'', * there exists a positive constant α such that :, * there exists a positive constant β such that for every solution ''y' '' to ''f''(''x'') :. 抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)』 ■ウィキペディアで「L-reduction」の詳細全文を読む スポンサード リンク
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