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ORCIDGiovanni Fasano
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2020 – today
- 2024
[j25]Andrea Caliciotti
, Marco Corazza, Giovanni Fasano:
From regression models to machine learning approaches for long term Bitcoin price forecast. Ann. Oper. Res. 336(1-2): 359-381 (2024)- 2021
- [j24]Marco Corazza, Giacomo di Tollo, Giovanni Fasano, Raffaele Pesenti:
A novel hybrid PSO-based metaheuristic for costly portfolio selection problems. Ann. Oper. Res. 304(1): 109-137 (2021) - [j23]Cecilia Leotardi, Andrea Serani, Matteo Diez, Emilio Fortunato Campana, Giovanni Fasano, Riccardo Gusso:
Dense conjugate initialization for deterministic PSO in applications: ORTHOinit+. Appl. Soft Comput. 104: 107121 (2021) - [j22]Giovanni Fasano, Raffaele Pesenti:
Polarity and conjugacy for quadratic hypersurfaces: A unified framework with recent advances. J. Comput. Appl. Math. 390: 113248 (2021) - 2020
- [j21]Andrea Caliciotti, Giovanni Fasano, Florian A. Potra, Massimo Roma:
Issues on the use of a modified Bunch and Kaufman decomposition for large scale Newton's equation. Comput. Optim. Appl. 77(3): 627-651 (2020) - [j20]Renato De Leone, Giovanni Fasano, Massimo Roma, Yaroslav D. Sergeyev:
Iterative Grossone-Based Computation of Negative Curvature Directions in Large-Scale Optimization. J. Optim. Theory Appl. 186(2): 554-589 (2020) - [j19]Mehiddin Al-Baali, Andrea Caliciotti, Giovanni Fasano, Massimo Roma:
A Class of Approximate Inverse Preconditioners Based on Krylov-Subspace Methods for Large-Scale Nonconvex Optimization. SIAM J. Optim. 30(3): 1954-1979 (2020) - [p2]Marco Corazza, Giacomo di Tollo, Giovanni Fasano, Raffaele Pesenti:
A PSO-Based Framework for Nonsmooth Portfolio Selection Problems. Neural Advances in Processing Nonlinear Dynamic Signals 2020: 265-275
2010 – 2019
- 2018
- [j18]Andrea Caliciotti, Giovanni Fasano, Massimo Roma:
Preconditioned Nonlinear Conjugate Gradient methods based on a modified secant equation. Appl. Math. Comput. 318: 196-214 (2018) - [j17]Renato De Leone, Giovanni Fasano, Yaroslav D. Sergeyev:
Planar methods and grossone for the Conjugate Gradient breakdown in nonlinear programming. Comput. Optim. Appl. 71(1): 73-93 (2018) - [j16]Andrea Caliciotti, Giovanni Fasano, Stephen G. Nash, Massimo Roma:
An adaptive truncation criterion, for linesearch-based truncated Newton methods in large scale nonconvex optimization. Oper. Res. Lett. 46(1): 7-12 (2018) - [c4]Renato De Leone, Giovanni Fasano, Massimo Roma, Yaroslav D. Sergeyev:
How Grossone Can Be Helpful to Iteratively Compute Negative Curvature Directions. LION 2018: 180-183 - 2017
- [j15]Giovanni Fasano, Raffaele Pesenti:
Conjugate Direction Methods and Polarity for Quadratic Hypersurfaces. J. Optim. Theory Appl. 175(3): 764-794 (2017) - [j14]Mehiddin Al-Baali, Andrea Caliciotti, Giovanni Fasano, Massimo Roma:
Exploiting damped techniques for nonlinear conjugate gradient methods. Math. Methods Oper. Res. 86(3): 501-522 (2017) - [j13]Andrea Caliciotti, Giovanni Fasano, Massimo Roma:
Novel preconditioners based on quasi-Newton updates for nonlinear conjugate gradient methods. Optim. Lett. 11(4): 835-853 (2017) - 2016
- [j12]Andrea Serani, Cecilia Leotardi, Umberto Iemma, Emilio Fortunato Campana, Giovanni Fasano, Matteo Diez:
