Linear programming (LP) is a powerful technique for optimizing a linear objective function subject to a set of linear constraints. However, not every LP problem has a feasible or optimal solution.

Maleki et al. [19] introduced a linear programming problem with fuzzy variables and proposed a method for solving it. Fang and Hu [20] consider linear programming with fuzzy constraint coefficients ...

Convexity Assumption: Linear programming problems are convex optimization problems, meaning that the feasible region is a convex set, and the objective function is convex.

When the function is strictly convex for all points in the convex region then the quadratic problem has a unique local minimum which is also the global minimum [11] . 2.2. Karush-Kuhn-Tucker ...

Convex piecewise linear programming is a small and useful extension to linear programming. Two Python modules will allow easy formulation of a PLP problem with variables and equations. The CBC solver ...

Iterative method for finding roots in convex functions. Gradient Descent: Implemented Gradient Descent algorithms, exploring the impact of learning rates on optimization. Linear Programming with ...

At present, the most commonly used method for multiobjective linear programming (MOLP) is goal programming (GP) based methods but these methods do not always generate efficient solutions. Recently, an ...

Linear programming (LP) is a mathematical optimization technique used to achieve the best outcome, such as maximum profit or minimum cost, ...

Abstract: Mathematical programs with complementarity constraints are notoriously difficult to solve due to their nonconvexity and lack of constraint qualifications in every feasible point. This letter ...

An efficient algorithm is proposed for the solution of multiparametric convex nonlinear problems (NLPs). Based on an outer-approximation algorithm, the proposed iterative procedure involves the ...