This paper proposes a novel algorithm for solving NP-hard constrained discrete minimization problems whose unconstrained versions are solvable in polynomial time such as constrained submodular function minimization. Applications of our algorithm include constrained MAP inference in Markov Random Fields, and energy minimization in various computer vision problems. Our algorithm assumes the exist...

Constrained optimization problems have a wide range of applications in science, economics, and engineering. In this paper, a neural network model is proposed to solve a class of nonsmooth constrained optimization problems with a nonsmooth convex objective function subject to nonlinear inequality and affine equality constraints. It is a one-layer non-penalty recurrent neural network based on the...

We propose a procedure for building symmetric positive definite band preconditioners for large-scale symmetric, possibly indefinite, linear systems, when the coefficient matrix is not explicitly available, but matrix-vector products involving it can be computed. We focus on linear systems arising in Newton-type iterations within matrix-free versions of projected methods for bound-constrained no...

DIRECT (DIviding RECTangles) [16] is an optimization algorithm designed to aggressively search for global minima of a real valued objective function over a bound-constrained domain. The algorithm does not use derivative information in its search; instead, it relies on the iteration history to determine future sample locations. The strength of DIRECT is the balanced global and local search it pe...

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...

Considering the effect of the utility harmonic impedance variations on harmonic responsibility, a method based on piecewise bound constrained optimization is proposed in this paper to evaluate the load harmonic responsibilities. The wavelet packet transform is employed to determine the change times of the utility harmonic impedances. The harmonic monitoring data is divided into several segments...

the markowitz’s optimization problem is considered as a standard quadratic programming problem that has exact mathematical solutions. considering real world limits and conditions, the portfolio optimization problem is a mixed quadratic and integer programming problem for which efficient algorithms do not exist. therefore, the use of meta-heuristic methods such as neural networks and evolutionar...

Production optimization involves the determination of optimum well controls to maximize an objective function such as cumulative oil production or net present value. In practice, this problem additionally requires the satisfaction of physical and economic constraints. Thus the overall problem represents a challenging nonlinearly constrained optimization. This work entails a comparative study of...

in this paper, according to high load capacity and rather large workspace characteristics of cable driven robots (cdrs), maximum dynamic load carrying capacity (dlcc) between two given end points in the workspace along with optimal trajectory is obtained. in order to find dlcc of cdrs, joint actuator torque and workspace of the robot constraints concerning to non-negative tension in cables are ...

This paper presents a numerical study of a stochastic augmented Lagrangian algorithm to solve continuous constrained global optimization problems. The algorithm approximately solves a sequence of bound constrained subproblems whose objective function penalizes equality and inequality constraints violation and depends on the Lagrange multiplier vectors and a penalty parameter. Each subproblem is...