On the public citation graph

Variable metric gradient projection processes in convex feasible sets defined by nonlinear inequalities

https://doi.org/10.1007/bf01448362
0 signed · 28 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

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

inferredcites →https://doi.org/10.1007/bf00939081openalex-snapshot
On the convergence of projected gradient processes to singular critical points
inferredcites →https://doi.org/10.1007/bf01449032openalex-snapshot
A class of superlinearly convergent projection algorithms with relaxed stepsizes
inferredcites →https://doi.org/10.1007/bf01449042openalex-snapshot
Newton?Goldstein convergence rates for convex constrained minimization problems with singular solutions
inferredcites →https://doi.org/10.1016/0041-5553(66)90114-5openalex-snapshot
Constrained minimization methods
inferredcites →https://doi.org/10.1016/b978-0-12-468662-5.50011-2openalex-snapshot
ITERATIVE ALGORITHMS FOR SINGULAR MINIMIZATION PROBLEMS
inferredcites →https://doi.org/10.1090/s0002-9904-1964-11178-2openalex-snapshot
Convex programming in Hilbert space
inferredcites →https://doi.org/10.1109/tac.1976.1101194openalex-snapshot
On the Goldstein-Levitin-Polyak gradient projection method
inferredcites →https://doi.org/10.1137/0303013openalex-snapshot
On Steepest Descent
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/10.1137/0322061openalex-snapshot
Two-Metric Projection Methods for Constrained Optimization

What points at this work

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

inferred← citeshttps://doi.org/10.1007/978-1-4613-3632-7_6openalex-snapshot
Gradient-Related Constrained Minimization Algorithms in Function Spaces: Convergence Properties and Computational Implications
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/bf02096261openalex-snapshot
Error bounds and convergence analysis of feasible descent methods: a general approach
inferred← citeshttps://doi.org/10.1007/bfb0042769openalex-snapshot
Trust regions and projected gradients
inferred← citeshttps://doi.org/10.1137/0329062openalex-snapshot
A Subspace Decomposition Principle for Scaled Gradient Projection Methods: Global Theory
inferred← citeshttps://doi.org/10.1137/0330029openalex-snapshot
Mesh Independence of the Gradient Projection Method for Optimal Control Problems
inferred← citeshttps://doi.org/10.1137/0331014openalex-snapshot
A Subspace Decomposition Principle for Scaled Gradient Projection Methods: Local Theory
inferred← citeshttps://doi.org/10.1137/0331048openalex-snapshot
Identifiable Surfaces in Constrained Optimization
inferred← citeshttps://doi.org/10.1137/0725068openalex-snapshot
On the Identification of Active Constraints
inferred← citeshttps://doi.org/10.1137/0801037openalex-snapshot
Partitioned Dynamic Programming for Optimal Control
inferred← citeshttps://doi.org/10.1137/0803003openalex-snapshot
Error Bound and Reduced-Gradient Projection Algorithms for Convex Minimization over a Polyhedral Set
inferred← citeshttps://doi.org/10.1137/16m1092301openalex-snapshot
An Extension of the Projected Gradient Method to a Banach Space Setting with Application in Structural Topology Optimization
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/s036301299224031xopenalex-snapshot
Second-Order Optimality Conditions in Sets of ${\text{L}}^\infty $ Functions with Range in a Polyhedron
inferred← citeshttps://doi.org/10.1137/s0363012994266127openalex-snapshot
On ${\text{L}}^2 $ Sufficient Conditions and the Gradient Projection Method for Optimal Control Problems
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.