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08/21/2023
GBM-based Bregman Proximal Algorithms for Constrained Learning
As the complexity of learning tasks surges, modern machine learning enco...
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08/21/2023
A Homogenization Approach for Gradient-Dominated Stochastic Optimization
Gradient dominance property is a condition weaker than strong convexity,...
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04/10/2023
First-order methods for Stochastic Variational Inequality problems with Function Constraints
The monotone Variational Inequality (VI) is an important problem in mach...
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01/30/2023
Delayed Stochastic Algorithms for Distributed Weakly Convex Optimization
This paper studies delayed stochastic algorithms for weakly convex optim...
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01/28/2023
Stochastic Dimension-reduced Second-order Methods for Policy Optimization
In this paper, we propose several new stochastic second-order algorithms...
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12/21/2022
Efficient First-order Methods for Convex Optimization with Strongly Convex Function Constraints
Convex function constrained optimization has received growing research i...
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06/06/2021
Minibatch and Momentum Model-based Methods for Stochastic Non-smooth Non-convex Optimization
Stochastic model-based methods have received increasing attention lately...
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10/23/2020
A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization
Nonconvex sparse models have received significant attention in high-dime...
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09/26/2019
Artificial Intelligence BlockCloud (AIBC) Technical Whitepaper
The AIBC is an Artificial Intelligence and blockchain technology based l...
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09/03/2019
Efficiency of Coordinate Descent Methods For Structured Nonconvex Optimization
Novel coordinate descent (CD) methods are proposed for minimizing noncon...
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08/07/2019
Proximal Point Methods for Optimization with Nonconvex Functional Constraints
Nonconvex optimization is becoming more and more important in machine le...
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10/01/2018