In recent years, sequential optimality conditions are frequently used for convergence of iterative methods to solve nonlinear constrained optimization problems. The sequential optimality conditions do not require any of the constraint qualications. In this paper, We present the necessary sequential complementary approximate Karush Kuhn Tucker (CAKKT) condition for a point to be a solution of a ...

in this paper, we study the problem of minimizing the ratio of two quadratic functions subject to a quadratic constraint. first we introduce a parametric equivalent of the problem. then a bisection and a generalized newton-based method algorithms are presented to solve it. in order to solve the quadratically constrained quadratic minimization problem within both algorithms, a semidefinite optim...

Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem. Methods: In this...

Stock portfolio selection is a classic problem in finance, and it involves deciding how to allocate an institution’s or an individual’s wealth to a number of stocks, with certain investment objectives (return and risk). In this paper, we adopt the classical Markowitz mean-variance model and consider an additional common realistic constraint, namely, the cardinality constraint. Thus, stock portf...

This paper explores whether analog circuitry can adequately perform constrained optimization. Constrained optimization circuits are designed using the differential multiplier method. These circuits fulfill time-varying constraints correctly. Example circuits include a quadratic programming circuit and a constrained flip-flop.

In this paper, we extend a bio-inspired algorithm called the porcellio scaber algorithm (PSA) to solve constrained optimization problems, including a constrained mixed discrete-continuous nonlinear optimization problem. Our extensive experiment results based on benchmark optimization problems show that the PSA has a better performance than many existing methods or algorithms. The results indica...

Sequential quadratic optimization algorithms are proposed for solving smooth nonlinear problems with equality constraints. The main focus is an algorithm the case when the...

Many methods exist for solving the one-dimensional cutting stock optimization problem with usable leftovers (CSPUL), but none of them consider the excessive generation of usable leftovers (UL) in stock after several consecutiveorders. To highlight this problem, the COLA method for solving the CSPUL [1] was selected. We performed experiments that showed how UL in stock continuously grow when the...

Stock prices have the characteristics of nonlinearity, randomicity and uncertainty, so It is difficult to accurately depict the change rules of stock prices using traditional linear forecasting methods, which lead to low stock price prediction accuracy. In order to improve the stock price prediction precision , this paper proposed a stock price predicting model using SVM optimized by particle s...

A new active set algorithm (ASA) for large-scale box constrained optimization is introduced. The algorithm consists of a nonmonotone gradient projection step, an unconstrained optimization step, and a set of rules for switching between the two steps. Numerical experiments and comparisons are presented using box constrained problems in the CUTEr and MINPACK test problem libraries. keywords: Nonm...