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In convex analysis, a non-negative function is logarithmically concave (or log-concave for short) if its domain is a convex set, and if it satisfies the inequality : for all and . If is strictly positive, this is equivalent to saying that the logarithm of the function, , is concave; that is, : for all and . Examples of log-concave functions are the 0-1 indicator functions of convex sets (which requires the more flexible definition), and the Gaussian function. Similarly, a function is log-convex if it satisfies the reverse inequality : for all and . ==Properties== * A positive log-concave function is also quasi-concave. * Every concave function that is nonnegative on its domain is log-concave. However, the reverse does not necessarily hold. An example is the Gaussian function = which is log-concave since = is a concave function of . But is not concave since the second derivative is positive for || > 1: :: * A twice differentiable, nonnegative function with a convex domain is log-concave if and only if for all satisfying , ::,〔Stephen Boyd and Lieven Vandenberghe, (Convex Optimization ) (PDF) p.105〕 :i.e. :: is :negative semi-definite. For functions of one variable, this condition simplifies to :: 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「Logarithmically concave function」の詳細全文を読む スポンサード リンク
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