This paper presents a tutorial on the state-of-the-art software for the solution of two-stage
(mixed-integer) linear stochastic programs and provides a list of software designed for this …

We study convergence rates of the classic proximal bundle method for a variety of
nonsmooth convex optimization problems. We show that, without any modification, this …

Linear energy system models are a crucial component of energy system design and
operations, as well as energy policy consulting. If detailed enough, such models lead to …

We present a general graph-based modeling abstraction for optimization that we call an
OptiGraph. Under this abstraction, any optimization problem is treated as a hierarchical …

We present a scalable branching method for the dual decomposition of stochastic mixed-
integer programming. Our new branching method is based on the branching method …

With increased reliance on cyber infrastructure, large scale power networks face new
challenges owing to computational scalability. In this paper, we focus on develo** an …

We investigate new methods for generating Lagrangian cuts to solve two-stage stochastic
integer programs. Lagrangian cuts can be added to a Benders reformulation and are …

We present the implementation of a trust-region Newton algorithm ExaTron for bound-
constrained nonlinear programming problems, fully running on multiple GPUs. Without data …

We propose a revision of the classical column generation algorithm for solving Dantzig–
Wolfe decompositions of mixed integer programs. It is meant to fully exploit the availability of …

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 …