We present an algorithm for large-scale equality constrained optimization. The method is based on a characterization of inexact sequential quadratic programming (SQP) steps that can ensure global convergence. Inexact SQP methods are needed for large-scale applications for which the iteration matrix cannot be explicitly formed or factored and the arising linear systems must be solved using itera...

We present the first bioluminescence tomography algorithm that makes use of the PDEconstrained concept, which has shown to lead to significant savings in computation times in similar applications. Implementing a sequential quadratic programming (SQP) method, we solve the forward and inverse problems simultaneously. Using numerical results we show that the PDEconstrained SQP approach leads to ~1...

We propose an SQP algorithm for mathematical programs with complementarity constraints which solves at each iteration a quadratic program with linear complementarity constraints. We demonstrate how strongly M-stationary solutions of this quadratic program can be obtained by an active set method without using enumeration techniques. We show that all limit points of the sequence of iterate genera...

Merit functions utilized to monitor the convergence of sequential quadratic programming (SQP) methods for nonlinear programs and variational inequality problems have in common that they include a penalty function for the explicit constraints, the value of the penalty parameter for which is subject to the requirement of being large enough compared to estimates of the optimal Lagrange multipliers...

An algorithm for general nonlinearly constrained optimization is presented, which solves an unconstrained piecewise quadratic subprob-lem and a quadratic programming subproblem at each iterate. The algorithm is robust since it can circumvent the diiculties associated with the possible inconsistency of QP subproblem of the original SQP method. Moreover, the algorithm can converge to a point whic...

in this paper, an integration of improve particle swarm optimization (ipso) incombination with successive quadratic programming (sqp) so called ipso-sqpalgorithm is proposed to solve time optimal bang-bang control problems. theprocedure is found not sensitive to the initial guess of the solution. due to randomselection in the first stage of the search process, the chance of converging to theglo...

In this paper, we focus on the mathematical program with second-order cone (SOC) complementarity constraints, which contains the well-known mathematical program with nonnegative complementarity constraints as a subclass. For solving such a problem, we propose a smoothing-based sequential quadratic programming (SQP) methods. We first replace the SOC complementarity constraints with equality cons...

The aim of the present study is to a new model based on nonlinear singular second order delay differential equation Lane–Emden type and numerically solved by using heuristic technique. Four different examples are presented designed artificial neural networks optimized global search, local search methods their hybrid combinations, respectively, named as genetic algorithm (GA), sequential quadrat...

The current work focuses on the development and application of an efficient algorithm for optimization of threedimensional turbulent flows, simulated using Direct Numerical Simulation (DNS) or large-eddy simulations, and further characterized by large-dimensional optimization-parameter spaces. The optimization algorithm is based on Sequential Quadratic Programming (SQP) in combination with a da...