Speaker: Yaroslav D. Sergeyev
Affiliation: Università della Calabria and N.I. Lobachevsky University
of Nizhni Novgorod, Russia.
Time: Tuesday, 12 April 2016, 12:00
Place: Sala Seminari Ovest, Dipartimento di Informatica, Università di Pisa
Title: The Infinity Computer and numerical
computations with infinities and in infinitesimals
Abstract:
The lecture presents a recent methodology allowing one to execute
numerical computations with finite, infinite, and infinitesimal numbers
on a new type of a computer – the Infinity Computer – patented in EU,
USA, and Russia (see [23]). The new approach is based on the principle
‘The whole is greater than the part’ (Euclid’s Common Notion 5) that is
applied to all numbers (finite, infinite, and infinitesimal) and to all
sets and processes (finite and infinite). It is shown that it becomes
possible to write down finite, infinite, and infinitesimal numbers by a
finite number of symbols as particular cases of a unique framework
different from that of the non-standard analysis [...]
http://pages.di.unipi.it/fpoloni/files/seminari/Sergeyev_Infinity.pdf
===
Everyone is welcome!
--
--federico poloni
Dipartimento di Informatica, Università di Pisa
http://www.di.unipi.it/~fpoloni/ tel:+39-050-2213143
Speaker: Yaroslav D. Sergeyev
Affiliation: Università della Calabria and N.I. Lobachevsky University
of Nizhni Novgorod, Russia.
Time: Monday, 11 April 2016, 15:00
Place: Sala Seminari Ovest, Dipartimento di Informatica, Università di Pisa
Title: Lipschitz global optimization
Abstract: Global optimization is a thriving branch of applied
mathematics and an extensive literature is dedicated to it (see e.g.,
[1–24]). In this lecture, the global optimization problem of a
multidimensional function satisfying the Lipschitz condition over a
hyperinterval with an unknown Lipschitz constant is considered. It is
supposed that the objective function can be “black box”, multiextremal,
and non-differentiable. It is also assumed that evaluation of the
objective function at a point is a time-consuming operation. Many
algorithms for solving this problem have been discussed in literature.
They can be distinguished, for example, by the way of obtaining
information about the Lipschitz constant and by the strategy of
exploration of the search domain. Different exploration techniques based
on various adaptive partition strategies are analyzed. [...]
http://pages.di.unipi.it/fpoloni/files/seminari/Sergeyev.pdf
===
Everyone is welcome!
--
--federico poloni
Dipartimento di Informatica, Università di Pisa
http://www.di.unipi.it/~fpoloni/ tel:+39-050-2213143
Nell’ambito del corso di Esperienze di programmazione saranno tenuti 4 seminari introduttivi di circa 2 ore sul linguaggio Mathematica.
Mercoledì 9/3 11-13 Aula C1 Introduzione a Mathematica
Mercoledì 16/3 11-13 Aula C1 Grafica con Mathematica
Mercoledì 23/3 11-13 Aula C1 Programmazione in Mathematica
Mercoledì 27/4 11-13 Aula C1 Calcolo Numerico con Mathematica
La partecipazione è aperta a tutti e vi prego di diffondere l’informazione ad eventuali interessati.
Grazie, saluti, FR
____________________________
Prof. Francesco Romani
Dipartimento di Informatica
Via Buonarroti 2, 56127 PISA, ITALY
Tel +39-050-2212734
FAX +39-050-2212726
http://www.di.unipi.it/~romani
Anche quest’anno, all’interno del corso di Esperienze di Programmazione, faccio 4-5 seminari su Mathematica.
L’orario è mercoledi 11-13 / Venerdi 16-18
Avendo una certra libertà per collocarli volevo sapere se qualcuno ha intenzione di seguirli e ha preferenze sugli orari.
Grazie saluti Francesco
____________________________
Prof. Francesco Romani
Dipartimento di Informatica
Via Buonarroti 2, 56127 PISA, ITALY
Tel +39-050-2212734
FAX +39-050-2212726
http://www.di.unipi.it/~romani
Speaker: Andrea Masciotta
Affiliation: Dipartimento di Matematica, Università di Pisa
Time: Monday, 7 March 2016, 14:30
Place: Sala Seminari Ovest, Dipartimento di Informatica, Università di Pisa
Title: Minimization algorithms for compressive sensing
Abstract: Compressive sensing is a new approach to digital signals
acquisition and processing. Its main aim is the reconstruction of sparse
signals or images (i.e. with few significant coefficients) from what was
previously believed to be incomplete information. Compressive sensing
has many potential applications, not only limited to the analysis of
signals or images.
