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Error Bound and Reduced-Gradient Projection Algorithms for Convex Minimization over a Polyhedral Set
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Descent methods for convex essentially smooth minimization
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Error bounds for strongly convex programs and (super)linearly convergent iterative schemes for the least 2-norm solution of linear programs
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Variable metric gradient projection processes in convex feasible sets defined by nonlinear inequalities
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Some continuity properties of polyhedral multifunctions
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Projection methods for variational inequalities with application to the traffic assignment problem
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Generalized equations and their solutions, part II: Applications to nonlinear programming
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Bounds for error in the solution set of a perturbed linear program
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Constrained minimization methods
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Convex programming in Hilbert space
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Convergence of gradient projection routing methods in an asynchronous stochastic quasi-static virtual circuit network
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On the Goldstein-Levitin-Polyak gradient projection method
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Projected Newton methods and optimization of multicommodity flows
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Distributed asynchronous optimal routing in data networks
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Global and Asymptotic Convergence Rate Estimates for a Class of Projected Gradient Processes
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Projected Newton Methods for Optimization Problems with Simple Constraints
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Lipschitz Continuity of Solutions of Linear Inequalities, Programs and Complementarity Problems
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On the Linear Convergence of Descent Methods for Convex Essentially Smooth Minimization
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Error Bound and Convergence Analysis of Matrix Splitting Algorithms for the Affine Variational Inequality Problem
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Iterative Solution of Nonlinear Equations in Several Variables
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A Posteriori Error Bounds for the Linearly-Constrained Variational Inequality Problem
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Iterative Solution of Nonlinear Equations in Several Variables
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Error bounds and convergence analysis of feasible descent methods: a general approach
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Error bounds in mathematical programming
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Relaxation Methods for Strictly Convex Regularizations of Piecewise Linear Programs
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Error bounds for inconsistent linear inequalities and programs
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Error estimates and Lipschitz constants for best approximation in continuous function spaces
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Semiconvergence and Relaxation Parameters for Projected SIRT Algorithms
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Randomized Methods for Linear Constraints: Convergence Rates and Conditioning
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Partial Affine-Scaling for Linearly Constrained Minimization
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Free-Steering Relaxation Methods for Problems with Strictly Convex Costs and Linear Constraints
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A Two-Stage Approach for Learning a Sparse Model with Sharp Excess Risk Analysis
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