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RSG: Beating Subgradient Method without Smoothness and Strong Convexity (Q4558142)

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scientific article; zbMATH DE number 6982297

Language	Label	Description	Also known as
English	RSG: Beating Subgradient Method without Smoothness and Strong Convexity	scientific article; zbMATH DE number 6982297

Statements

scholarly article

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publication date

21 November 2018

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full work available at URL

https://arxiv.org/abs/1512.03107

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http://jmlr.csail.mit.edu/papers/v19/17-016.html

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zbMATH Keywords

subgradient method

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improved convergence

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local error bound

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machine learning

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describes a project that uses

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MaRDI profile type

MaRDI publication profile

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arXiv classification

math.OC

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stat.ML

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Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality

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Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward-backward splitting, and regularized Gauss-Seidel methods

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Learning with Submodular Functions: A Convex Optimization Perspective

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Characterization of the equivalence of robustification and regularization in linear and matrix regression

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The Łojasiewicz Inequality for Nonsmooth Subanalytic Functions with Applications to Subgradient Dynamical Systems

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Clarke Subgradients of Stratifiable Functions

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Proximal alternating linearized minimization for nonconvex and nonsmooth problems

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From error bounds to the complexity of first-order descent methods for convex functions

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Weak sharp minima revisited. II: Application to linear regularity and error bounds

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Weak sharp minima revisited. III: Error bounds for differentiable convex inclusions

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Weak Sharp Minima in Mathematical Programming

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Error Bounds, Quadratic Growth, and Linear Convergence of Proximal Methods

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Second-order growth, tilt stability, and metric regularity of the subdifferential

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Finite termination of the proximal point algorithm

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New computational guarantees for solving convex optimization problems with first order methods, via a function growth condition measure

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Sparse inverse covariance estimation with the graphical lasso

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Optimal Stochastic Approximation Algorithms for Strongly Convex Stochastic Composite Optimization, II: Shrinking Procedures and Optimal Algorithms

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First-order algorithm with \({\mathcal{O}(\ln(1/\epsilon))}\) convergence for \({\epsilon}\)-equilibrium in two-person zero-sum games

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An optimal algorithm for stochastic strongly-convex optimization

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Deterministic and stochastic primal-dual subgradient algorithms for uniformly convex minimization

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Error bounds and Hölder metric subregularity

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On the Asymptotically Well Behaved Functions and Global Error Bound for Convex Polynomials

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Global error bounds for piecewise convex polynomials

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Hölder Metric Subregularity with Applications to Proximal Point Method

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On the convergence of the coordinate descent method for convex differentiable minimization

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On the Linear Convergence of Descent Methods for Convex Essentially Smooth Minimization

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Error bounds and convergence analysis of feasible descent methods: A general approach

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Higher-order metric subregularity and its applications

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Parallel Random Coordinate Descent Method for Composite Minimization: Convergence Analysis and Error Bounds

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Robust Stochastic Approximation Approach to Stochastic Programming

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Introductory lectures on convex optimization. A basic course.

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Smooth minimization of non-smooth functions

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Primal-dual subgradient methods for convex problems

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Error bounds in mathematical programming

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``Efficient” Subgradient Methods for General Convex Optimization

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Convex Analysis

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Convergence Results for Projected Line-Search Methods on Varieties of Low-Rank Matrices Via Łojasiewicz Inequality

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Non-asymptotic convergence analysis of inexact gradient methods for machine learning without strong convexity

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Weak Sharp Minima: Characterizations and Sufficient Conditions

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Block-coordinate gradient descent method for linearly constrained nonsmooth separable optimization

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A coordinate gradient descent method for nonsmooth separable minimization

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Robust Regression and Lasso

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RSG: Beating Subgradient Method without Smoothness and Strong Convexity

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An efficient primal dual prox method for non-smooth optimization

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Error Bounds for Convex Polynomials

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Restricted strong convexity and its applications to convergence analysis of gradient-type methods in convex optimization

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A unified approach to error bounds for structured convex optimization problems

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Identifiers

zbMATH Open document ID

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Mathematics Subject Classification ID

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zbMATH DE Number

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Sitelinks

Mathematics(1 entry)

mardi Publication:4558142

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