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Survey of sequential convex programming and generalized Gauss-Newton methods* [PDF]
ESAIM: Proceedings and Surveys, 2021 We provide an overview of a class of iterative convex approximation methods for nonlinear optimization problems with convex-over-nonlinear substructure.
Messerer Florian +2 more
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Modified hybrid decomposition of the augmented Lagrangian method with larger step size for three-block separable convex programming [PDF]
Journal of Inequalities and Applications, 2018 The Jacobian decomposition and the Gauss–Seidel decomposition of augmented Lagrangian method (ALM) are two popular methods for separable convex programming. However, their convergence is not guaranteed for three-block separable convex programming.
Min Sun, Yiju Wang
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Upper bounds for packings of spheres of several radii [PDF]
Forum of Mathematics, Sigma, 2014 We give theorems that can be used to upper bound the densities of packings of different spherical caps in the unit sphere and of translates of different convex bodies in Euclidean space. These theorems extend the linear programming bounds for packings of
DAVID DE LAAT +2 more
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Stability in E-convex programming [PDF]
International Journal of Mathematics and Mathematical Sciences, 2001 We define and analyze two kinds of stability in E-convex programming problem in which the feasible domain is affected by an operator E. The first kind of this stability is that the set of all operators E that make an optimal set stable while the other ...
Ebrahim A. Youness
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Disciplined multi-convex programming [PDF]
2017 29th Chinese Control And Decision Conference (CCDC), 2017 A multi-convex optimization problem is one in which the variables can be partitioned into sets over which the problem is convex when the other variables are fixed. Multi-convex problems are generally solved approximately using variations on alternating or cyclic minimization.
Shen, Xinyue +4 more
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GuSTO: Guaranteed Sequential Trajectory optimization via Sequential Convex Programming [PDF]
IEEE International Conference on Robotics and Automation, 2019 Sequential Convex Programming (SCP) has recently seen a surge of interest as a tool for trajectory optimization. However, most available methods lack rigorous performance guarantees and they are often tailored to specific optimal control setups.
Riccardo Bonalli +3 more
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Journal of Numerical Analysis and Approximation Theory, 2004 In [6] one shows that some of the results obtained in [5] on \(E\)-convex programming are incorrect. In this paper we recover these results in the new hypotheses.
Liana Lupşa, Dorel Duca
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Super-resolution of point sources via convex programming [PDF]
IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2015 Recent work has shown that convex programming allows to recover a superposition of point sources exactly from low-resolution data as long as the sources are separated by 2/fc, where fc is the cut-off frequency of the sensing process.
Carlos Fernandez‐Granda
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Extended Formulations in Mixed-Integer Convex Programming
Conference on Integer Programming and Combinatorial Optimization, 2015 We present a unifying framework for generating extended formulations for the polyhedral outer approximations used in algorithms for mixed-integer convex programming (MICP).
Miles Lubin +3 more
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Robust Low-Thrust Trajectory Optimization Using Convex Programming and a Homotopic Approach
IEEE Transactions on Aerospace and Electronic Systems, 2022 A robust algorithm to solve the low-thrust fuel-optimal trajectory optimization problem for interplanetary spacecraft is developed in this article.
A. Morelli, C. Hofmann, F. Topputo
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