E-LIB Suche - Ergebnisse für: Caliciotti, Andrea

1

From regression models to machine learning approaches for l..:

Caliciotti, Andrea ; Corazza, Marco ; Fasano, Giovanni
Annals of Operations Research. 336 (2023) 1-2 - p. 359-381 , 2023

Link: https://doi.org/10.1007/..

2

On Predicting Ticket Reopening for Improving Customer Servi..:

Ricciardi Celsi, Lorenzo ; Caliciotti, Andrea ; D'Onorio, Matteo...
Future Internet. 13 (2021) 10 - p. 259 , 2021

Link: https://doi.org/10.3390/..

3

Issues on the use of a modified Bunch and Kaufman decomposi..:

Caliciotti, Andrea ; Fasano, Giovanni ; Potra, Florian.
Computational Optimization and Applications. 77 (2020) 3 - p. 627-651 , 2020

Link: https://doi.org/10.1007/..

4

On Optimal Buffer Allocation for Guaranteeing Quality of Se..:

Caliciotti, Andrea ; Celsi, Lorenzo Ricciardi
International Journal of Control, Automation and Systems. 18 (2020) 12 - p. 3043-3050 , 2020

Link: https://doi.org/10.1007/..

5

A Class of Approximate Inverse Preconditioners Based on Kry..:

Al-Baali, Mehiddin ; Caliciotti, Andrea ; Fasano, Giovanni.
SIAM Journal on Optimization. 30 (2020) 3 - p. 1954-1979 , 2020

Link: https://doi.org/10.1137/..

6

An adaptive truncation criterion, for linesearch-based trun..:

Caliciotti, Andrea ; Fasano, Giovanni ; Nash, Stephen G..
Operations Research Letters. 46 (2018) 1 - p. 7-12 , 2018

Link: https://doi.org/10.1016/..

7

Preconditioned Nonlinear Conjugate Gradient methods based o..:

Caliciotti, Andrea ; Fasano, Giovanni ; Roma, Massimo
Applied Mathematics and Computation. 318 (2018) - p. 196-214 , 2018

Link: https://doi.org/10.1016/..

8

Data and performance profiles applying an adaptive truncati..:

Caliciotti, Andrea ; Fasano, Giovanni ; Nash, Stephen G..
Data in Brief. 17 (2018) - p. 246-255 , 2018

Link: https://doi.org/10.1016/..

9

Exploiting damped techniques for nonlinear conjugate gradie..:

Al-Baali, Mehiddin ; Caliciotti, Andrea ; Fasano, Giovanni.
Mathematical Methods of Operations Research. 86 (2017) 3 - p. 501-522 , 2017

Link: https://doi.org/10.1007/..

10

Novel preconditioners based on quasi–Newton updates for non..:

Andrea, Caliciotti ; Giovanni, Fasano ; Massimo, Roma
Optimization Letters. 11 (2016) 4 - p. 835-853 , 2016

Link: https://doi.org/10.1007/..

11

From regression models to Machine Learning approaches for l..:

Caliciotti Andrea ; Corazza Marco ; Fasano Giovanni
info:eu-repo/semantics/altIdentifier/wos/WOS:001021378700001. , 2023

Link: https://hdl.handle.net/1..

12

On Predicting Ticket Reopening for Improving Customer Servi..:

Ricciardi Celsi, Lorenzo ; Caliciotti, Andrea ; D'Onorio, Matteo...
info:eu-repo/semantics/altIdentifier/wos/WOS:000715143800001. , 2021

Link: http://hdl.handle.net/11..

13

A Class of Approximate Inverse Preconditioners Based on Kry..:

Al-Baali, Mehiddin ; Caliciotti, Andrea ; Fasano, Giovanni.
info:eu-repo/semantics/altIdentifier/wos/WOS:000576467300008. , 2020

Link: http://hdl.handle.net/11..

14

Issues on the use of a modified Bunch and Kaufman decomposi..:

Caliciotti, Andrea ; Fasano, Giovanni ; Potra, Florian.
info:eu-repo/semantics/altIdentifier/wos/WOS:000570452300001. , 2020

Link: http://hdl.handle.net/11..

15

Preconditioned nonlinear conjugate gradient methods based o..:

CALICIOTTI, ANDREA ; Fasano, Giovanni ; ROMA, Massimo
info:eu-repo/semantics/altIdentifier/wos/WOS:000415905500014. , 2018

Link: http://hdl.handle.net/11..