The SimplexMethod package provides an efficient implementation of the Simplex Algorithm for solving linear programming (LP) problems. It supports both maximization and minimization of objective ...

Scenario: Solve a linear program with an objective function parameterized by a constant. Tasks: Use the Simplex Method to solve the initial problem for a fixed value of the parameter. Analyze how ...

This study proposes a novel technique for solving linear programming problems in a fully fuzzy environment. A modified version of the well-known dual simplex method is used for solving fuzzy linear ...

A computational procedure is given for finding the minimum of a quadratic function of variables subject to linear inequality constraints. The procedure is analogous to the Simplex Method for linear ...

As a classic tool for linear programming, the core value of the dual simplex method lies in its flexibility in handling changes in constraints. However, in large-scale, dynamic, and multi-objective ...

The problem of PAPR minimization is well-studied in the literature. However, most of the algorithms have high computational costs, making them impractical for real-time applications. To that end, we ...

The simplex method can handle any type of linear programming problem, as long as it is formulated in standard form, which means that all variables are non-negative and all constraints are equalities.

We prove that the classic policy-iteration method [Howard, R. A. 1960. Dynamic Programming and Markov Processes. MIT, Cambridge] and the original simplex method with the most-negative-reduced-cost ...

The simplex algorithm first presented by George B. Dantzig, is a widely used method for solving a linear programming problem (LP). One of the important steps of the simplex algorithm is applying an ...