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DTSTART:20171029T030000
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DTSTART:20170326T020000
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UID:calendar.12417.field_data.0@www.glad.uniroma1.it
DTSTAMP:20260403T174200Z
CREATED:20170628T105104Z
DESCRIPTION:Titolo: 'A Dual Step for Improving Alternating Augmented Lagran
 gian Methods for Semidefinite Programming'Speaker:  Marianna De Santis(Joi
 nt work with Franz Rendl e Angelika Wiegele)Abstract:'It is well known tha
 t SDP problems are solvable in polynomial time by interior point methods (
 IPMs). However\, if the number of constraints m in an SDP is of order O(n^
 2)\, when the unknown positive semidefinite matrix is n × n\, interior poi
 nt methods become impractical both in terms of the time and the amount of 
 memory required at each iteration. As a matter of fact\, in order to compu
 te the search direction\, IPMs need to form the m × m positive definite Sc
 hur complement matrix M and find its Cholesky factorization. On the other 
 hand\, first-order methods typically require much less computation effort 
 per iteration\, as they do not form or factorize these large dense matrice
 s.Furthermore\, some first-order methods are able to take advantage of pro
 blem structure such as sparsity. Hence\, they are often more suitable\, an
 d sometimes the only practical choice for solving large-scale SDPs.Most ex
 isting first-order methods for SDP are based on the augmented Lagrangian m
 ethod. Alternating direction augmented Lagrangian (ADAL) methods usually p
 erform a projection onto the cone of semidefinite matrices at each iterati
 on.With the aim of improving the convergence rate of ADAL methods\, we pro
 pose to update the dual variables before the projection step. Numerical re
 sults are shown on both random instances and on instances for the computat
 ion of the Lovàsz theta number\, giving some insights on the benefits of t
 he approach.'  Titolo: 'A heuristic method to solve the challenging Sales 
 Based Integer Program for Network Airlines Revenue Management'Speaker: Gio
 rgio Grani(Joint work with Gianmaria Leo\, Laura Palagi\, Mauro Piacentini
 \, Hunkar Toyoglu)Abstract:'Revenue Management (RM) has been playing over 
 recent years an increasingly crucial role in both strategic and tactical d
 ecisions of Airlines business. Successful RM processes aim to achieve the 
 maximization of revenue by leveraging huge amount of data\, upcoming techn
 ologies and more sophisticated approaches to measure the RM performances. 
 Multiple phases of RM processes\, as well as different components of RM sy
 stems\, are based on the solution of large integer programming models\, li
 ke the well-known Sales Based Integer Program (SBIP)\, whose instances tur
 n out to be challenging\, or even not solvable in practice by the state-of
 -art MIP solvers.Our work aims to investigate useful polyhedral properties
  and introduce a practical heuristic method to find a good solution of har
 d instances of SBIP. We use a LP-based branch-and-bound paradigm. Firstly\
 , we strengthen the linear relaxations of subproblems by introducing effec
 tive Chvátal-Gomory cuts\, exploiting the structure of the polytope.Our ma
 in contribution consists in the decomposition of the SBIP into two stage M
 INLP smaller problems. The basic idea is to separate the optimal allocatio
 n of the capacity to the markets and then to split it among the different 
 travel options on each market\, differently from the traditional leg-based
  decomposition. This leads to the formulation of the market subproblems as
  piecewise linear problem. We define a concave approximation of the piecew
 ise linear objective function in order to reach a good solution in reasona
 ble time. Decomposition ensures a radical reduction of the dimension of th
 e problem instances\, both the number of variables and of constraints\, wh
 ile the concave approximation reduces computational times for the solution
  of each subproblems. Computational results are reported. '  
DTSTART;TZID=Europe/Paris:20170727T110000
DTEND;TZID=Europe/Paris:20170727T120000
LAST-MODIFIED:20200515T073055Z
LOCATION:Aula A2 - DIAG
SUMMARY:MORE@DIAG: Marianna De Santis  -  Giorgio Grani   - Marianna De San
 tis\, Giorgio Grani\n\n\n  \n  \n\n    \n\n\nMarianna\n\n\nDe Santis  \n\n
   \n\n    \n\n\n\n\n\nesterno\n\n\npagina personale\n\nstanza: \n\nA116\n
 \ntelefono: \n\n+390677274078  \n\n  \n\n    \n\nBiografia: \n\n\n\n2007: 
 Laurea in Matematica (110 e lode)\, Sapienza Università di Roma\n2012: Dot
 torato in Ricerca Operativa\, Sapienza Università di Roma\n2012-2013: Asse
 gno di ricerca presso IASI (CNR)\, Roma\nSettembre 2013 - Febbraio 2016: P
 ost-Doc - Fakultät für Mathematik\,Technische Universität Dortmund\, Germa
 ny\nMarzo 2016 – Settembre 2016: Post-doc - Institut für Mathematik\,Alpen
 -Adria-Universität Klagenfurt\, Austria\nSettembre 2016 - Marzo 2017: Post
 -doc - Dipartimento di Matematica\, Università di Padova\, Italy\nAprile 2
 017 - Marzo 2020: RTDb (SSD MAT/09)\, DIAG\, Sapienza Università di Roma\n
 Maggio 2019: Abilitazione Scientifica Nazionale a ricoprire il ruolo di pr
 ofessore di II fascia (SSD MAT/09)\nAprile 2020 -: Professore Associato (S
 SD MAT/09)\, DIAG\, Sapienza Università di Roma\n\n\n*/\n\n\nkeywords: \n
 \nGlobal OptimizationMixed Integer Nonlinear ProgrammingSemidefinite Progra
 mming\n\nqualifica_rr: \n\nAssociate professors
URL;TYPE=URI:http://www.glad.uniroma1.it/node/12417
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