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- Yin Tat Lee, Aaron Sidford
- 2013 IEEE 54th Annual Symposium on Foundations ofâ€¦
- 2013

In this paper we show how to accelerate randomized coordinate descent methods and achieve faster convergence rates without paying per-iteration costs in asymptotic running time. In particular, weâ€¦ (More)

In this paper, we determine the optimal convergence rates for strongly convex and smooth distributed optimization in two settings: centralized and decentralized communications over a network. Forâ€¦ (More)

- Yin Tat Lee, Aaron Sidford, Sam Chiu-wai Wong
- 2015 IEEE 56th Annual Symposium on Foundations ofâ€¦
- 2015

In this paper we improve upon the running time for finding a point in a convex set given a separation oracle. In particular, given a separation oracle for a convex set K âŠ‚ R<sup>n</sup> that isâ€¦ (More)

- Yin Tat Lee, Aaron Sidford
- 2014 IEEE 55th Annual Symposium on Foundations ofâ€¦
- 2014

In this paper, we present a new algorithm for '/ solving linear programs that requires only OÌƒ(âˆšrank(A)L) iterations where A is the constraint matrix of a linear program with m constraints, nâ€¦ (More)

In this paper we provide faster algorithms for solving the geometric median problem: given <i>n</i> points in <sup><i>d</i></sup> compute a point that minimizes the sum of Euclidean distances to theâ€¦ (More)

Random sampling has become a critical tool in solving massive matrix problems. For linear regression, a small, manageable set of data rows can be randomly selected to approximate a tall, skinny dataâ€¦ (More)

- SÃ©bastien Bubeck, Yin Tat Lee, Mohit Singh
- ArXiv
- 2015

We propose a new method for unconstrained optimization of a s mooth and strongly convex function, which attains the optimal rate of convergence of N esterovâ€™s accelerated gradient descent. The newâ€¦ (More)

- Yin Tat Lee, He Sun
- STOC
- 2017

For any undirected and weighted graph <i>G</i>=(<i>V</i>,<i>E</i>,<i>w</i>) with <i>n</i> vertices and <i>m</i> edges, we call a sparse subgraph <i>H</i> of <i>G</i>, with proper reweighting of theâ€¦ (More)

- Zeyuan Allen Zhu, Yin Tat Lee, Lorenzo Orecchia
- SODA
- 2016

We study the design of polylogarithmic depth algorithms for approximately solving packing and covering semidefinite programs (or positive SDPs for short). This is a natural SDP generalization of theâ€¦ (More)

In this paper, we introduce a new framework for approximately solving flow problems in capacitated, undirected graphs and apply it to provide asymptotically faster algorithms for the maximum s-t flowâ€¦ (More)