Journal Papers:

[J46] V. Piccialli, D. Romero Morales, C. Salvatore (2023).  Supervised feature compression based on counterfactual analysis,

European Journal of Operational Research, https://doi.org/10.1016/j.ejor.2023.11.019. open access.

[J45] V. Piccialli, A. Sudoso (2023).Global optimization for cardinality-constrained minimum sum-of-squares clustering via semidefinite programming. Mathematical Programming, https://doi.org/10.1007/s10107-023-02021-8 open access.

[J44] B. Addis, C. Castel, A. Macali, R. Misener, V. Piccialli (2023). Data augmentation driven by optimization for membrane separation process synthesis, Computers & Chemical Engineering, 177, 108342, https://doi.org/10.1016/j.compchemeng.2023.108342.

[J43] F. Maita, V. Piccialli, F. Pensa, M. Scatto, M Ruggeri, L. Maiolo (2022). Application of Unconditioned Nanostructured Thermoplastic-Based Strain Gauge Sensor in Wearable Electronics, IEEE Sensors Journal, Vol 22(24), pp 24019 - 24026.

[J42] V. Piccialli, A. Russo Russo, A.M. Sudoso (2022). An exact algorithm for semi-supervised minimum sum-of-squares clustering, Computers and Operations Research vol. 147,105958

[J41] V. Piccialli, A.M.. Sudoso, A. Wiegele (2022). SOS-SDP: An Exact Solver for Minimum Sum-of-Squares ClusteringINFORMS Journal on Computing, vol. 34(4), pp. 2144-2162

[J40] M. Balletti, V. Piccialli, A.M. Sudoso (2022). Mixed-Integer Nonlinear Programming for State-Based Non-Intrusive Load Monitoring. IEEE Transactions on Smart Grid, vol. 13(4), pp. 3301-3314

[J39] T. Neveux, B. Addis, C. Castel, V. Piccialli, E. Favre (2022). A comparison of process synthesis approaches for multistage separation processes by gas permeation, Computer Aided Chemical Engineering, vol. 51, pp. 685-690.

[J38] C. Salvatore, D. Valeriani, V. Piccialli, L. Bianchi (2022). Optimized Collaborative Brain-Computer Interfaces for Enhancing Face Recognition. IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.  30, pp. 1223-1232

[J37] L. Bianchi, C. Liti, G. Liuzzi, V. Piccialli, C. Salvatore (2022). Improving P300 Speller performance by means of optimization and machine learning, online first, Annals of Operations Research, vol. 312(2), pp. 1221-1259.

[J36] L. Galli, G. Galvan, T. Levato, C. Liti, V. Piccialli, M. Sciandrone (2021).  Football: Discovering elapsing-time bias in the science of success,Chaos, Solitons & Fractals,

vol. 152, https://doi.org/10.1016/j.chaos.2021.111370.

[J35] G. Liuzzi, M. Locatelli, V. Piccialli, S. Rass (2021). Computing mixed strategies equilibria in presence of switching costs by the solution of nonconvex QP problems

(2021) Computational Optimization and Applications, 79 (3), pp. 561-599,  https://doi.org/10.1007/s10589-021-00282-7.

[J34] P. Mancuso, V. Piccialli, A. M. Sudoso. A machine learning approach for forecasting hierarchical time series, Expert Systems with Applications, vol.182, 2021,115102, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2021.115102.

[J34] V. Piccialli, A.M. Sudoso (2021). Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network, Energies 2021, vol. 14(4), https://doi.org/10.3390/en14040847.

