We introduce a computer program PENNON for the solution of problems of convex Nonlinear and Semidefinite Programming (NLP-SDP). The algorithm used in PENNON is a generalized version of the Augmented Lagrangian method, originally introduced by Ben-Tal and Zibulevsky for convex NLP problems. We present generalization of this algorithm to convex NLP-SDP problems, as implemented in PENNON and detai...

Abstract We introduce a flexible, open source implementation that provides the optimal sensitivity of solutions of nonlinear programming (NLP) problems, and is adapted to a fast solver based on a barrier NLP method. The program, called sIPOPT evaluates the sensitivity of the KKT system with respect to model parameters. It is paired with the open-source IPOPT NLP solver and reusesmatrix factoriz...

Decoding and Turbo Equalization (TEQ) algorithms based on the Sum-Product Algorithm (SPA) are well established for LDPC codes. However there is increasing interest in linear and nonlinear programming (NLP)-based decoders which may offer computational and performance advantages over the SPA. We present NLP decoders and Turbo equalizers based on an Augmented Lagrangian formulation of the decoding...

Sequential (or Successive) Quadratic Programming (SQP) is a technique for the solution of Nonlinear Programming (NLP) problems. It is, as we shall see, an idealized concept, permitting and indeed necessitating many variations and modifications before becoming available as part of a reliable and efficient production computer code. In this monograph we trace the evolution of the SQP method throug...

In this paper, we develop heuristics for finding good starting points when solving large-scale nonlinear constrained optimization problems (COPs) formulated as nonlinear programming (NLP) and mixedinteger NLP (MINLP). By exploiting the localities of constraints, we first partition each problem by parallel decomposition into subproblems that are related by complicating constraints and complicati...

In this paper a constrained nonlinear model predictive control (CNMPC) based on deterministic global optimisation is designed. The approach adopted consists in the transformation of the dynamic optimisation problem into a nonlinear programming (NLP) problem using the method of orthogonal collocation on finite elements. Rigorous convex underestimators of the nonconvex NLP problem are then derive...

This paper describes a software package for the solution of Nonlinear Programming (NLP) problems. The package implements a Sequential Quadratic Programming solver with a “filter” to promote global convergence. The solver runs with a dense or a sparse linear algebra package and a robust QP solver.

This paper describes a software package for the solution of Nonlinear Programming (NLP) problems. The package implements a Sequential Quadratic Programming solver with a \\lter" to promote global convergence. The solver runs with a dense or a sparse linear algebra package and a robust QP solver.

In this paper we present a method for converting general nonlinear programming (NLP) problems into separable programming (SP) problems by using feedforward neural networks (FNNs). The basic idea behind the method is to use two useful features of FNNs: their ability to approximate arbitrary continuous nonlinear functions with a desired degree of accuracy and their ability to express nonlinear fu...

This paper presents a study on experimentally measured degrees of conversion in successive catalyst bed levels which can be included in simultaneous optimization using nonlinear programming (NLP) algorithm. The NLP model is including equations of structural and parametric optimization of: existing catalyst model, recycled gas stream, reactor, gas turbine, heat exchangers, flash, compressors, sp...