Some of the currently best-known approximation algorithms for network design are based on random sampling. One of the key steps of such algorithms is connecting a set of source nod...
In this paper we discuss the application of circuit-based logical reasoning to simplify optimization problems expressed as integer linear programs (ILP) over circuit states. We de...
Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in control theory, machine learning, and discrete geometry. This c...
A powerful class of rate-compatible serially concatenated convolutional codes (SCCCs) has been proposed based on minimizing analytical upper bounds on the error probability in the ...
We present a probabilistic analysis for a large class of combinatorial optimization problems containing, e.g., all binary optimization problems defined by linear constraints and a...