[J32] M, Bozorg, A. Ramìrez-Santos, B. Addis, V. Piccialli, C. Castel, E. Favre (2020). Optimal process design of biogas upgrading membrane systems: Polymeric vs high performance inorganic membrane materials Chemical Engineering Science, vol. 225, https://doi.org/10.1016/j.ces.2020.115769

[J31] M. Bozorg, B. Addis, V. Piccialli, A. Ramìrez-Santos, C. Castel, I. Pinnau, E. Favre (2019). Polymeric membrane materials for nitrogen production from air: A process synthesis study, Chemical Engineering Science, vol. 207, pp. 1196-1213, https://doi.org/10.1016/j.ces.2019.07.029

[J30] M. Cosmi, G. Oriolo, V. Piccialli and P. Ventura (2019). Single Courier Single Restaurant Meal Delivery (Without Routing), Operations Research Letters

vol. 47(6), pp. 537-541, https://doi.org/10.1016/j.orl.2019.09.007

[J29] L. Bianchi,  C. Liti, and V. Piccialli (2019).  A new early stopping method for P300 spellers, IEEE Transactions on Neural Systems & Rehabilitation Engineering, vol. 27(8), pp. 1635--1643,  doi:10.1109/TNSRE.2019.2924080. 

[J28] G. Liuzzi, M. Locatelli, V. Piccialli (2019). A new branch-and-bound algorithm for standard quadratic programming problems, Optimization Methods and Software, vol. 34(1), pp. 79 - 97,  https://doi.org/10.1080/10556788.2017.1341504

[J27] S. Burer, and V. Piccialli (2019). Three Methods for robust grading, EJOR, vol. 272(1), 364--371, https://doi.org/10.1016/j.ejor.2018.06.019

[J26] A. Ramìrez-Santos, M. Bozorg, B. Addis, V. Piccialli, C. Castel, E. Favre (2018). Optimization of multistage membrane gas separation processes. Example of application to CO\textsubscript{2} capture from blast furnace gas, Journal of Membrane Science, vol. 566, pp 346-366, https://doi.org/10.1016/j.memsci.2018.08.024

[J25] V. Piccialli and M. Sciandrone (2018).  Nonlinear Optimization and Support Vector Machines,  4OR, vol. 16(2), pp 111--149, https://doi.org/10.1007/s10288-018-0378-2, shareable link: https://rdcu.be/cx4XC 

[J24] E.F. Campana, M. Diez, G. Liuzzi, S. Lucidi, R. Pellegrini, V. Piccialli, F. Rinaldi, A. Serani (2018). A Multi-objective DIRECT algorithm for ship hull optimization, Computational Optimization and Optimization,  vol. 71(1), pp. 53 - 72, https://doi.org/10.1007/s10589-017-9955-0, shareable link: https://rdcu.be/cx4XC

[J23]  G. Cocchi, A. Gallligari, F. Picca Nicolino, V. Piccialli, F. Schoen, M. Sciandrone (2018). Scheduling the Italian National Volley Tournament, Interfaces , vol. 48(3), pp. 271 - 284, https://doi.org/10.1287/inte.2017.0932

[J22] F. Fedeli, R. Mancini, C. Mannino, P. Ofria, G. Oriolo, A. Pacifici, and V. Piccialli (2017). Optimal design of a regional railway service in Italy, Journal  of Rail Transport Planning & Management, vol. 7(4), pp. 308-319, https://doi.org/10.1016/j.jrtpm.2017.10.001

[21] L. Bravi, V. Piccialli, M. Sciandrone (2017). An optimization-based method for feature ranking in nonlinear regression problems. IEEE Transactions on Neural Networks and Learning Systems, vol. 28(4), p. 1005 - 1010, DOI: 10.1109/TNNLS.2015.2504957

[20] G. Liuzzi, S. Lucidi, V. Piccialli (2016). Exploiting derivative-free local searches in DIRECT-type algorithms for global optimization. Computational Optimization and Applications,  vol. 65(2), pp. 449--4759, https://doi.org/10.1007/s10589-015-9741-9, shareable link: https://rdcu.be/cx4XL

[19] G. Di Pillo, G. Liuzzi, S. Lucidi, F. Rinaldi, V. Piccialli (2016). A DIRECT-type approach for derivative-free constrained global optimization, Computational Optimization and Applications, vol. 65(2), pp. 361--397, https://doi.org/10.1007/s10589-016-9876-3, shareable link: https://rdcu.be/cx4XR

