In this paper, a sequential quadratic programming method combined with a trust region globalization strategy is analyzed and studied for solving a certain nonlinear constrained optimization problem with matrix variables. The optimization problem is derived from the infinite-horizon linear quadratic control problem for discrete-time systems when a complete set of state variables is not available...

We consider the inexact restoration and the composite-step sequential quadratic programming (SQP) methods, and relate them to the so-called perturbed SQP framework. In particular, iterations of the methods in question are interpreted as certain structured perturbations of the basic SQP iterations. This gives a different insight into local behaviour of those algorithms, as well as improved or di...

The question of post-retirement optimal consumption and investment of retirement savings is addressed. This problem has received considerably less attention than that of how to invest for retirement. With the increase in life span and an increase in private pension funds, a retiree has considerable flexibility in both how to consume and how to continue to invest their retirement funds. Our inte...

Voltage collapse; Sequential quadratic programming; FACTS devices; Voltage security margin; Optimal continuous power flow. Abstract In this paper, an effective method is presented to determine the security margin against voltage collapse, and improve it by means of FACTS devices in the optimal continuous power flow framework. The primary methods for determining the system’s critical states of v...

The problem of choosing an optimal signal set for non-Gaussian detection was reduced to a smooth inequality constrained mini-max nonlinear programming problem by Gockenbach and Kearsley. Here we consider the application of several optimization algorithms, both global and local, to this problem. The most promising results are obtained when special-purpose sequential quadratic programming (SQP) a...

We propose an SQP algorithm for mathematical programs with vanishing constraints which solves at each iteration a quadratic program with linear vanishing constraints. The algorithm is based on the newly developed concept of [Formula: see text]-stationarity (Benko and Gfrerer in Optimization 66(1):61-92, 2017). We demonstrate how [Formula: see text]-stationary solutions of the quadratic program ...

This paper presents a procedure for performing parameter optimization of ignition and growth continuum models for high explosive systems. Continuum modeling of reactive flow in high explosive systems can yield highly accurate predictions of experimental observations. However, the numerical parameter optimization that is needed to establish these predictions generally requires many evaluations o...

Sequential quadratic programming (SQP) methods form a class of highly efficient algorithms for solving nonlinearly constrained optimization problems. Although second derivative information may often be calculated, there is little practical theory that justifies exact-Hessian SQP methods. In particular, the resulting quadratic programming (QP) subproblems are often nonconvex, and thus finding th...

This paper provides closed-form equations parameterizing the 2 smooth path that globally minimizes the Euclidean norm of a robot’s peak base reaction force while avoiding obstacles during three-dimensional maneuvers in a gravity-free environment. In addition, the paper describes a computationally efficient technique that leads to a path typically having a peak force within 5% of the optimal pat...

This paper presents hybrid differential evolution DE and sequential quadratic programming SQP for solving the dynamic economic dispatch DED problem for generating units with valvepoint effects. DE is used as a global optimizer and SQP is used as a fine tuning to determine the optimal solution at the final. The feasibility of the proposed method is validated with fiveand ten-unit test systems. R...