Lagrangian Dual Decision Rules for Multistage Stochastic Mixed Integer Programming

Multistage stochastic programs can be approximated by restricting policies to follow decision rules. Directly applying this idea to problems with integer decisions is difficult because of the need for decision rules that lead to integral decisions. In this work, we introduce Lagrangian dual decision rules (LDDRs) for multistage stochastic mixed integer programming (MSMIP) which overcome … Read more

Quasi-Monte Carlo methods for two-stage stochastic mixed-integer programs

We consider randomized QMC methods for approximating the expected recourse in two-stage stochastic optimization problems containing mixed-integer decisions in the second stage. It is known that the second-stage optimal value function is piecewise linear-quadratic with possible kinks and discontinuities at the boundaries of certain convex polyhedral sets. This structure is exploited to provide conditions implying … Read more

Stochastic Dual Dynamic Programming for Multistage Stochastic Mixed-Integer Nonlinear Optimization

In this paper, we study multistage stochastic mixed-integer nonlinear programs (MS-MINLP). This general class of problems encompasses, as important special cases, multistage stochastic convex optimization with \emph{non-Lipschitz-continuous} value functions and multistage stochastic mixed-integer linear optimization. We develop stochastic dual dynamic programming (SDDP) type algorithms with nested decomposition, deterministic sampling, and stochastic sampling. The key ingredient … Read more

Joint Pricing and Production: A Fusion of Machine Learning and Robust Optimization

We integrate machine learning with distributionally robust optimization to address a two-period problem for the joint pricing and production of multiple items. First, we generalize the additive demand model to capture both cross-product and cross-period effects as well as the demand dependence across periods. Next, we apply K-means clustering to the demand residual mapping based … Read more

On sample average approximation for two-stage stochastic programs without relatively complete recourse

We investigate sample average approximation (SAA) for two-stage stochastic programs without relatively complete recourse, i.e., for problems in which there are first-stage feasible solutions that are not guaranteed to have a feasible recourse action. As a feasibility measure of the SAA solution, we consider the “recourse likelihood”, which is the probability that the solution has … Read more

An integrated planning model in centralized power systems

In the context of centralized electricity markets, we propose an integrated planning model for power pricing and network expansion, which endogenizes the scaling costs from power losses. While the substitutability pattern between pricing and expansion has been overlooked in the power flow optimization literature, this becomes particularly relevant in centralized electricity markets (where the headquarters … Read more

Nearly optimal first-order methods for convex optimization under gradient norm measure: An adaptive regularization approach

In the development of first-order methods for smooth (resp., composite) convex optimization problems minimizing smooth functions, the gradient (resp., gradient mapping) norm is a fundamental optimality measure for which a regularization technique of first-order methods is known to be nearly optimal. In this paper, we report an adaptive regularization approach attaining this iteration complexity without … Read more

Stochastic Dynamic Cutting Plane for multistage stochastic convex programs

We introduce StoDCuP (Stochastic Dynamic Cutting Plane), an extension of the Stochastic Dual Dynamic Programming (SDDP) algorithm to solve multistage stochastic convex optimization problems. At each iteration, the algorithm builds lower affine functions not only for the cost-to-go functions, as SDDP does, but also for some or all nonlinear cost and constraint functions. We show … Read more

Distributionally Robust Stochastic Dual Dynamic Programming

We consider a multi-stage stochastic linear program that lends itself to solution by stochastic dual dynamic programming (SDDP). In this context, we consider a distributionally robust variant of the model with a finite number of realizations at each stage. Distributional robustness is with respect to the probability mass function governing these realizations. We describe a … Read more