The utility of numerical codes is greatly enhanced if they can be used in design, a situation that typically involves iterative optimization algorithms. An attractive way is to use gradient-based algorithms developed for solving nonlinear programming (NLP) problems. In this letter, we examine the performance of a general sequential quadratic programming (SQP) optimization algorithm for designin...

Convex Nonparametric Least Squares (CNLSs) is a nonparametric regression method that does not require a priori specification of the functional form. The CNLS problem is solved by mathematical programming techniques; however, since the CNLS problem size grows quadratically as a function of the number of observations, standard quadratic programming (QP) and Nonlinear Programming (NLP) algorithms ...

We examine various aspects of modeling and solution via mixedinteger nonlinear programming (MINLP). MINLP has much to offer as a powerful modeling paradigm. Recently, significant advances have been made in MINLP solution software. To fully realize the power of MINLP to solve complex business optimization problems, we need to develop knowledge and expertise concerning MINLP modeling and solution...

In finance, the most efficient portfolio is tangency portfolio, which formed by intersection point of frontier and capital market line. This paper defines explores a time-varying under nonlinear constraints (TV-TPNC) problem as programming (NLP) problem. Because meta-heuristics are commonly used to solve NLP problems, semi-integer beetle antennae search (SIBAS) algorithm proposed for solving ca...

Accurate modelling of real-world problems often requires nonconvex terms to be introduced in the model, either in the objective function or in the constraints. Nonconvex programming is one of the hardest fields of optimization, presenting many challenges in both practical and theoretical aspects. The presence of multiple local minima calls for the application of global optimization techniques. ...

Social cognitive optimization (SCO) for solving nonlinear programming problems (NLP) is presented based on human intelligence with the social cognitive theory (SCT). The experiments by comparing SCO with genetic algorithms on some benchmark functions show that it can get highquality solutions efficiently, even by only one learning agent.

Many recent convergence results obtained for primal-dual interior-point methods for nonlinear programming, use assumptions of the boundedness of generated iterates. In this paper we replace such assumptions by new assumptions on the NLP problem, develop a modification of a primal-dual interior-point method implemented in software package loqo and analyze convergence of the new method from any i...

Background The aim of the present research was to compare the effectiveness of training cognitive behavioral therapy and Neuro-linguistic programming (NLP) strategies on mitigating anxiety, depression, and stress of students. Materials and Methods: The method of this semi-experimental research was pretest posttest with control grou...

In the simultaneous heat and power integration approach with additional production the optimization problem is formulated using superstructure. Nonlinear programming (NLP) contains equations enabling structural and parametric optimization. In the present work the NLP model is formulated with the optimum energy target of the process integration and generation of electricity using a gas turbine. ...