Improving Hit-and-Run for global optimization
نویسندگان
چکیده
منابع مشابه
Improving Hit-and-Run for global optimization
Improving Hit-and-Run is a random search algorithm for global optimization that at each iteration generates a candidate point for improvement that is uniformly distributed along a randomly chosen direction within the feasible region. The candidate point is accepted as the next iterate if it offers an improvement over the current iterate. We show that for positive definite quadratic programs, th...
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We build on improving hit-and-run’s (IHR) prior success as a Monte Carlo random search algorithm for global optimization by generalizing the algorithm’s sampling distribution. Specifically, in place of the uniform step-size distribution in IHR, we employ a family of parameterized step-size distributions to sample candidate points. The IHR step-size distribution is a special instance within this...
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We develop new Markov chain Monte Carlo samplers for neighborhood generation in global optimization algorithms based on Hit-and-Run. The success of Hit-and-Run as a sampler on continuous domains motivated Discrete Hit-and-Run with random biwalk for discrete domains. However, the potential in efficiencies in the implementation, which requires a randomization at each move to create the biwalk, le...
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We study the numerical computation of an expectation of a bounded function f with respect to a measure given by a non-normalized density on a convex body K ⊂ Rd . We assume that the density is log-concave, satisfies a variability condition and is not too narrow. In [19, 25, 26] it is required that K is the Euclidean unit ball. We consider general convex bodies or even the whole Rd and show that...
متن کاملHit-and-run mixes fast
It is shown that the “hit-and-run” algorithm for sampling from a convex body K (introduced by R.L. Smith) mixes in time O(nR/r), where R and r are the radii of the inscribed and circumscribed balls of K. Thus after appropriate preprocessing, hit-andrun produces an approximately uniformly distributed sample point in time O(n), which matches the best known bound for other sampling algorithms. We ...
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ژورنال
عنوان ژورنال: Journal of Global Optimization
سال: 1993
ISSN: 0925-5001,1573-2916
DOI: 10.1007/bf01096737