With this grant, Dadush aims to revolutionize the understanding of integer programming (IP), the most popular method used today for finding optimal solutions to real-world optimization problems. Such problems include finding the most efficient way to schedule a train timetable, optimize an assembly line, or to ship goods to customers from an astronomically large set of alternatives.

The future results are expected help modern IP solvers – such as CPLEX, Gurobi and SCIP, currently used ubiquitously in industry – improve their practical performance.

"Daniel continues to conduct groundbreaking research on fundamental problems in algorithms and optimization," said Santosh Vampala, Dadush's dissertation advisor and Frederick G. Storey Chair in Tech's College of Computing. "He won the 2015 Tucker Prize (best thesis in optimization over a three-year period) for contributions to integer programming, lattice algorithms, and deterministic volume computation. Since then, he has discovered new properties of lattices and applied them to derive improved algorithms, most recently to the classical discrepancy minimization problem, in a very general setting. He has also discovered (together with his Ph.D. student) a new, improved, and insightful analysis of the shadow-vertex simplex method, a cornerstone of smoothed analysis."

Dadush is currently a tenure-track researcher in the CWI Networks and Optimization research group. To read more about the research that will be funded by the ERC grant, click here: http://bit.ly/2NRjNyQ.