We formulate a risk-averse two-stage stochastic linear programming problem in which unresolved uncertainty remains after the second stage. The objective function is formulated as a composition of ...

Dynamic stochastic matching problems arise in a variety of recent applications, ranging from ridesharing and online video games to kidney exchange. Such problems are naturally formulated as Markov ...

This paper gives an algorithm for L-shaped linear programs which arise naturally in optimal control problems with state constraints and stochastic linear programs (which can be represented in this ...

Stochastic Model Predictive Control (SMPC) for linear systems is an advanced control framework that blends systematic optimisation with probabilistic forecasting. By explicitly accounting for ...

Supports both constrained optimization. Automatically checks LICQ (Linear Independence Constraint Qualification). Includes examples for solving optimization problems, such as the Rosenbrock function ...

We consider optimal consumption and (strategic) asset allocation of an investor with uncertain lifetime. The problem is solved using a multi-stage stochastic linear programming (SLP) model to ...

Abstract: This paper investigates optimal tracking control of linear stochastic systems with multiplicative state-dependent and input-dependent noise via a novel model-free integral reinforcement ...

Stochastic linear program for investments in the European power system. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii ...

Abstract: This brief studies the discounted stochastic linear quadratic regulator (LQR) problem for systems suffering from additive noise of unknown mean. A completely model-free (MF) value iteration ...