We want to choose an intermediate approach between stochastic optimization, which has no robustness to the error of distribution; and robust optimization, which ignores available problem data. where we consider a set Γ of density functions or distributions, and maximize the worst-case expected cost value among those distributions in Γ.

Abstract—The linear programming (LP) approach is, together with value iteration and policy iteration, one of the three fundamental methods to solve optimal control problems in a dynamic programming setting.

The linear programming (LP) approach is, together with value iteration and policy iteration, one of the three fundamental methods to solve optimal control problems in a dynamic programming setting.

Feb 26, 2025 · CB2203 Data-Driven Business Modeling 1 Tutorial 9 Linear Programming: Sensitivity Analysis Question 1 A furniture company makes sofa, tables, and chairs. Each piece of furniture requires a fixed amount of wood and metal. In addition, the production of each piece of furniture requires a fixed amount of labour hours from …

Oct 9, 2024 · CB2203 Data-Driven Business Modeling Tutorial 8 Linear Programming Part 2 Question 1 A bank employs 12 full-time tellers. Part-time employee (working four hours per day) are also available. Tell requirements of each hour are as follow: Time Period # of Tellers Required 9am - 10am 10 10am - 11am 12 11am - 12noon 14 12noon - 1pm 16 1pm - 2pm 18 2pm - 3pm 17 3pm - 4pm 15 4pm - 5pm 10 According to ...

Learning how to optimally regulate a dynamical system from data is a fundamental problem in control theory. This project focuses on investigating new theory and methods about the so-called linear programming approach to approximate dynamic programming.

Feb 1, 2022 · In this work, we introduce a relaxed version of the Bellman operator for q -functions and prove that it is still a monotone contraction mapping with a unique fixed point. In the spirit of the LP approach, we exploit the new operator to build a relaxed linear program (RLP).

Sep 25, 2024 · TL;DR: We present a generalization bound and learning methods for reducing the dimensionality of linear programs with projection matrices learned from data. How to solve high-dimensional linear programs (LPs) efficiently is a fundamental question.

In this article, we propose an alternative methodology for solving the sample-based optimal transportation problem, using Kernel functions in both primal and dual space to discretize the problem into a nite linear program-ming one.

Abstract: This paper presents a linear-programming based algorithm to perform data-driven stabilizing control of linear positive systems. A set of state-input-transition observations is collected up to magnitude-bounded noise.