This project is aimed at implementing nonlinear programming algorithms as the (un-)constrained minimization problems with the focus on their numerical expression using various programming languages.

Linear and nonlinear programming are powerful methods for optimizing complex problems that involve multiple variables and constraints. However, they also have some challenges and limitations that ...

In addition, two algorithms are presented for approximately solving fractional programming (FP) problems. The first algorithm is based on an objective space cut and bound method for solving convex FP ...

An improved genetic algorithm (IGA) based on a novel selection strategy to handle nonlinear programming problems is proposed. Each individual in selection process is represented as a three-dimensional ...

Nonlinear Programming algorithms cannot consistently find global minima. All the algorithms in PROC NLP find a local minimum for this problem. If you need to check whether the obtained solution is a ...

Nonlinear convex programming (NCP) has convex functions for both the objective and constraints, where a convex function is one with a single global minimum or maximum and curves downward or upward ...

Iterative algorithms of Gauss-Newton type for the solution of nonlinear least squares problems are considered. They separate the variables into two sets in such a way that in each iteration, ...

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 ...

The bilevel programming problem (BLPP) is a model of a leader-follower game in which play is sequential and cooperation is not permitted. Some basic properties of the general model are developed, and ...