A number of stochastic quasigradient methods are discussed from the point of view of implementation. The discussion revolves around the interactive package of stochastic optimization routines (STO) recently developed by the Adaptation and Optimization group at IIASA. (This package is based on the stochastic and nondifferentiable optimization package (NDO) developed at the V. Glushkov Institute of Cybernetics in Kiev.) The IIASA implementation is described and its use illustrated by application to three problems which have arisen in various IIASA projects
This paper studies the use of randomized Quasi-Monte Carlo methods (RQMC) in sample approximations o...
International audienceRandomization is an efficient tool for global optimization. We here define a m...
AbstractWe briefly discuss the following issues in quasi-Monte Carlo methods: error bounds and error...
This paper systematically surveys the basic direction of development of stochastic quasigradient met...
The paper deals with choosing stepsize and other parameters in stochastic quasi-gradient methods for...
This paper deals with a new variable metric algorithm for stochastic optimization problems. The esse...
For algorithms of the Robbins-Monro type, the best choice (from the asymptotic point of view) for th...
Uncertainties, risks, and disequilibrium are pervasive characteristics of modern socio-economic, tec...
This paper contains a detailed description of the SQG-PC program (Stochastic Quasi-Gradients for Per...
This thesis is concerned with stochastic optimization methods. The pioneering work in the field is t...
Solutions techniques for stochastic programs are reviewed. Particular emphasis is placed on those me...
This is a comprehensive and timely overview of the numerical techniques that have been developed to ...
In this paper, the author looks at some quite general optimization problems on the space of probabil...
The paper presents a risk minimization approach to estimate a flexible form that meets a priori rest...
This paper contains most of the documentation for a collection of routines designed to solve problem...
This paper studies the use of randomized Quasi-Monte Carlo methods (RQMC) in sample approximations o...
International audienceRandomization is an efficient tool for global optimization. We here define a m...
AbstractWe briefly discuss the following issues in quasi-Monte Carlo methods: error bounds and error...
This paper systematically surveys the basic direction of development of stochastic quasigradient met...
The paper deals with choosing stepsize and other parameters in stochastic quasi-gradient methods for...
This paper deals with a new variable metric algorithm for stochastic optimization problems. The esse...
For algorithms of the Robbins-Monro type, the best choice (from the asymptotic point of view) for th...
Uncertainties, risks, and disequilibrium are pervasive characteristics of modern socio-economic, tec...
This paper contains a detailed description of the SQG-PC program (Stochastic Quasi-Gradients for Per...
This thesis is concerned with stochastic optimization methods. The pioneering work in the field is t...
Solutions techniques for stochastic programs are reviewed. Particular emphasis is placed on those me...
This is a comprehensive and timely overview of the numerical techniques that have been developed to ...
In this paper, the author looks at some quite general optimization problems on the space of probabil...
The paper presents a risk minimization approach to estimate a flexible form that meets a priori rest...
This paper contains most of the documentation for a collection of routines designed to solve problem...
This paper studies the use of randomized Quasi-Monte Carlo methods (RQMC) in sample approximations o...
International audienceRandomization is an efficient tool for global optimization. We here define a m...
AbstractWe briefly discuss the following issues in quasi-Monte Carlo methods: error bounds and error...