Stability in two-stage stochastic programming
SIAM Journal on Control and Optimization
Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Stochastic decomposition: an algorithm for two-state linear programs with recourse
Mathematics of Operations Research
Parallel decomposition of multistage stochastic programming problems
Mathematical Programming: Series A and B
Stochastic network programming for financial planning problems
Management Science - Focused issue on financial modeling
Superlinearly convergent approximate Newton methods for LC1 optimization problems
Mathematical Programming: Series A and B
A globally convergent Newton method for convex SC1minimization problems
Journal of Optimization Theory and Applications
Newton's method for quadratic stochastic programs with recourse
Proceedings of the international meeting on Linear/nonlinear iterative methods and verification of solution
Convergence of the BFGS Method for LC1 Convex Constrained Optimization
SIAM Journal on Control and Optimization
Minimization of SC1 functions and the Maratos effect
Operations Research Letters
Applications of smoothing methods in numerical analysis and optimization
Focus on computational neurobiology
| Hi-index | 0.98 |
Stochastic programming is concerned with practical procedures for decision making under uncertainty, by modelling uncertainties and risks associated with decision in a form suitable for optimization. The field is developing rapidly with contributions from many disciplines such as operations research, probability and statistics, and economics. A stochastic linear program with recourse can equivalently be formulated as a convex programming problem. The problem is often large-scale as the objective function involves an expectation, either over a discrete set of scenarios or as a multi-dimensional integral. Moreover, the objective function is possibly nondifferentiable. This paper provides a brief overview of recent developments on smooth approximation techniques and Newton-type methods for solving two-stage stochastic linear programs with recourse, and parallel implementation of these methods. A simple numerical example is used to signal the potential of smoothing approaches.