This paper reports two proposals of possible preconditioners for the Nonlinear Conjugate Gradient (NCG) method, in large scale unconstrained optimization. On one hand, the common idea of our preconditioners is inspired to L-BFGS quasi–Newton updates, on the other hand we aim at explicitly...
Nonlinear Optimization
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We propose a class of preconditioners for large positive definite linear systems, arising in nonlinear optimization frameworks. These preconditioners can be computed as by-product of Krylov-subspace solvers. Preconditioners in our class are chosen by setting the values of some user-dependent...
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Speaker: Tommaso Colombo
Title: Recurrent Neural Networks: why do LSTM networks perform so well in time series prediction?
(Joint work with: Alberto De Santis, Stefano Lucidi)
Abstract:
Long Short-Term Memory (LSTM)...