Transformation methods in nonlinear programming

The Squared Slacks Transformation in Nonlinear Programming

We recall the use of squared slacks used to transform inequality constraints into equalities and several reasons why their introduction may be harmful in many algorithmic frameworks routinely used in nonlinear programming. Numerical examples performed with the sequential quadratic programming method illustrate those reasons.

Partial Linearization Methods in Nonlinear Programming 1

In this paper, we characterize a class of feasible direction methods in nonlinear programming through the concept of partial linearization of the objective function. Based on a feasible point, the objective is replaced by an arbitrary convex and continuously diierentiable function, and the error is taken into account by a rst order approximation of it. A new feasible point is deened through a l...

Steering exact penalty methods for nonlinear programming

This paper reviews, extends and analyzes a new class of penalty methods for nonlinear optimization. These methods adjust the penalty parameter dynamically; by controlling the degree of linear feasibility achieved at every iteration, they promote balanced progress toward optimality and feasibility. In contrast with classical approaches, the choice of the penalty parameter ceases to be a heuristi...

Hydrogen Distribution in Refinery with Nonlinear Programming

Deterministic Methods for Mixed Integer Nonlinear Programming