Hit - and - Run Methods Zelda
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منابع مشابه
Pattern discrete and mixed Hit-and-Run for global optimization
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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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...
متن کاملAdaptive parameterized improving hit-and-run for global optimization
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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The hit-and-run algorithm is one of the fastest known methods to generate a random point in a high dimensional convex set. In this paper we study a natural extension of the hit-and-run algorithm to sampling from a logconcave distribution in n dimensions. After appropriate preprocessing, hit-and-run produces a point from approximately the right distribution in amortized time O * (n 3).
متن کاملHit - and - Run is Fast and Fun ∗
The hit-and-run algorithm is one of the fastest known methods to generate a random point in a high dimensional convex set. In this paper we study a natural extension of the hit-and-run algorithm to sampling from a logconcave distribution in n dimensions. After appropriate preprocessing, hit-and-run produces a point from approximately the right distribution in amortized time O∗(n3).
متن کاملDiscrete Hit-and-Run for Sampling Points from Arbitrary Distributions Over Subsets of Integer Hyperrectangles
We consider the problem of sampling a point from an arbitrary distribution π over an arbitrary subset S of an integer hyper-rectangle. Neither the distribution π nor the support set S are assumed to be available as explicit mathematical equations but may only be defined through oracles and in particular computer programs. This problem commonly occurs in black-box discrete optimization as well a...
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تاریخ انتشار 2015