By introducing quadratic penalty terms, a convex nonseparable quadratic network program can be reduced to an unconstrained optimization problem whose objective function is a piecewise quadratic and continuously differentiable function. A conjugate gradient method is applied to the reduced problem and its convergence is proved. The computation exploits the special network data structures origina...

The interaction between linear, quadratic programming and regression analysis are explored by both statistical and operations research methods. Estimation and optimization problems are formulated in two different ways: on one hand linear and quadratic programming problems are formulated and solved by statistical methods, and on the other hand the solution of the linear regression model with con...

CH4, C2H6 and C2H4 are the most important outlet gaseous of oxidative couple methane (OCM) reaction and this process is a new technology for conversion of natural gas to ethane and ethylene products. In this study, adsorption of OCM outlet hydrocarbons over 10X zeolite has been examined at equilibrium conditions. Temperature and pressure are the most effective operational parameters in the batc...

We consider the global optimization of nonconvex mixed-integer quadratic programs with linear equality constraints. In particular, we present a new class convex relaxations which are derived via cuts. To construct these cuts, solve separation problem involving matrix inequality special structure that allows use specialized solution algorithms. Our cuts nonconvex, but define feasible set when in...

L1 regularized logistic regression is now a workhorse of machine learning: it is widely used for many classification problems, particularly ones with many features. L1 regularized logistic regression requires solving a convex optimization problem. However, standard algorithms for solving convex optimization problems do not scale well enough to handle the large datasets encountered in many pract...

This paper presents a new variant of Particle Swarm Optimization algorithm named QPSO for solving global optimization problems. QPSO is an integrated algorithm making use of a newly defined, multiparent, quadratic crossover operator in the Basic Particle Swarm Optimization (BPSO) algorithm. The comparisons of numerical results show that QPSO outperforms BPSO algorithm in all the twelve cases ta...

The connections between optimization and control theory have been explored by many researchers , and optimization algorithms have been applied with success to optimal control. The rapid pace of developments in model predictive control has given rise to a host of new problems to which optimization has yet to be applied. Concurrently, developments in optimization, and especially in interior-point...

The connections between optimization and control theory have been explored by many researchers, and optimization algorithms have been applied with success to optimal control. The rapid pace of developments in model predictive control has given rise to a host of new problems to which optimization has yet to be applied. Concurrently, developments in optimization, and especially in interior-point ...

A central problem of branch and bound methods for global optimization is that lower bounds are often not exact even if the diameter of the subdivided regions shrinks to zero This can lead to a large number of subdivisions preventing the method from terminating in reasonable time For the all quadratic optimization problem with convex constraints we present locally exact lower bounds and optimali...

Quadratic elements place stringent requirements on a surface mesh smoother. One of the biggest challenges is that a good linear element may become invalid when mid-side nodes are introduced. To help alleviate this problem, a new objective function for optimization-based smoothing is proposed for triangular and quadrilateral elements, linear or quadratic. Unlike the current popular approaches, t...