Journal ArticleDOI
Constrained Minimization Problems in Finite-Dimensional Spaces
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This article is published in Siam Journal on Control.The article was published on 1966-08-01. It has received 96 citations till now. The article focuses on the topics: Karush–Kuhn–Tucker conditions & Nonlinear programming.read more
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A limited memory algorithm for bound constrained optimization
TL;DR: An algorithm for solving large nonlinear optimization problems with simple bounds is described, based on the gradient projection method and uses a limited memory BFGS matrix to approximate the Hessian of the objective function.
Journal ArticleDOI
Constrained minimization under vector-valued criteria in finite dimensional spaces☆
N. O. Da Cunha,Elijah Polak +1 more
TL;DR: Constrained minimization problem extended to necessary conditions for characterizing non-inferior points to determine vector-valued criteria in finite dimensional spaces as discussed by the authors, where the objective is to find the smallest point in the space.
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Exact penalty functions in nonlinear programming
TL;DR: Sufficient conditions are given for the existence of exact penalty functions for inequality constrained problems more general than concave and several classes of such functions are presented.
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Spectral Projected Gradient Methods: Review and Perspectives
TL;DR: A review of so-called spectral projected gradient methods for convex constrained optimization for low-cost schemes that rely on choosing the step lengths according to novel ideas that are related to the spectrum of the underlying local Hessian.
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An implementable proximal point algorithmic framework for nuclear norm minimization
TL;DR: This paper investigates the performance of the proposed algorithms in which the inner sub-problems are approximately solved by the gradient projection method or the accelerated proximal gradient method, and shows that these algorithms perform favorably in comparison to several recently proposed state-of-the-art algorithms.