Sequential quadratic programming for large-scale nonlinear optimization

Alternative fuels mixture in cement industry kilns employing Particle Swarm Optimization algorithm

“…The parameters of PSO and PSO-CG used a population of 30 particles; c 1 = c 2 = 2.05; initial weighted w of 0.729 with linear decline up to 0.400; search space of the variables to be optimized in the interval 0 < X n < 3, where n=1,..,7; V max: 20% of the search space of each variable; and maximum number of generations (stop criterion): 500 generations. SQP uses a Hessian matrix approach (of the function Lagrange) and it uses a Quasi-Newton's method and line search for minimization of the cost function (Fletcher, 1987;Nash and Sofer, 1996;Boggs and Tolle, 2000).…”

Some mathematical aspects of price optimisation

“…The most popular method discussed in the literature for solving this type of optimisation problem is the Sequential Quadratic Programming method (SQP), see [15,16,17]. It is an iterative method that generates a sequence of quadratic programs to be solved at each iterate.…”

A solution to the inverse problem of mathematical modeling of structural mechanics for beam elements

“…The SQP method is one of the most common methods for obtaining solutions of nonlinear programming problems. One of the most efficient implementations in the case of nonlinear programming tasks having large dimensions is presented in [4]. In order to apply SQP to our problem, it is necessary to linearize the connections In this case, our task becomes a quadratic programming problem, which can be solved using the conditional gradient or the Dantzig-Wolfe decomposition method.…”