Reduction Methods in Nonlinear Programming

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

Nonlinear Programming Methods for Distributed Optimization

In this paper we investigate how standard nonlinear programming algorithms can be used to solve constrained optimization problems in a distributed manner. The optimization setup consists of a set of agents interacting through a communication graph that have as common goal the minimization of a function expressed as a sum of (possibly non-convex) differentiable functions. Each function in the su...