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Newton?Goldstein convergence rates for convex constrained minimization problems with singular solutions
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/bf01448383openalex-snapshot
The effect of perturbations on the convergence rates of optimization algorithms
inferredcites →https://doi.org/10.1007/bf01460129openalex-snapshot
Extremal types for certainL p minimization problems and associated large scale nonlinear programs
inferredcites →https://doi.org/10.1007/bfb0004475openalex-snapshot
Projection Methods in Constrained Optimisation and Applications to Optimal Policy Decisions
inferredcites →https://doi.org/10.1016/0041-5553(66)90114-5openalex-snapshot
Constrained minimization methods
inferredcites →https://doi.org/10.1016/0041-5553(70)90033-9openalex-snapshot
Minimization methods based on approximation of the initial functional by a convex functional
inferredcites →https://doi.org/10.1137/0306032openalex-snapshot
A Tight Upper Bound on the Rate of Convergence of Frank-Wolfe Algorithm
inferredcites →https://doi.org/10.1137/0317015openalex-snapshot
Rates of Convergence for Conditional Gradient Algorithms Near Singular and Nonsingular Extremals
inferredcites →https://doi.org/10.1137/0318035openalex-snapshot
Convergence Rates for Conditional Gradient Sequences Generated by Implicit Step Length Rules
inferredcites →https://doi.org/10.1137/0318050openalex-snapshot
Newton’s Method and the Goldstein Step-Length Rule for Constrained Minimization Problems
inferredcites →https://doi.org/10.1137/0319022openalex-snapshot
Global and Asymptotic Convergence Rate Estimates for a Class of Projected Gradient Processes
inferredcites →https://doi.org/10.1137/0320018openalex-snapshot
Projected Newton Methods for Optimization Problems with Simple Constraints
inferredcites →https://doi.org/10.1137/0715064openalex-snapshot
On Newton’s Method for Singular Problems
inferredcites →https://doi.org/10.1137/0717009openalex-snapshot
Newton’s Method at Singular Points. I
inferredcites →https://doi.org/10.1137/0717039openalex-snapshot
Newton’s Method at Singular Points. II
inferredcites →https://doi.org/10.1137/0720020openalex-snapshot
Convergence Rates for Newton’s Method at Singular Points
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/bf00939081openalex-snapshot
On the convergence of projected gradient processes to singular critical points
inferred← citeshttps://doi.org/10.1007/bf00939866openalex-snapshot
A projected Newton method in a Cartesian product of balls
inferred← citeshttps://doi.org/10.1007/bf01396325openalex-snapshot
A projected Newton method for minimization problems with nonlinear inequality constraints
inferred← citeshttps://doi.org/10.1007/bf01442181openalex-snapshot
Newton's method for singular constrained optimization problems
inferred← citeshttps://doi.org/10.1007/bf01448362openalex-snapshot
Variable metric gradient projection processes in convex feasible sets defined by nonlinear inequalities
inferred← citeshttps://doi.org/10.1007/bf02055200openalex-snapshot
Convergence of algorithms for perturbed optimization problems
inferred← citeshttps://doi.org/10.1016/b978-0-08-037036-1.50035-5openalex-snapshot
THE ROLE OF GROWTH RATES FOR GAUSS–NEWTON METHODS AND PARAMETER IDENTIFICATION PROBLEMS
inferred← citeshttps://doi.org/10.1016/s1474-6670(17)53539-9openalex-snapshot
The Role of Growth Rates for Gauss-Newton Methods and Parameter Identification Problems
inferred← citeshttps://doi.org/10.1137/0324071openalex-snapshot
Diagonally Modified Conditional Gradient Methods for Input Constrained Optimal Control Problems
inferred← citeshttps://doi.org/10.1137/s0363012992233652openalex-snapshot
On the Gradient Projection Method for Optimal Control Problems with Nonnegative $\mathcal{L}^2 $ Inputs
inferred← citeshttps://doi.org/10.1137/s0363012994266127openalex-snapshot
On ${\text{L}}^2 $ Sufficient Conditions and the Gradient Projection Method for Optimal Control Problems
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