In this talk we will first describe the main theoretical properties of
compressive sensing, then three solution algorithms based on \ell_1
minimization will be presented, along with numerical experiments to
highlight the main characteristics of these three methods in the
framework of compressive sensing.
===
Everyone is welcome!
--
--federico poloni
Dipartimento di Informatica, Università di Pisa
http://www.di.unipi.it/~fpoloni/ tel:+39-050-2213143
Speaker: Enrico Miglierina
Affiliation: Università Cattolica del Sacro Cuore, Milano
Time: Thursday, 25 February 2016, 15:00
Place: Sala Seminari Est, Dipartimento di Informatica, Università di Pisa
Title: Stability by set convergences: from Vector to Set optimization
Abstract: (pdf)
http://pages.di.unipi.it/fpoloni/files/seminari/Miglierina.pdf
The notions of set convergences reveal to be very useful to study
stability properties in vector optimization. Indeed, some results that
show how the convergence of feasible region implies the convergence of
efficient frontiers are known. The oldest ones are based on suitable
compactness assumptions that concern the sequence of sets as a whole(see
\cite{Attouch-Rihai,Bednarczuk2007,Luc-Lucchetti-Malivert,LoridanMorganRaucci,Penot-Sterna}).
Moreover, more recently, some results about convergence of efficient
frontiers have been obtained by replacing compactness by convexity
(\cite{Lucchetti-Miglierina2004Opt,MM2005}). After a presentation of
these results, the last aim of the talk is to extend this class of
results to the broader framework of set optimization, that recently
received an increasing attention in the literature
(\cite{Gutierrez-Miglierina-Molho-Novo}).
===
Everyone is welcome!
--
--federico poloni
_______________________________________________
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Speaker: Nataša Strabić
Affiliation: The University of Manchester
Time: Wednesday, December, 9; h. 15:00
Place: Sala Seminari Ovest, Dipartimento di Informatica, Università di Pisa
Title: Recent Progress on the Nearest Correlation Matrix Problem
Abstract: In a wide range of applications it is required to replace an
empirically obtained unit diagonal indefinite symmetric matrix with a
valid correlation matrix (unit diagonal positive semidefinite matrix).
A popular replacement is the nearest correlation matrix in the Frobenius
norm.
The first method for computing the nearest correlation matrix with
guaranteed convergence was the alternating projections method proposed
by Higham in 2002.
The rate of convergence of this method is at best linear, and it can
require a large number of iterations to converge to within a given
tolerance.
Although a faster globally convergent Newton algorithm was subsequently
developed by Qi and Sun in 2006, the alternating projections method
remains very widely used.
We show that Anderson acceleration, a technique for accelerating the
convergence of fixed-point iterations, can be applied to the alternating
projections method and that in practice it brings a significant
reduction in both the number of iterations and the computation time.
We also show that Anderson acceleration remains effective, and indeed
can provide even greater improvements, when it is applied to the
variants of the nearest correlation matrix problem in which specified
elements are fixed or a lower bound is imposed on the smallest eigenvalue.
This is particularly significant for the nearest correlation matrix
problem with fixed elements because no Newton method with guaranteed
convergence is available for it.
Both methods for computing the nearest correlation matrix are based on
repeated eigenvalue decompositions and so they can be infeasible in
time-critical situations.
We have recently proposed an alternative method to restore definiteness
to an indefinite matrix called shrinking.
The method is based on computing the optimal parameter in a convex
linear combination of the indefinite starting matrix and a chosen
positive definite target matrix.
We show how this problem can be solved by the bisection method and posed
as a generalized eigenvalue problem, and we demonstrate how exploiting
positive definiteness in these two methods leads to impressive
computational savings.
The work on these two topics is joint with Nicholas J. Higham, and, for
shrinking, with Vedran Šego.
===
Everyone is welcome!
--
--federico poloni
Dipartimento di Informatica, Università di Pisa
http://www.di.unipi.it/~fpoloni/ tel. +39 050 2213143
______________________________________________
Dipartimento.di mailing list
Dipartimento.di(a)listgateway.unipi.it
http://listgateway.unipi.it/mailman/listinfo/dipartimento.di
Speaker: Nataša Strabić
Affiliation: The University of Manchester
Time: Wednesday, December, 9; h. 15:00
Place: Sala Seminari Ovest, Dipartimento di Informatica, Università di Pisa
Title: Recent Progress on the Nearest Correlation Matrix Problem
Abstract: In a wide range of applications it is required to replace an
empirically obtained unit diagonal indefinite symmetric matrix with a
valid correlation matrix (unit diagonal positive semidefinite matrix).
