Randomized rounding algorithms for large scale unsplittable flow problems

Improved Approximation Algorithms for Unsplittable Flow Problems

In the single-source unsplittable ow problem we are given a graph G; a source vertex s and a set of sinks t 1 ; : : :; t k with associated demands. We seek a single s-t i ow path for each commodity i so that the demands are satissed and the total ow routed across any edge e is bounded by its capacity c e : The problem is an NP-hard variant of max ow and a generalization of single-source edge-di...

Randomized Algorithms for Solving Large Scale Nonlinear Least Squares Problems

This thesis presents key contributions towards devising highly efficient stochastic reconstruction algorithms for solving large scale inverse problems, where a large data set is available and the underlying physical systems is complex, e.g., modeled by partial differential equations (PDEs). We begin by developing stochastic and deterministic dimensionality reduction methods to transform the ori...

Approximation Algorithms for Single-Source Unsplittable Flow

Rounding algorithms for covering problems

In the last 25 years approximation algorithms for discrete optimization problems have been in the center of research in the fields of mathematical programming and computer science. Recent results from computer science have identified barriers to the degree of approximability of discrete optimization problems unless P -NP. As a result, as far as negative results are concerned a unifying picture ...

Randomized Algorithms for Large scale SVMs

We propose a randomized algorithm for training Support vector machines(SVMs) on large datasets. By using ideas from Random projections we show that the combinatorial dimension of SVMs is O(log n) with high probability. This estimate of combinatorial dimension is used to derive an iterative algorithm, called RandSVM, which at each step calls an existing solver to train SVMs on a randomly chosen ...