Date & Time:
February 12, 2019 3:30 pm – 4:30 pm
Location:
Ryerson 251, 1100 E. 58th St., Chicago, IL,
02/12/2019 03:30 PM 02/12/2019 04:30 PM America/Chicago Sushant Sachdeva (Toronto) – Iterative Refinement for p-norms Ryerson 251, 1100 E. 58th St., Chicago, IL,

Iterative Refinement for p-norms

In the current optimization literature, for several convex programs, it is significantly faster to obtain O(1)-approximate solutions as compared to high accuracy (1+1/poly(n))-approximate solutions. This gap is reflected in the differences between interior point methods vs. (accelerated) gradient descent for regression problems, and between exact vs. approximate undirected max-flow. One exception has been L2-regression, where an algorithm for computing an approximate solution can be converted into a high-accuracy solution via iterative refinement. In this talk, we will present generalizations of iterative refinement to p-norms. This leads to algorithms that produce high accuracy solutions by crudely solving only a polylogarithmic number of residual problems. I will also discuss several results that build on this new approach to high-accuracy algorithms, including p-norm regression using m^{1/3} linear system solves, and p-norm flow in undirected unweighted graphs in almost-linear time.

This talk will be based on joint works with Deeksha Adil, Richard Peng, Rasmus Kyng, and Di Wang.

Host: Madhur Tulsiani

Sushant Sachdeva

Assistant Professor, University of Toronto

Research Interests

Algorithms, and its connections to optimization, machine learning, and statistics.

Focus areas: Design of fast algorithms, particularly for graph problems; Approximation algorithms; Numerical Linear Algebra; Hardness of approximation

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