A popular replacement is the nearest correlation matrix in the Frobenius
norm.
The first method for computing the nearest correlation matrix with
guaranteed convergence was the alternating projections method proposed
by Higham in 2002.
The rate of convergence of this method is at best linear, and it can
require a large number of iterations to converge to within a given
tolerance.
Although a faster globally convergent Newton algorithm was subsequently
developed by Qi and Sun in 2006, the alternating projections method
remains very widely used.
We show that Anderson acceleration, a technique for accelerating the
convergence of fixed-point iterations, can be applied to the alternating
projections method and that in practice it brings a significant
reduction in both the number of iterations and the computation time.
We also show that Anderson acceleration remains effective, and indeed
can provide even greater improvements, when it is applied to the
variants of the nearest correlation matrix problem in which specified
elements are fixed or a lower bound is imposed on the smallest eigenvalue.
This is particularly significant for the nearest correlation matrix
problem with fixed elements because no Newton method with guaranteed
convergence is available for it.
Both methods for computing the nearest correlation matrix are based on
repeated eigenvalue decompositions and so they can be infeasible in
time-critical situations.
We have recently proposed an alternative method to restore definiteness
to an indefinite matrix called shrinking.
The method is based on computing the optimal parameter in a convex
linear combination of the indefinite starting matrix and a chosen
positive definite target matrix.
We show how this problem can be solved by the bisection method and posed
as a generalized eigenvalue problem, and we demonstrate how exploiting
positive definiteness in these two methods leads to impressive
computational savings.
The work on these two topics is joint with Nicholas J. Higham, and, for
shrinking, with Vedran Šego.
===
Everyone is welcome!
--
--federico poloni
Dipartimento di Informatica, Università di Pisa
http://www.di.unipi.it/~fpoloni/ tel. +39 050 2213143
______________________________________________
Dipartimento.di mailing list
Dipartimento.di(a)listgateway.unipi.it
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Speaker: Caterina Fenu
Affiliation: Università di Pisa
Time: Thursday, November, 26; 9 am
Place: Sala Seminari Ovest, Dipartimento di Informatica, Università di Pisa
Title: Fast Computation of Centrality Indices
Abstract: One of the main issues in complex networks theory is to find
the “most important” nodes. To this aim, one can use matrix functions
applied to its adjacency matrix.
After an introduction on the use of Gauss-type quadrature rules, we will
discuss a new computational method to rank the nodes of both directed
and undirected (unweighted) networks according to the values of these
functions. The algorithm uses a low-rank approximation of the adjacency
matrix, then Gauss quadrature is used to refine the computation.
Extended pdf abstract on
http://www.di.unipi.it/~fpoloni/files/seminari/Fenu.pdf
===
Everyone is welcome!
--
--federico poloni
Dipartimento di Informatica, Università di Pisa
http://www.di.unipi.it/~fpoloni/ tel. +39 050 2213143
Speaker: Simone Brugiapaglia
Affiliation: Politecnico di Milano --- Laboratory for Modeling and
Scientific Computing MOX
Time: Tuesday, December 15, 11 am
Place: Sala Seminari Est, Dipartimento di Informatica, Università di Pisa
Title: CORSING: Sparse approximation of PDEs based on Compressed Sensing
Abstract: We present a novel method for the numerical approximation of
PDEs, motivated by recent developments in sparse representation, and
particularly by compressed sensing. We named this approach CORSING
(COmpRessed SolvING).
Establishing an analogy between the sampling of a signal and the
Petrov-Galerkin discretization of a PDE, the CORSING method can recover
the best s-term approximation to the solution with respect to N suitable
trial functions, with s<<N, by evaluating the bilinear form associated
with the PDE against a randomized choice of m<<N test functions. This
yields an underdetermined m x N linear system, that is solved by means
of sparse optimization techniques.
A theoretical analysis of the CORSING procedure is presented, based on
the concepts of local a-coherence and restricted inf-sup property, along
with numerical experiments that confirm the robustness and reliability
of the proposed strategy.
===
Everyone is welcome!
--
--federico poloni
Dipartimento di Informatica, Università di Pisa
http://www.di.unipi.it/~fpoloni/ tel:+39-050-2213143