Analysis of Online Composite Mirror Descent Algorithm

DOI10.1162/NECO_a_00930zbMath1474.68314OpenAlexW2578544862WikidataQ39016839 ScholiaQ39016839MaRDI QIDQ5380674

Ding-Xuan Zhou, Yunwen Lei

Publication date: 6 June 2019

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco_a_00930

Mathematics Subject Classification ID

Analysis of algorithms (68W40) Convex programming (90C25) Online algorithms; streaming algorithms (68W27) Computational aspects of data analysis and big data (68T09)

Related Items (7)

Block coordinate type methods for optimization and learning ⋮ A stochastic variance reduction algorithm with Bregman distances for structured composite problems ⋮ Federated learning for minimizing nonsmooth convex loss functions ⋮ Convergence of online mirror descent ⋮ Learning Theory of Randomized Sparse Kaczmarz Method ⋮ Analysis of singular value thresholding algorithm for matrix completion ⋮ Error analysis of the kernel regularized regression based on refined convex losses and RKBSs

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unregularized online learning algorithms with general loss functions
• Online gradient descent learning algorithms
• Sharp uniform convexity and smoothness inequalities for trace norms
• Modified Fejér sequences and applications
• Mirror descent and nonlinear projected subgradient methods for convex optimization.
• Convergence of stochastic proximal gradient algorithm
• Online learning algorithms
• Iterative Regularization for Learning with Convex Loss Functions
• Online Learning as Stochastic Approximation of Regularization Paths: Optimality and Almost-Sure Convergence
• Linearized Bregman iterations for compressed sensing
• Support Vector Machines
• On the Generalization Ability of On-Line Learning Algorithms
• Online Regularized Classification Algorithms
• ONLINE LEARNING WITH MARKOV SAMPLING
• Splitting Algorithms for the Sum of Two Nonlinear Operators
• Regularization schemes for minimum error entropy principle
• Information-Theoretic Lower Bounds on the Oracle Complexity of Stochastic Convex Optimization
• Online Pairwise Learning Algorithms
• Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing

This page was built for publication: Analysis of Online Composite Mirror Descent Algorithm