Linear Convergence of Descent Methods for the Unconstrained Minimization of Restricted Strongly Convex Functions
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Publication:2821800
DOI10.1137/140992990zbMath1348.65094OpenAlexW2512490502MaRDI QIDQ2821800
Publication date: 23 September 2016
Published in: SIAM Journal on Optimization (Search for Journal in Brave)
Full work available at URL: https://semanticscholar.org/paper/2c8b02a331801460a35cc8a8fe7bf33684f7c656
conjugate gradient methoderror boundlinear convergencegradient descent methodconjugate gradientBroyden-Fletcher-Goldfarb-Shanno methodrestricted strong convexityquadratic splines
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Uses Software
Cites Work
- Strongly convex programming for exact matrix completion and robust principal component analysis
- Approximation accuracy, gradient methods, and error bound for structured convex optimization
- Global convergence of a two-parameter family of conjugate gradient methods without line search
- A nonmonotone conjugate gradient algorithm for unconstrained optimization
- On the limited memory BFGS method for large scale optimization
- Efficient generalized conjugate gradient algorithms. I: Theory
- An algorithm for a singly constrained class of quadratic programs subject upper and lower bounds
- A new algorithm of nonlinear conjugate gradient method with strong convergence
- Fast linearized Bregman iteration for compressive sensing and sparse denoising
- Efficient hybrid conjugate gradient techniques
- Characterization of the subdifferential of some matrix norms
- Error bounds and convergence analysis of feasible descent methods: A general approach
- Error bounds in mathematical programming
- On the characterization of quadratic splines
- Strong conical hull intersection property, bounded linear regularity, Jameson's property \((G)\), and error bounds in convex optimization
- Linearly convergent descent methods for the unconstrained minimization of convex quadratic splines
- A conjugate gradient method for the unconstrained minimization of strictly convex quadratic splines
- Damped techniques for the limited memory BFGS method for large-scale optimization
- Restricted strong convexity and its applications to convergence analysis of gradient-type methods in convex optimization
- Alternative proofs of the convergence properties of the conjugate- gradient method
- Nonsmooth analysis of singular values. I: Theory
- Nonsmooth analysis of singular values. II: Applications
- Augmented $\ell_1$ and Nuclear-Norm Models with a Globally Linearly Convergent Algorithm
- The Linearized Bregman Method via Split Feasibility Problems: Analysis and Generalizations
- Asynchronous Stochastic Coordinate Descent: Parallelism and Convergence Properties
- Robust principal component analysis?
- Analysis and Generalizations of the Linearized Bregman Method
- Convergence of the linearized Bregman iteration for ℓ₁-norm minimization
- A Singular Value Thresholding Algorithm for Matrix Completion
- Rank-Sparsity Incoherence for Matrix Decomposition
- A Newton Method for Convex Regression, Data Smoothing, and Quadratic Programming with Bounded Constraints
- Sparse and Redundant Representations
- Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
- Global Convergence of a Cass of Quasi-Newton Methods on Convex Problems
- A Tool for the Analysis of Quasi-Newton Methods with Application to Unconstrained Minimization
- Some continuity properties of polyhedral multifunctions
- On the Linear Convergence of Descent Methods for Convex Essentially Smooth Minimization
- Global Convergence Properties of Conjugate Gradient Methods for Optimization
- Convergence conditions for restarted conjugate gradient methods with inaccurate line searches
- Quasi-Newton Methods, Motivation and Theory
- Algorithms for nonlinear constraints that use lagrangian functions
- Atomic Decomposition by Basis Pursuit
- Numerical Optimization
- Variational Analysis
- Strong Semismoothness of Eigenvalues of Symmetric Matrices and Its Application to Inverse Eigenvalue Problems
- Rate of Convergence of Several Conjugate Gradient Algorithms
- Error Bounds for Piecewise Convex Quadratic Programs and Applications
- Exact Regularization of Polyhedral Norms
- A Nonlinear Conjugate Gradient Method with a Strong Global Convergence Property
- A New Conjugate Gradient Method with Guaranteed Descent and an Efficient Line Search
- Function minimization by conjugate gradients
- The conjugate gradient method in extremal problems
- Linear Convergence of the Conjugate Gradient Method
- Methods of conjugate gradients for solving linear systems
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