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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A greedy randomized adaptive search procedure (GRASP) is an itera- tive multistart metaheuristic for difficult combinatorial optimization problems. Each GRASP iteration consists of two phases: a construction phase, in which a feasible solution is produced, and a local search phase, in which a local...
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We consider the problem of extracting a complete set of numerical parameters that characterize the robot dynamics, starting from the identified values of dynamic coefficients that linearly parametrize the robot dynamic equations. This information is relevant when realistic dynamic simulations have...
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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)...