Srinivasa Rao Arikati

Planar Spanners and Approximate Shortest Path Queries among Obstacles in the Plane

We consider the problem of finding an obstacle-avoiding path between two points s and t in the plane, amidst a set of disjoint polygonal obstacles with a total of n vertices. The length of this path should be within a small constant factor c of the length of the shortest possible obstacle-avoiding s-t path measured in the L p -metric. Such an approximate shortest path is called a c-short path, or a short path with stretch factor c. The goal is to preprocess the obstacle-scattered plane by creating an efficient data structure that enables fast reporting of a c-short path (or its length). In this paper, we give a family of algorithms for the above problem that achieve an interesting trade-off between the stretch factor, the query time and the preprocessing bounds. Our main results are algorithms that achieve logarithmic length query time, after subquadratic time and space preprocessing.

A signature based approach to regularity extraction

Regularity extraction is an important step in the design flow of datapath-dominated circuits. This paper outlines a new method that automatically extracts regular structures from the netlist. The method is general enough to handle two types of designs: designs with structured cluster information for a portion of the datapath components that are identified at the HDL level; and designs with no such structured cluster information. The method analyzes the circuit connectivity and uses signature based approaches to recognize regularity.

Efficient computation of implicit representations of sparse graphs

Abstract The problem of finding an implicit representation for a graph such that vertex adjacency can be tested quickly is fundamental to all graph algorithms. In particular, it is possible to represent sparse graphs on n vertices using O(n) space such that vertex adjacency is tested in O(l) time. We show here how to construct such a representation efficiently by providing simple and optimal algorithms, both in a sequential and a parallel setting. Our sequential algorithm runs in O(n) time. The parallel algorithm runs in O(log n) time using O(n/log n) CRCW PRAM processors, or in O (log n log ∗ n) time using O (n/log n log ∗ n) EREW PRAM processors. Previous results for this problem are based on matroid partitioning and thus have a high complexity.

Realizing Degree Sequences in Parallel

A sequence

Approximation Algorithms for Maximum Two-Dimensional Pattern Matching

d

All-Pairs Min-Cut in Sparse Networks

of integers is a degree sequence if there exists a (simple) graph

An O(n) Algorithm for Realizing Degree Sequences

G

Approximation algorithms for maximum two-dimensional pattern matching

such that the components of

Realizing degree sequences in parallel

d

Efficient computation of implicit representations of sparse graphs (revised version)

are equal to the degrees of the vertices of