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The augmented lagrangian method for equality and inequality constraints in hilbert spaces

https://doi.org/10.1007/bf01585750
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Outgoing claims: the papers this work cites, builds on, or argues with.

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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 globally convergent, implementable multiplier method with automatic penalty limitation
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First and second-order necessary and sufficient optimality conditions for infinite-dimensional programming problems
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Second-order necessary and sufficient optimality conditions for infinite-dimensional programming problems
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Multiplier methods: A survey
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The method of penalty estimates for conditional extremum problems
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Projections on Convex Sets in Hilbert Space and Spectral Theory: Part I. Projections on Convex Sets: Part II. Spectral Theory
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Constrained Optimization and Lagrange Multiplier Methods
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Augmented Lagrangian Methods: Applications to the Numerical Solution of Boundary-Value Problems
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Optimization by Vector Space Methods
inferredcites →https://doi.org/10.1080/00036818608839584openalex-snapshot
Regularity properties in parameter estimation of diffusion coefficients in one dimensional elliptic boundary value problems
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Projection on a Cone, Penalty Functionals and Duality Theory for Problems with Inequaltity Constraints in Hilbert Space
inferredcites →https://doi.org/10.1201/9781003071884-2openalex-snapshot
Lipschitzian Perturbations of Infinite Optimization Problems
inferredcites →https://doi.org/10.2307/3008903openalex-snapshot
Optimization Theory: The Finite Dimensional Case

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

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The augmented Lagrangian method can approximately solve convex optimization with least constraint violation
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