Zobrazeno 1 - 10
of 334
pro vyhledávání: '"Toint, Philippe"'
Autor:
Toint, Philippe L.
The adaptive regularization algorithm for unconstrained nonconvex optimization was shown in Nesterov and Polyak (2006) and Cartis, Gould and Toint (2011) to require, under standard assumptions, at most $\mathcal{O}(\epsilon^{3/(3-q)})$ evaluations of
Externí odkaz:
http://arxiv.org/abs/2409.16047
We revisit the standard ``telescoping sum'' argument ubiquitous in the final steps of analyzing evaluation complexity of algorithms for smooth nonconvex optimization, and obtain a refined formulation of the resulting bound as a function of the reques
Externí odkaz:
http://arxiv.org/abs/2408.09124
Autor:
Gratton, Serge, Toint, Philippe L.
A new decoder for the SIF test problems of the CUTEst collection is described, which produces problem files allowing the computation of values and derivatives of the objective function and constraints of most \cutest\ problems directly within ``nativ
Externí odkaz:
http://arxiv.org/abs/2407.07812
A fully stochastic second-order adaptive-regularization method for unconstrained nonconvex optimization is presented which never computes the objective-function value, but yet achieves the optimal $\mathcal{O}(\epsilon^{-3/2})$ complexity bound for f
Externí odkaz:
http://arxiv.org/abs/2407.08018
A parametric class of trust-region algorithms for constrained nonconvex optimization is analyzed, where the objective function is never computed. By defining appropriate first-order stationarity criteria, we are able to extend the Adagrad method to t
Externí odkaz:
http://arxiv.org/abs/2406.15793
Multi-level methods are widely used for the solution of large-scale problems, because of their computational advantages and exploitation of the complementarity between the involved sub-problems. After a re-interpretation of multi-level methods from a
Externí odkaz:
http://arxiv.org/abs/2305.14477
A class of second-order algorithms is proposed for minimizing smooth nonconvex functions that alternates between regularized Newton and negative curvature steps in an iteration-dependent subspace. In most cases, the Hessian matrix is regularized with
Externí odkaz:
http://arxiv.org/abs/2302.10065
Autor:
Gratton, Serge, Toint, Philippe L.
OPM is a small collection of CUTEst unconstrained and bound-constrained nonlinear optimization problems, which can be used in Matlab for testing optimization algorithms directly (i.e. without installing additional software).
Externí odkaz:
http://arxiv.org/abs/2112.05636
Autor:
Gratton, Serge, Toint, Philippe L.
A regularization algorithm (AR1pGN) for unconstrained nonlinear minimization is considered, which uses a model consisting of a Taylor expansion of arbitrary degree and regularization term involving a possibly non-smooth norm. It is shown that the non
Externí odkaz:
http://arxiv.org/abs/2105.07765
This paper considers optimization of smooth nonconvex functionals in smooth infinite dimensional spaces. A H\"older gradient descent algorithm is first proposed for finding approximate first-order points of regularized polynomial functionals. This me
Externí odkaz:
http://arxiv.org/abs/2104.02564