Fast gradient descent for convex minimization problems with an oracle producing a \(( \delta, L)\)-model of function at the requested point
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Publication:2278192
DOI10.1134/S0965542519070078OpenAlexW2968762515MaRDI QIDQ2278192
A. I. Tyurin, Alexander V. Gasnikov
Publication date: 4 December 2019
Published in: Computational Mathematics and Mathematical Physics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1134/s0965542519070078
gradient descentconditional gradient methodcomposite optimizationuniversal methodfast gradient descentmodel of function
Related Items (10)
First-order methods for convex optimization ⋮ Unifying framework for accelerated randomized methods in convex optimization ⋮ A heuristic adaptive fast gradient method in stochastic optimization problems ⋮ Accelerated and unaccelerated stochastic gradient descent in model generality ⋮ Accelerated methods for saddle-point problem ⋮ Analogues of Switching Subgradient Schemes for Relatively Lipschitz-Continuous Convex Programming Problems ⋮ Solving convex min-min problems with smoothness and strong convexity in one group of variables and low dimension in the other ⋮ Inexact model: a framework for optimization and variational inequalities ⋮ Universal intermediate gradient method for convex problems with inexact oracle ⋮ Convex optimization with inexact gradients in Hilbert space and applications to elliptic inverse problems
Uses Software
Cites Work
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- Gradient methods for minimizing composite functions
- First-order methods of smooth convex optimization with inexact oracle
- On lower complexity bounds for large-scale smooth convex optimization
- Conditional gradient algorithms for norm-regularized smooth convex optimization
- Universal gradient methods for convex optimization problems
- Stochastic intermediate gradient method for convex problems with stochastic inexact oracle
- Dual approaches to the minimization of strongly convex functionals with a simple structure under affine constraints
- Dual methods for finding equilibriums in mixed models of flow distribution in large transportation networks
- Complexity bounds for primal-dual methods minimizing the model of objective function
- Non-smooth non-convex Bregman minimization: unification and new algorithms
- Bundle-level type methods uniformly optimal for smooth and nonsmooth convex optimization
- Lectures on Modern Convex Optimization
- Deterministic and stochastic primal-dual subgradient algorithms for uniformly convex minimization
- Optimal methods of smooth convex minimization
- Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice
- Accuracy Guarantees for <formula formulatype="inline"> <tex Notation="TeX">$\ell_1$</tex></formula>-Recovery
- Exact Worst-Case Performance of First-Order Methods for Composite Convex Optimization
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