Learn what dual variables are, how they are computed, and what they tell you about the optimal solution and the sensitivity of a linear programming problem.

Learn the main differences between linear and nonlinear programming, examples of problems that require each method, and tips to identify the best method.

This letter proposes a multi-agent distributed solution for linear programming (LP) problems with time-invariant box constraints on the decision variables and possibly time-varying inequality ...

This example shows how to use PROC LP to solve a linear goal-programming problem. PROC LP has the ability to solve a series of linear programs, each with a new objective function. These objective ...

Recently, a matrix-type neural dynamical method for matrix-variable nonlinear optimization with box constraints was presented. This paper proposes two matrix-type neural dynamical optimization methods ...

This model is an example of a constraint optimization problem. Considering a constraint of an integer programming model where all the decision variables in the constraint are binary, the goal is to ...

Variables, constraints, and linear expressions are available as Java objects to make model building as easy as possible. Decision variables as well as their bounds and objective coefficients can be ...

We present integer linear models with a polynomial number of variables and constraints for combinatorial optimization problems in graphs: optimum elementary cycles, optimum elementary paths and ...

Discover a simple solution-assisted methodology in Linear Programming (LP) applications to detect active constraints with the most impact on non binding constraints. Save time and effort in large ...

This paper shows a method for solving linear programming problems that includes Interval Type-2 fuzzy constraints. The proposed method finds an optimal solution in these conditions using convex ...