least-squares? Does it have linear or nonlinear constraints? Next, it is important to consider the type of derivatives of the objective function and the constraints that are needed and whether these ...

The NLPQUA subroutine uses a fast algorithm for maximizing or minimizing the quadratic objective function Assuming nonnegativity constraints , the quadratic optimization problem solved with the LCP ...

In this work, a new method is presented for determining the binding constraints of a general linear maximization problem. The new method uses only objective function values at points which are ...

Abstract: When solving constrained optimization problems by evolutionary algorithms, an important issue is how to balance constraints and objective function. This paper presents a new method to ...

In the most general case, both the objective function and the constraints are represented as Tensors, giving users the maximum amount of flexibility in specifying their optimization problems.

The purpose of this paper is to present a Monte Carlo solution for a random objective function coefficient linear programming problem that can be executed in Excel. A solution was given in Ridley and ...

Abstract: In this paper minimizing a linear objective function subject to a continuous max-i-norm fuzzy relational equation is considered. Our contributions are two folds. First, We show that this ...

0 branch of your objective and constraint functions. To stay simple and lightweight, NLopt does not provide ways to automatically compute derivatives. If you do not have analytic expressions for the ...