Speaker: Daniel Kressner Affiliation: EPFL Time: Wednesday, 9 January 2019, h. 11:00 Place: Aula Seminari, Dipartimento di Matematica
Title: Tensorized Krylov subspace methods: Algorithms, analysis, and applications
Tensorized Krylov subspace methods are a versatile tool in numerical linear algebra for addressing large-scale applications that involve tensor product structure. This includes the discretization of high-dimensional PDEs, the solution of linear matrix equations, as well as low-rank updates and Frechet derivatives for matrix functions. This talk gives an overview of such methods, discusses their theoretical properties, and highlights applications.
Buongiorno, vi ricordo il seminario di oggi di Daniel Kressner a Matematica, alle ore 11.00.
A presto, -- Leonardo.
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Speaker: Daniel Kressner Affiliation: EPFL Time: Wednesday, 9 January 2019, h. 11:00 Place: Aula Seminari, Dipartimento di Matematica
Title: Tensorized Krylov subspace methods: Algorithms, analysis, and applications
Tensorized Krylov subspace methods are a versatile tool in numerical linear algebra for addressing large-scale applications that involve tensor product structure. This includes the discretization of high-dimensional PDEs, the solution of linear matrix equations, as well as low-rank updates and Frechet derivatives for matrix functions. This talk gives an overview of such methods, discusses their theoretical properties, and highlights applications.
Buongiorno, inoltro il seguente annuncio di seminario.
-- Leonardo.
Speaker: Prof. Immanuel Bomze Affiliation: University of Vienna Time: Wednesday, 6 February 2019, h. 11:30 Place: Sala Gerace, Dipartimento di Informatica
Title: The simplest of the hard problems has a wide application range - from Standard Quadratic to Copositive Optimization
(joint work with M. Kahr, M. Leitner, W. Schachinger, R. Ullrich)
In a Standard Quadratic Optimization Problem (StQP), a possibly indefinite quadratic form (the simplest nonlinear function) is extremized over the standard simplex, the simplest polytope. Despite this simplicity, the nonconvex instances of this problem class allow for remarkably rich patterns of coexisting local solutions, which are closely related to practical difficulties in solving StQPs globally, and also reflect NP-hardness of the problem.
Apart from presenting a new world record we apparently hold, and a robust counterpart in case of data uncertainty, the connections to a powerful class of conic optimization involving copositivity will shortly be addressed. Applications of this approach range from general Nonconvex (Fractional) Quadratic Optimization involving continuous and also binary variables, to stochastic optimization problems with second-order moment conditions, allowing for a distributionally robust framework.
RIFERIMENTO: MG Scutellà
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Leonardo Robol