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X-WR-CALNAME;VALUE=TEXT:Eventi DIAG
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DTSTART:20191027T030000
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DTSTART:20190331T020000
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UID:calendar.18658.field_data.0@www.glad.uniroma1.it
DTSTAMP:20260409T101503Z
CREATED:20190916T150049Z
DESCRIPTION:Nell'ambito della procedura di valutazione di un Ricercatore a 
 TempoDeterminato tipologia B ai fini della chiamata nel ruolo di Professor
 e di II fascia ai sensidell’art. 24\, comma 5\, legge 240/2010\, SSD MAT/0
 9 – SC 01/A6Marianna De Santis terrà un seminario pubblico venerdì 20 Sett
 embre2019\, ore 11.00\, aula A5 (DIAG\, Via Ariosto 25) TITLE: Augmented L
 agrangian Approaches for Solving Doubly Nonnegative Programs*ABSTRACTIt is
  well known that semidefinite programming problems (SDPs) are solvable in 
 polynomial time by interior point methods.However\, if the number of const
 raints m in an SDP is of order O(n^2)\, when the unknown positive semidefi
 nitematrix is n × n\, interior point methods become impractical both in te
 rms of the time and the amountof memory required at each iteration. As a m
 atter of fact\, in order to compute the search direction\,Interior point m
 ethods need to form the m × m positive definite Schur complement matrixM a
 nd 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 matrices. Furthermore\, some first
 -order methods areable to take advantage of problem structure such as spar
 sity. Hence\, they are often more suitable\,and sometimes the only practic
 al choice for solving large-scale SDPs.Most existing first-order methods f
 or SDP are based on the augmented Lagrangian method.In this talk\, we focu
 s on doubly nonnegative problems (DNNs)\, namely semidefinite programmingp
 roblems where the elements of the matrix variable are constrained to be no
 nnegative.Starting from two methods already proposed in the literature on 
 conic programming\,we introduce Augmented Lagrangian methods with the poss
 ibility of employing a factorizationof the dual variable. We present numer
 icalresults for instances of the DNN relaxation of the stable set problem\
 ,including instances from the Second DIMACS Implementation Challenge.*
DTSTART;TZID=Europe/Paris:20190920T110000
DTEND;TZID=Europe/Paris:20190920T110000
LAST-MODIFIED:20200723T172246Z
LOCATION:AULA A5 DIAG
SUMMARY:Augmented Lagrangian Approaches for Solving Doubly Nonnegative Prog
 rams - \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 Ma
 tematica (110 e lode)\, Sapienza Università di Roma\n2012: Dottorato in Ri
 cerca Operativa\, Sapienza Università di Roma\n2012-2013: Assegno di ricer
 ca presso IASI (CNR)\, Roma\nSettembre 2013 - Febbraio 2016: Post-Doc - Fa
 kultät für Mathematik\,Technische Universität Dortmund\, Germany\nMarzo 20
 16 – Settembre 2016: Post-doc - Institut für Mathematik\,Alpen-Adria-Unive
 rsität Klagenfurt\, Austria\nSettembre 2016 - Marzo 2017: Post-doc - Dipar
 timento di Matematica\, Università di Padova\, Italy\nAprile 2017 - Marzo 
 2020: RTDb (SSD MAT/09)\, DIAG\, Sapienza Università di Roma\nMaggio 2019:
  Abilitazione Scientifica Nazionale a ricoprire il ruolo di professore di 
 II fascia (SSD MAT/09)\nAprile 2020 -: Professore Associato (SSD MAT/09)\,
  DIAG\, Sapienza Università di Roma\n\n\n*/\n\n\nkeywords: \n\nGlobal Opti
 mizationMixed Integer Nonlinear ProgrammingSemidefinite Programming\n\nqual
 ifica_rr: \n\nAssociate professors
URL;TYPE=URI:http://www.glad.uniroma1.it/node/18658
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