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Combined Primal–Dual and Penalty Methods for Convex Programming

https://doi.org/10.1137/0314020
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Penalty methods and augmented Lagrangians in nonlinear programming
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Multiplier and gradient methods
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The multiplier method of Hestenes and Powell applied to convex programming
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A dual approach to solving nonlinear programming problems by unconstrained optimization
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Convergence bounds for nonlinear programming algorithms
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Necessary and sufficient conditions for a penalty method to be exact
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A new method for the optimization of a nonlinear function subject to nonlinear constraints
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A Comparison of Several Current Optimization Methods, and the use of Transformations in Constrained Problems
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On the method of multipliers for convex programming
inferredcites →https://doi.org/10.1137/0312021openalex-snapshot
Augmented Lagrange Multiplier Functions and Duality in Nonconvex Programming
inferredcites →https://doi.org/10.1137/0313030openalex-snapshot
Combined Primal-Dual and Penalty Methods for Constrained Minimization
inferredcites →https://doi.org/10.1137/0314017openalex-snapshot
On Penalty and Multiplier Methods for Constrained Minimization
inferredcites →https://doi.org/10.1515/9781400873173openalex-snapshot
Convex Analysis
inferredcites →https://doi.org/10.2307/2004942openalex-snapshot
Iterative Solution of Nonlinear Equations in Several Variables

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ON PENALTY AND MULTIPLIER METHODS FOR CONSTRAINED MINIMIZATION**This work was supported by the Joint Services Electronics Program (U.S. Army, U.S. Navy, U.S. Air Force) under Contract DAAB-07-72-C-0259.
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Optimization for Machine Learning
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