ﻻ يوجد ملخص باللغة العربية
We prove that a special variety of quadratically constrained quadratic programs, occurring frequently in conjunction with the design of wave systems obeying causality and passivity (i.e. systems with bounded response), universally exhibit strong duality. Directly, the problem of continuum (grayscale or effective medium) device design for any (complex) quadratic wave objective governed by independent quadratic constraints can be solved as a convex program. The result guarantees that performance limits for many common physical objectives can be made nearly tight, and suggests far-reaching implications for problems in optics, acoustics, and quantum mechanics.
We study nonconvex homogeneous quadratically constrained quadratic optimization with one or two constraints, denoted by (QQ1) and (QQ2), respectively. (QQ2) contains (QQ1), trust region subproblem (TRS) and ellipsoid regularized total least squares p
A sequential quadratic optimization algorithm is proposed for solving smooth nonlinear equality constrained optimization problems in which the objective function is defined by an expectation of a stochastic function. The algorithmic structure of the
We consider the problem of solving a large-scale Quadratically Constrained Quadratic Program. Such problems occur naturally in many scientific and web applications. Although there are efficient methods which tackle this problem, they are mostly not s
We propose a sigmoidal approximation for the value-at-risk (that we call SigVaR) and we use this approximation to tackle nonlinear programs (NLPs) with chance constraints. We prove that the approximation is conservative and that the level of conserva
We use the technique of information relaxation to develop a duality-driven iterative approach to obtaining and improving confidence interval estimates for the true value of finite-horizon stochastic dynamic programming problems. We show that the sequ