Parameter selection in synchronous and asynchronous deterministic particle swarm optimization for ship hydrodynamics problems. Appl. Soft Comput. 49: 313-334 (2016) - [j11]Giovanni Fasano, Massimo Roma:
A novel class of approximate inverse preconditioners for large positive definite linear systems in optimization. Comput. Optim. Appl. 65(2): 399-429 (2016) - [c3]Matteo Diez, Andrea Serani, Cecilia Leotardi, Emilio Fortunato Campana, Giovanni Fasano, Riccardo Gusso:
Dense Orthogonal Initialization for Deterministic PSO: ORTHOinit+. ICSI (1) 2016: 322-330 - 2015
- [j10]Giovanni Fasano:
A Framework of Conjugate Direction Methods for Symmetric Linear Systems in Optimization. J. Optim. Theory Appl. 164(3): 883-914 (2015) - [p1]Andrea Serani, Matteo Diez, Emilio Fortunato Campana, Giovanni Fasano, Daniele Peri, Umberto Iemma:
Globally Convergent Hybridization of Particle Swarm Optimization Using Line Search-Based Derivative-Free Techniques. Recent Advances in Swarm Intelligence and Evolutionary Computation 2015: 25-47 - 2014
- [j9]Giovanni Fasano, Giampaolo Liuzzi, Stefano Lucidi, Francesco Rinaldi:
A Linesearch-Based Derivative-Free Approach for Nonsmooth Constrained Optimization. SIAM J. Optim. 24(3): 959-992 (2014) - [c2]Matteo Diez, Andrea Serani, Cecilia Leotardi, Emilio Fortunato Campana, Daniele Peri, Umberto Iemma, Giovanni Fasano, Silvio Giove:
A Proposal of PSO Particles' Initialization for Costly Unconstrained Optimization Problems: ORTHOinit. ICSI (1) 2014: 126-133 - 2013
- [j8]Marco Corazza, Giovanni Fasano, Riccardo Gusso:
Particle Swarm Optimization with non-smooth penalty reformulation, for a complex portfolio selection problem. Appl. Math. Comput. 224: 611-624 (2013) - [j7]Giovanni Fasano, Massimo Roma:
Preconditioning Newton-Krylov methods in nonconvex large scale optimization. Comput. Optim. Appl. 56(2): 253-290 (2013) - [c1]Emilio Fortunato Campana, Matteo Diez, Giovanni Fasano, Daniele Peri:
Initial Particles Position for PSO, in Bound Constrained Optimization. ICSI (1) 2013: 112-119 - 2010
- [j6]Emilio Fortunato Campana, Giovanni Fasano, Antonio Pinto:
Dynamic analysis for the selection of parameters and initial population, in particle swarm optimization. J. Glob. Optim. 48(3): 347-397 (2010)
2000 – 2009
- 2009
- [j5]Giovanni Fasano, Stefano Lucidi:
A nonmonotone truncated Newton-Krylov method exploiting negative curvature directions, for large scale unconstrained optimization. Optim. Lett. 3(4): 521-535 (2009) - [j4]Giovanni Fasano, José Luis Morales, Jorge Nocedal:
On the geometry phase in model-based algorithms for derivative-free optimization. Optim. Methods Softw. 24(1): 145-154 (2009) - 2007
- [j3]Giovanni Fasano, Massimo Roma:
Iterative computation of negative curvature directions in large scale optimization. Comput. Optim. Appl. 38(1): 81-104 (2007) - 2006
- [j2]Giovanni Fasano, Francesco Lampariello, Marco Sciandrone:
A Truncated Nonmonotone Gauss-Newton Method for Large-Scale Nonlinear Least-Squares Problems. Comput. Optim. Appl. 34(3): 343-358 (2006) - 2004
- [j1]Giovanni Fasano:
Conjugate gradient (CG)-type method for the solution of Newton's equation within optimization frameworks. Optim. Methods Softw. 19(3-4): 267-290 (2004)
Coauthor Index
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