linear convergence
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization
Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong
Neural Information Processing Systems http://nips.cc/
- North America > United States > Minnesota > Hennepin County > Minneapolis (0.28)
- Asia > China > Hong Kong (0.04)
- North America > United States > New Jersey > Mercer County > Princeton (0.04)
- (3 more...)
Rest-Katyusha: Exploiting the Solution's Structure via Scheduled Restart Schemes
Junqi Tang, Mohammad Golbabaee, Francis Bach, Mike E. davies
Neural Information Processing Systems http://nips.cc/
- Oceania > Australia > New South Wales > Sydney (0.04)
- North America > Canada > Quebec > Montreal (0.04)
- Europe > United Kingdom > England > Somerset > Bath (0.04)
- Europe > France (0.04)
Primal-DualBlockGeneralized Frank-Wolfe
We propose a generalized variant of Frank-Wolfe algorithm for solving a class ofsparse/low-rank optimization problems.
A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization
Sulaiman Alghunaim, Kun Yuan, Ali H. Sayed
Neural Information Processing Systems http://nips.cc/
- North America > United States > California > Los Angeles County > Los Angeles (0.28)
- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)
- Europe > Switzerland > Vaud > Lausanne (0.04)
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Hamiltonian descent for composite objectives
Brendan O'Donoghue, Chris J. Maddison
Neural Information Processing Systems http://nips.cc/
- Asia > Middle East > Jordan (0.04)
- North America > Canada > Ontario > Hamilton (0.04)
- Europe > United Kingdom > England > Oxfordshire > Oxford (0.04)
- (3 more...)
The Physical Systems Behind Optimization Algorithms
Lin Yang, Raman Arora, Vladimir braverman, Tuo Zhao
Neural Information Processing Systems http://nips.cc/
- Asia > Middle East > Jordan (0.04)
- North America > Canada > Quebec > Montreal (0.04)
- Europe > Russia (0.04)
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A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence Carlo Alfano Department of Statistics University of Oxford
In this work, we introduce a framework for policy optimization based on mirror descent that naturally accommodates general parameterizations. The policy class induced by our scheme recovers known classes, e.g., softmax, and generates new ones depending on the choice of mirror map.
- Europe > United Kingdom > England > Oxfordshire > Oxford (0.50)
- North America > United States > Illinois > Cook County > Chicago (0.04)
- Europe > Russia (0.04)
- (3 more...)
- Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (0.93)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (0.92)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Gradient Descent (0.46)
- North America > United States > California > Santa Clara County > Palo Alto (0.04)
- North America > United States > California > San Diego County > San Diego (0.04)
- Europe > Spain > Aragón (0.04)
- (2 more...)
General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme
Tao Sun, Yuejiao Sun, Dongsheng Li, Qing Liao
Andtheexisting convergence rates are also limited to linear convergence.
- Asia > China > Guangdong Province > Shenzhen (0.05)
- North America > United States (0.04)
- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)