[18] V. Cardellini, V. De Nitto Personè, V. Di Valerio, F. Facchinei, V. Grassi, V. Lo Presti, V. Piccialli (2016). A Game-theoretic approach to computation offloading in mobile cloud computing, Mathematical Programming, vol. 157 (2), p. 421-449, https://doi.org/10.1007/s10107-015-0881-6, shareable link: https://rdcu.be/cx4XS

[J17] L. Bianco, M. Caramia, S. Giordani, V. Piccialli (2016). A Game-theoretic Approach for Regulating Hazmat Transportation. Transportation Science, vol. 50 (2), p. 424-438, https://doi.org/10.1287/trsc.2015.0592

[J16] L. Grippo, L. Palagi, M. Piacentini, V. Piccialli, G. Rinaldi (2012). SpeeDP: An algorithm to compute SDP bounds for very large Max-Cut instances. Mathematical Programming, vol. 136, p. 353-373, https://doi.org/10.1007/s10107-012-0593-0, shareable link: https://rdcu.be/cx40x.

[J15] A.Y. Alfakih, M.F. Anjos, V. Piccialli, H. Wolkowicz (2011). Euclidean Distance Matrices, Semidefinite Programming, and Sensor Network Localization (a survey), Portugaliae Mathematica, vol. 68, n. 1, pp. 53-102, DOI: 10.4171/PM/1881

[J14] F. Facchinei, V. Piccialli, M. Sciandrone (2011). Decomposition Algorithms for Generalized Potential Games. Computational Optimization and Applications, vol.50, pp. 237-262, https://doi.org/10.1007/s10589-010-9331-9, shareable link: https://rdcu.be/cx40D

[J13] D. di Serafino, G. Liuzzi, V. Piccialli, F. Riccio , G. Toraldo (2011). A Modified DIviding RECTangles Algorithm for a Problem in Astrophysics. Journal of Optimization 

Theory and Applications, vol. 151, Number 1 (2011), 175-190, https://doi.org/10.1007/s10957-011-9856-9, shareable link: https://rdcu.be/cx40F.

[J12] L. Grippo, L. Palagi, V. Piccialli (2011). An unconstrained minimization method for solving low rank SDP relaxations of the

max cut problem, Mathematical Programming Series A, vol. 126, n. 1, pp. 119-146, https://doi.org/10.1007/s10107-009-0275-8, shareable link:  https://rdcu.be/cx40O.

[J11] G. Liuzzi, S. Lucidi, V. Piccialli (2010). A DIRECT-based approach for large-scale global optimization problems, Computational Optimization and Applications, vol. 45, n. 2, pp. 353-375, 10.1007/s10589-008-9217-2, shareable link: https://rdcu.be/cxTgi

[J10] G. Liuzzi, S. Lucidi, V. Piccialli (2010). A partition-based Global Optimization Algorithm, Journal of Global Optimization, vol.48, n. 1, pp. 113-128, https://doi.org/10.1007/s10898-009-9515-y,shareable link: https://rdcu.be/cxTjw.

[J9] E. Campana, G. Liuzzi, S. Lucidi, D. Peri, A. Pinto, V. Piccialli (2009). New Global Optimization Methods for Ship Design

Problems, Optimization and Engineering,  vol. 10, n.4, pp. 533-555, https://doi.org/10.1007/s11081-009-9085-3,shareable link: https://rdcu.be/cxTka

[J8] L. Grippo, L. Palagi, V. Piccialli (2009). Necessary and sufficient global optimality conditions for NLP reformulations of linear SDP problems, Journal of Global Optimization, vol. 44, n 3, pp. 339 - 348, https://doi.org/10.1007/s10898-008-9328-4, shareable link: https://rdcu.be/cxTmL

[J7] F. Facchinei, A. Fischer, V. Piccialli (2009). Generalized Nash Equilibrium Problems and Newton methods, Mathematical Programming Series B, Vol. 117, n. 1-2, pages 163-194, https://doi.org/10.1007/s10107-007-0160-2, shareable link: https://rdcu.be/cxTmN.

