Statistical signal processing applications usually require the estimation of some parameters
of interest given a set of observed data. These estimates are typically obtained either by …

We give a randomized algorithm using O (n7 log2 n) separation calls to approximate the
volume of a convex body with a fixed relative error. The bound is O (n6 log4 n) for centrally …

We study a class of simulated annealing algorithms for global minimization of a continuous
function defined on a subset of We consider the case where the selection Markov kernel is …

It is shown that the “hit-and-run” algorithm for sampling from a convex body K (introduced by
RL Smith) mixes in time O*(n 2 R 2/r 2), where R and r are the radii of the inscribed and …

Constrained optimization models are core tools in business, science, government, and the
military with applications including airline scheduling, control of petroleum refining …

We introduce a general class of Hit-and-Run algorithms for generating essentially arbitrary
absolutely continuous distributions on R d. They include the Hypersphere Directions …

The field of global optimization has been developing at a rapid pace. There is a journal
devoted to the topic, as well as many publications and notable books discussing various …

Hide-and-Seek is a powerful yet simple and easily implemented continuous simulated
annealing algorithm for finding the maximum of a continuous function over an arbitrary …

The developing theory of geometric random walks is outlined here. Three aspects—general
methods for estimating convergence (the “mixing” rate), isoperimetric inequalities in Rn and …

Hit-and-Run algorithms are Monte Carlo procedures for generating points that are
asymptotically distributed according to general absolutely continuous target distributions G …