Zobrazeno 1 - 10
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pro vyhledávání: '"Erway, Jennifer B."'
In this work, we consider methods for large-scale and nonconvex unconstrained optimization. We propose a new trust-region method whose subproblem is defined using a so-called "shape-changing" norm together with densely-initialized multipoint symmetri
Externí odkaz:
http://arxiv.org/abs/2209.12057
Autor:
Erway, Jennifer B., Rezapour, Mostafa
In large-scale optimization, when either forming or storing Hessian matrices are prohibitively expensive, quasi-Newton methods are often used in lieu of Newton's method because they only require first-order information to approximate the true Hessian
Externí odkaz:
http://arxiv.org/abs/2107.06321
Machine learning (ML) problems are often posed as highly nonlinear and nonconvex unconstrained optimization problems. Methods for solving ML problems based on stochastic gradient descent are easily scaled for very large problems but may involve fine-
Externí odkaz:
http://arxiv.org/abs/1807.00251
We consider a family of dense initializations for limited-memory quasi-Newton methods. The proposed initialization exploits an eigendecomposition-based separation of the full space into two complementary subspaces, assigning a different initializatio
Externí odkaz:
http://arxiv.org/abs/1710.02396
In this paper, we present the compact representation for matrices belonging to the the Broyden class of quasi-Newton updates, where each update may be either rank-one or rank-two. This work extends previous results solely for the restricted Broyden c
Externí odkaz:
http://arxiv.org/abs/1705.08306
Autor:
Erway, Jennifer B.1 (AUTHOR) erwayjb@wfu.edu, Rezapour, Mostafa1 (AUTHOR)
Publikováno v:
Optimization Methods & Software. Jul2023, Vol. 38 Issue 4, p698-722. 25p.
We present a MATLAB implementation of the symmetric rank-one (SC-SR1) method that solves trust-region. subproblems when a limited-memory symmetric rank-one (L-SR1) matrix is used in place of the true Hessian matrix, which can be used for large-scale
Externí odkaz:
http://arxiv.org/abs/1607.03533
In this paper, we solve the l2-l1 sparse recovery problem by transforming the objective function of this problem into an unconstrained differentiable function and apply a limited-memory trust-region method. Unlike gradient projection-type methods, wh
Externí odkaz:
http://arxiv.org/abs/1602.08813
Autor:
Erway, Jennifer B., Marcia, Roummel F.
We consider the problem of solving linear systems of equations arising with limited-memory members of the restricted Broyden class of updates and the symmetric rank-one (SR1) update. In this paper, we propose a new approach based on a practical imple
Externí odkaz:
http://arxiv.org/abs/1510.06378
In this article, we consider solvers for large-scale trust-region subproblems when the quadratic model is defined by a limited-memory symmetric rank-one (L-SR1) quasi-Newton matrix. We propose a solver that exploits the compact representation of L-SR
Externí odkaz:
http://arxiv.org/abs/1506.07222