#### Filter Results:

- Full text PDF available (32)

#### Publication Year

2000

2017

- This year (12)
- Last 5 years (38)
- Last 10 years (38)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

Learn More

- Yin Tat Lee, Aaron Sidford
- 2013 IEEE 54th Annual Symposium on Foundations of…
- 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, we show how to generalize and efficiently implement a method proposed by Nesterov, giving faster asymptotic running times for various algorithms that use standard… (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 flow and maximum concurrent multicommodity flow problems. For graphs with n vertices and m edges, it allows us to find an ε-approximate maximum s-t flow in time… (More)

- Yin Tat Lee, Aaron Sidford
- 2014 IEEE 55th Annual Symposium on Foundations of…
- 2014

In this paper, we present a new algorithm for '/ solving linear programs that requires only Õ(√rank(A)L) iterations where A is the constraint matrix of a linear program with m constraints, n variables, and bit complexity L. Each iteration of our method consists of solving Õ(1) linear systems and additional nearly linear time… (More)

- Yin Tat Lee, Aaron Sidford, Sam Chiu-wai Wong
- 2015 IEEE 56th Annual Symposium on Foundations of…
- 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 ⊂ R<sup>n</sup> that is contained in a box of radius R we show how to either compute a point in K or prove that K does not contain a ball of radius ϵ using an expected O(n… (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 data matrix, improving processing time significantly. For theoretical performance guarantees, each row must be sampled with probability proportional to its… (More)

- Sébastien Bubeck, Yin Tat Lee, Mohit Singh
- ArXiv
- 2015

We propose a new method for unconstrained optimization of a smooth and strongly convex function, which attains the optimal rate of convergence of Nesterov’s accelerated gradient descent. The new algorithm has a simple geometric interpretation, loosely inspired by the ellipsoid method. We provide some numerical evidence that the new method can be superior to… (More)

- Mikhail Kapralov, Yin Tat Lee, Cameron Musco, Christopher Musco, Aaron Sidford
- 2014 IEEE 55th Annual Symposium on Foundations of…
- 2014

We present the first single pass algorithm for computing spectral sparsifiers of graphs in the dynamic semi-streaming model. Given a single pass over a stream containing insertions and deletions of edges to a graph, G, our algorithm maintains a randomized linear sketch of the incidence matrix into dimension O(1/∈<sup>2n</sup>polylog(n)). Using this… (More)

Let φ(G) be the minimum conductance of an undirected graph G, and let 0=λ<sub>1</sub> ≤ λ<sub>2</sub> ≤ ... ≤ λ<sub>n</sub> ≤ 2 be the eigenvalues of the normalized Laplacian matrix of G. We prove that for any graph G and any k ≥ 2, [φ(G) = O(k) l<sub>2</sub>/√l<sub>k</sub>,] and this performance… (More)

- Yin Tat Lee, Satish Rao, Nikhil Srivastava
- STOC
- 2013

We give an algorithm which computes a (1-ε)-approximately maximum st-flow in an undirected uncapacitated graph in time O(1/ε√m/F⋅ m log<sup>2</sup> n) where <i>F</i> is the flow value. By trading this off against the Karger-Levine algorithm for undirected graphs which takes ~O(m+nF) time, we obtain a running time of ~O(m… (More)

- Yin Tat Lee, Aaron Sidford
- 2013

In this paper we present an Õ(m √ n log U) time algorithm for solving the maximum flow problem on directed graphs with m edges, n vertices, and capacity ratio U . This improves upon the previous fastest running time of O(mmin ( n,m ) log ( n/m ) logU) achieved over 15 years ago by Goldberg and Rao [8] and improves upon the previous best running times for… (More)