Research team debuts the first deterministic streaming algorithms for non-monotone submodular maximization, delivering superior approximation ratios with minimal memory and real-time throughput on ...

Abstract: Training a one-node neural network with the ReLU activation function via optimization, which we refer to as the ON-ReLU problem, is a fundamental problem in machine learning. In this paper, ...

Submission for the CS421 project "Approximation algorithms for geometric problems". We first compute a Well-Separated-Pair-Decomposition from an octree, and then apply this representation to ...

Stochastic approximation algorithms are used to approximate solutions to fixed point equations that involve expectations of functions with respect to possibly unknown distributions. Among many ...

Abstract: Multiprocessor task scheduling problem has become increasingly interesting, for both theoretical study and practical applications. Theoretical study of the problem has made significant ...

The travelling salesman problem (TSP) remains one of the most challenging NP‐hard problems in combinatorial optimisation, with significant implications for logistics, network design and route planning ...

This course studies approximation algorithms – algorithms that are used for solving hard optimization problems. Such algorithms find approximate (slightly suboptimal) solutions to optimization ...

The rectilinear traveling salesperson problem (RTSP) Remember that a coordinate is a number x∈ℜ, and in the plane, a point is a pair (x, y)∈ℜ2.