This paper integrates Discrete Particle Swarm Optimization (DPSO) and Sequential Quadratic Programming (SQP) to propose a DPSO-SQP method for solving unit commitment problems ancillary services. Through analysis of services, including Automatic Generation Control (AGC), Real Spinning Reserve (RSR), Supplemental (SR), the cost model was developed. With requirements energy balance, operating cons...

z Abstract. A series of numerical experiments with interior point (LOQO, KNI-TRO) and active-set SQP codes (SNOPT, lterSQP) are reported and analyzed. The tests were performed with small, medium-size and moderately large problems, and are examined by problem classes. Detailed observations on the performance of the codes, and several suggestions on how to improve them are presented. Overall, int...

We consider a fractional program with both linear and quadratic equation in numerator and denominator  having second order cone (SOC) constraints. With a suitable change of variable, we transform the problem into a  second order cone programming (SOCP)  problem.  For the quadratic fractional case, using a relaxation, the problem is reduced to a semi-definite optimization (SDO) program. The p...

In this paper we study the global convergence behavior of a class of composite–step trust–region SQP methods that allow inexact problem information. The inexact problem information can result from iterative linear systems solves within the trust–region SQP method or from approximations of first–order derivatives. Accuracy requirements in our trust– region SQP methods are adjusted based on feasi...

It is well known that convex SQP subproblems with a Euclidean norm trust region constraint can be reduced to second order cone programs for which the theory of Euclidean Jordan-algebras leads to efficient interior-point algorithms. Here, a brief and self-contained outline of the principles of such an implementation is given. All identities relevant for the implementation are derived from scratc...

Mathematical programs with nonlinear complementarity constraints are refor-mulated using better-posed but nonsmooth constraints. We introduce a class offunctions, parameterized by a real scalar, to approximate these nonsmooth prob-lems by smooth nonlinear programs. This smoothing procedure has the extrabenefits that it often improves the prospect of feasibility and stability...

Sequential quadratic programming (SQP) methods are widely used for solving practical optimization problems, especially in structural mechanics. The general structure of SQP methods is briefly introduced and it is shown how these methods can be adapted to distributed computing. However, SQP methods are sensitive subject to errors in function and gradient evaluations. Typically they break down wi...

this study focuses on the optimization of the plane structure. sequential quadratic programming (sqp) will be utilized, which is one of the most efficient methods for solving nonlinearly constrained optimization problems. a new formulation for the second order sensitivity analysis of the two-dimensional finite element will be developed. all the second order required derivatives will be calculat...

S u m m a r y. MPCC can be solved with specific MPCC codes or in its nonlinear equivalent formulation (NLP) using NLP solvers. Two NLP solvers-NPSOL and the line search filter SQP-are used to solve a collection of test problems in AMPL. Both are based on SQP (Sequential Quadratic Programming) philosophy but the second one uses a line search filter scheme.

The relationship between the mathematical program with linear complementarity constraints (MPCC) and its inequality relaxation is studied. A new sequential quadratic programming (SQP) method is presented for solving the MPCC based on this relationship. A certain SQP technique is introduced to deal with the possible infeasibility of quadratic programming subproblems. Global convergence results a...