Giuseppe Carlo Calafiore

Full Professor (Professore Ordinario)
Dipartimento di Automatica e Informatica (DAUIN)
Politecnico di Torino
Corso Duca degli Abruzzi, 24 - 10129 Torino, Italy
Giuseppe Carlo Calafiore

Full Professor (Professore Ordinario)
Dipartimento di Automatica e Informatica (DAUIN)
Politecnico di Torino
Corso Duca degli Abruzzi, 24 - 10129 Torino, Italy
Publications and CV
NEW BOOK ANNOUNCEMENT!


A SELECTION OF MY RECENT PAPERS
(please email me for preprints of other papers not available here. See here for the full publication list)
---
L. Carlone, V. Srivastava, F. Bullo, G.C. Calafiore, ``Distributed Random Convex Programming via Constraints Consensus,’’ SIAM J. on Control and Optimization, SIAM J. on Control and Optimization, vol. 52, n. 1, pp. 629-662, 2014.
Z. Mahmood, S. Grivet-Talocia, A. Chinea, G.C. Calafiore, L. Daniel, “Efficient Localization Methods for Passivity Enforcement of Linear Dynamical Models,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 33, n. 9, pp. 1-14, 2014.
A. Favetto, S. Appendino, A. Battezzato, F. Chen Chen, M. Mousavi, F. Pescarmona, G.C. Calafiore, ``Analysis and Optimization of a Wire Actuated, single effect n-R Robotic Structure,'' Robotica, pp. 1-17, ISSN 0263-5747, 2013.
G.C. Calafiore, L. Fagiano, ``Stochastic Model Predictive Control of LPV Systems via Scenario Optimization,'' Automatica, vol. 49, n. 6, p. 1861-1866, 2013.
G.C. Calafiore, ``Direct Data-Driven Portfolio Optimization with Guaranteed Shortfall Probability,'' Automatica, vol. 49, p. 370-380, 2013.
G.C. Calafiore, L. Fagiano, ``Robust Model Predictive Control via Scenario Optimization,'' IEEE Transactions on Automatic Control, vol. 58, n. 1, p. 219-224, 2013.
G. Calafiore, L. Carlone, M. Wei, ``A Distributed Technique for Localization of Agent Formations from Relative Range Measurements,'' IEEE Transactions on Systems Man and Cybernetics, Part A - Systems and Humans, vol. 42, p. 1065-1076, 2012.
R. Aragues, L. Carlone, C. Sagues, and G. Calafiore, ``Distributed Centroid Estimation from Noisy Relative Measurements,'' Systems and Control Letters, vol. 61, p. 773-779, 2012.
G.C. Calafiore, S. Grivet-Talocia, A. Chinea, ``Subgradient Techniques for Passivity Enforcement of Linear Device and Interconnect Macromodels,” IEEE Transactions on Microwave Theory and Techniques, vol. 60, p. 2990-3003, 2012.
G.C. Calafiore, F. Dabbene and R. Tempo, ``Research on probabilistic methods for control system design,'' Automatica, vol. 47; p. 1279-1293, 2011.
G.C. Calafiore, ``Random Convex Programs,'' SIAM Journal on Optimization, vol. 20, n. 6, pp. 3427-3464, 2010.
G.C. Calafiore, ``An Affine Control Method for Optimal Dynamic Asset Allocation with Transaction Costs,'' SIAM Journal of Control and Optimization, vol. 48, n. 4, p. 2254-2274, 2009.
G.C. Calafiore, ``Multi-Period Portfolio Optimization with Linear Control Policies,'' Automatica, vol 44, n. 10, p. 2463-2473, 2008.
G.C. Calafiore and M.C. Campi, ``The Scenario Approach to Robust Control Design,'' IEEE Transactions on Automatic Control, vol. 51, n. 5, p. 742-753, May 2006. Winner of the 2008 IEEE Axelby Award.
OTHER BOOKS
G.C. Calafiore & L. El Ghaoui
Cambridge Univ. Press, 2014.
660 pages. ISBN: 9781107050877
Emphasizing practical understanding over the technicalities of specific algorithms, this elegant textbook is an accessible introduction to the field of optimization, focusing on powerful and reliable convex optimization techniques. Students and practitioners will learn how to recognize, simplify, model and solve optimization problems - and apply these principles to their own projects. A clear and self-contained introduction to linear algebra demonstrates core mathematical concepts in a way that is easy to follow, and helps students to understand their practical relevance. Requiring only a basic understanding of geometry, calculus, probability and statistics, and striking a careful balance between accessibility and rigor, it enables students to quickly understand the material, without being overwhelmed by complex mathematics. Accompanied by numerous end-of-chapter problems, an online solutions manual for instructors, and relevant examples from diverse fields including engineering, data science, economics, finance, and management, this is the perfect introduction to optimization for undergraduate and graduate students.
Order on Amazon (USA): click here.
Order on Amazon (IT): click here.