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An augmented Lagrangian filter method
Mathematical Methods of Operations Research, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sven Leyffer, Charlie Vanaret
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Augmented Lagrangian Methods for Convex Matrix Optimization Problems
Journal of the Operations Research Society of China, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ying Cui +3 more
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An Augmented Lagrangian method for quasi-equilibrium problems
Computational Optimization and Applications, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
L. F. Bueno +3 more
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IMA Journal of Numerical Analysis, 2020
The augmented Lagrangian method (ALM) is fundamental in solving convex programming problems with linear constraints. The proximal version of ALM, which regularizes ALM’s subproblem over the primal variable at each iteration by an additional positive ...
B. He, Fengming Ma, Xiaoming Yuan
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The augmented Lagrangian method (ALM) is fundamental in solving convex programming problems with linear constraints. The proximal version of ALM, which regularizes ALM’s subproblem over the primal variable at each iteration by an additional positive ...
B. He, Fengming Ma, Xiaoming Yuan
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Inexact accelerated augmented Lagrangian methods
Computational Optimization and Applications, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kang, Myeongmin +2 more
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QPALM: a proximal augmented lagrangian method for nonconvex quadratic programs
Mathematical Programming Computation, 2020We propose QPALM, a nonconvex quadratic programming (QP) solver based on the proximal augmented Lagrangian method. This method solves a sequence of inner subproblems which can be enforced to be strongly convex and which therefore admit a unique solution.
Ben Hermans +2 more
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Mathematics of Operations Research
Nonconvex constrained stochastic optimization has emerged in many important application areas. Subject to general functional constraints, it minimizes the sum of an expectation function and a nonsmooth regularizer.
Qiankun Shi, Xiao Wang, Hao Wang
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Nonconvex constrained stochastic optimization has emerged in many important application areas. Subject to general functional constraints, it minimizes the sum of an expectation function and a nonsmooth regularizer.
Qiankun Shi, Xiao Wang, Hao Wang
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Augmented Lagrangian method for probabilistic optimization
Annals of Operations Research, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dentcheva, Darinka, Martinez, Gabriela
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Extended full waveform inversion in the time domain by the augmented Lagrangian method
Geophysics, 2020The search space of Full Waveform Inversion (FWI) can be extended via a relaxation of the wave equation to increase the linear regime of the inversion.
A. Gholami, H. Aghamiry, S. Operto
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Mathematical programming, 2021
There are many important practical optimization problems whose feasible regions are not known to be nonempty or not, and optimizers of the objective function with the least constraint violation prefer to be found.
Yuhong Dai, Liwei Zhang
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There are many important practical optimization problems whose feasible regions are not known to be nonempty or not, and optimizers of the objective function with the least constraint violation prefer to be found.
Yuhong Dai, Liwei Zhang
semanticscholar +1 more source