Primal Heuristics in Integer Programming

‘Primal Heuristics in Integer Programming by Timo Berthold, Andrea Lodi, and Domenico Salvagnin is a groundbreaking work that offers deep insights and practical approaches to design sound heuristics for integer programming. This book will be an invaluable resource for both researchers and practitioners in the field. I will highly recommend the book to anyone looking to deepen their understanding of this crucial area.’ Matteo Fischetti, University of Padua, Italy‘Primal Heuristics in Integer Programming is a singular work in the area of computational integer optimization. It aggregates many of the most important techniques that state-of-the-art solvers use for actually producing high-quality solutions to large-scale and/or difficult mixed-integer linear optimization problems. This book fills a big gap left by the many textbooks on mixed-integer linear optimization problems. Certainly it should be on the shelf of any student, researcher or practitioner who wants a complete picture of how such solvers work.’ Jon Lee, University of Michigan

Timo Berthold is Lecturer at TU Berlin and a Director at FICO, leading the MIP research and development team of the FICO Xpress Solver. He is an expert on heuristic methods and computational mixed-integer linear and nonlinear programming. He has won multiple awards for his research.

Andrea Lodi is Andrew H. and Ann R. Tisch Professor at the Jacobs Technion-Cornell Institute at Cornell Tech and the Technion - ITT. His main research interests are in mixed-integer linear and nonlinear programming and data science. He has been recognized by IBM and Google faculty awards, and the INFORMS Optimization Society 2021 Farkas Prize. He is a 2023 INFORMS Fellow.

Domenico Salvagnin is Associate Professor in Operations Research at the University of Padua, Italy. He was lead development scientist in the IBM CPLEX team in 2015–2017 and is currently scientific consultant for FICO Xpress. His research interests include computational integer programming, constraint programming and hybrid methods for optimization.