[J6] F. Facchinei, A. Fischer, V. Piccialli (2007). On Generalized Nash Games and Variational Inequalities, Operations Research Letters, vol. 35, n. 2, pp. 159-164, https://doi.org/10.1016/j.orl.2006.03.004

[J5] S. Lucidi, V. Piccialli, M. Sciandrone (2005). An Algorithm Model for Mixed Variable Programming. SIAM Journal on Optimization,  vol. 15, n. 4, pp. 1057 - 1084, https://doi.org/10.1137/S1052623403429573, 

[J4] G. Liuzzi, S. Lucidi, V. Piccialli, M. Villani (2005). Design of induction motors using a mixed-variable approach.  Computational Management Science, vol. 2, n. 3, pp.

213 - 228, https://doi.org/10.1007/s10287-005-0024-2, shareable link: https://rdcu.be/cxTnG

[J3] G.Liuzzi, S.Lucidi, V.Piccialli, A.Sotgiu (2004). A Magnetic Resonance device designed via global optimization techniques. 

Mathematical Programming, vol. 101, n. 2, pp. 339-364, https://doi.org/10.1007/s10107-004-0528-5, shareable link: https://rdcu.be/cxTnQ

[J2] S. Lucidi, V. Piccialli (2004). A derivative based algorithm for a particular class of mixed variable optimization problems.

Optimization Methods and Software, vol.19, n.3-4, pp. 371-387, https://doi.org/10.1080/10556780410001654197

[J1] S. Lucidi, V. Piccialli (2002). New Classes of Globally Convexized Filled Functions for Global Optimization. Journal

of Global Optimization, vol. 24, pp. 219-236, https://doi.org/10.1023/A:1020243720794, shareable link: https://rdcu.be/cxToy

Submitted papers:

[S4] Piccialli, V., & Sudoso, A. M. & Schwiddessen J. (2023). Optimization meets Machine Learning: An Exact Algorithm for Semi-Supervised Support Vector Machines. arXiv preprint  arXiv:2312.09789. Submitted to Mathematical Programming.

[S3] Piermarini, D., Sudoso, A. M., & Piccialli, V. (2023). Predicting municipalities in financial distress: a machine learning approach enhanced by domain expertise. arXiv preprint arXiv:2302.05780. Submitted to Soft Computing

[S2] Locatelli, M., Piccialli, V., & Sudoso, A. M. (2022). Fix and Bound: An efficient approach for solving large-scale quadratic programming problems with box constraints. arXiv preprint arXiv:2211.08911. SSubmitted to Mathematical Programming Computation.

[S1] M. Cosmi, G. Oriolo, V. Piccialli, P. Ventura (2023). Efficient Courier Assignment in Meal Delivery via Integer Programming, Optimization Online, Submitted.

Book chapters:

[C3] G. Liuzzi, S.Lucidi, V. Piccialli (2015). Global optimization of simulation based complex systems. In G. Dellino, C. Meloni, (Eds.) Uncertainty Management in

Simulation-Optimization of Complex Systems: Algorithms And Applications, Operations Research/Computer Science Interfaces Series, Springer, Volume 59, 2015, p. 173-202, doi: 10.1007/978-1-4899-7547-8\_8.

[C2] L. Bianco, M. Caramia, S. Giordani and V. Piccialli (2013). Operations research models for global route planning in hazardous material transportation. In Rajan Batta, Changhyun Kwon (Eds.), Handbook of OR/MS Models in Hazardous Materials Transportation, p. 49-101, Springer, doi: 10.1007/978-1-4614-6794-6\_3.

[C1] L. Palagi, V. Piccialli, F. Rendl, G. Rinaldi, and A. Wiegele (2012). Computational Approaches to Max-Cut. In M.F. Anjos and J.B. Lasserre (Eds.), Handbook of Semidefinite, Cone and Polynomial Optimization: Theory, Algorithms, Software and Applications, International Series in Operations Research and Management Science, 166, pp. 821-847. Springer, New York, doi: 10.1007/978-1-4614-0769-0\_28.