Zobrazeno 1 - 10
of 211
pro vyhledávání: '"Toint, Philippe L."'
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
Intrinsic noise in objective function and derivatives evaluations may cause premature termination of optimization algorithms. Evaluation complexity bounds taking this situation into account are presented in the framework of a deterministic trust-regi
Externí odkaz:
http://arxiv.org/abs/2104.02519
This paper focuses on regularisation methods using models up to the third order to search for up to second-order critical points of a finite-sum minimisation problem. The variant presented belongs to the framework of [3]: it employs random models wit
Externí odkaz:
http://arxiv.org/abs/2104.00592
We introduce the concept of strong high-order approximate minimizers for nonconvex optimization problems. These apply in both standard smooth and composite non-smooth settings, and additionally allow convex or inexpensive constraints. An adaptive reg
Externí odkaz:
http://arxiv.org/abs/2001.10802
A structured version of derivative-free random pattern search optimization algorithms is introduced which is able to exploit coordinate partially separable structure (typically associated with sparsity) often present in unconstrained and bound-constr
Externí odkaz:
http://arxiv.org/abs/2001.04801