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Smooth Optimization Methods for Minimax Problems

https://doi.org/10.1137/0326071
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Minimization Methods for Non-Differentiable Functions
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Approximation procedures based on the method of multipliers
inferredcites →https://doi.org/10.1007/bf00934777openalex-snapshot
The multiplier method of Hestenes and Powell applied to convex programming
inferredcites →https://doi.org/10.1007/bf01588251openalex-snapshot
Acceleration of the leastpth algorithm for minimax optimization with engineering applications
inferredcites →https://doi.org/10.1007/bf01589328openalex-snapshot
Variable metric methods for minimizing a class of nondifferentiable functions
inferredcites →https://doi.org/10.1007/bf01589332openalex-snapshot
Combined lp and quasi-Newton methods for minimax optimization
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Nonlinear leastpth optimization and nonlinear programming
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Nondifferentiable optimization via approximation
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Polynomial algorithms in linear programming
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Constrained Optimization and Lagrange Multiplier Methods
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Iterative Solution of Nonlinear Equations in Several Variables
inferredcites →https://doi.org/10.1093/comjnl/12.3.207-aopenalex-snapshot
Nonlinear Programming--Sequential Unconstrained Minimization Techniques
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A new algorithm for solution of resistive networks involving diodes
inferredcites →https://doi.org/10.1111/j.1749-6632.1983.tb23311.xopenalex-snapshot
SMOOTH OPTIMIZATION METHODS IN DISCRETE MINIMAX PROBLEMS
inferredcites →https://doi.org/10.1137/0314020openalex-snapshot
Combined Primal–Dual and Penalty Methods for Convex Programming
inferredcites →https://doi.org/10.2307/2342801openalex-snapshot
Studies in Linear and Non-Linear Programming.

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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