Add to ORKGIn this article, we propose a method for solving the trust-region subproblem when a limited-memory symmetric rank-one matrix is used in place of the true Hessian matrix. The method takes advantage of two shape-changing norms to decompose the trust-region subproblem into two separate problems, one of which has a closed-form solution and the other one is easy to solve. Sufficient conditions for global solutions to both subproblems are given. The proposed solver makes use of the structure of limited-memory symmetric rank-one matrices to find solutions that satisfy these optimality conditions. Solutions to the trust-region subproblem are computed to high-accuracy even in the so-called "hard case"
In classical trust-region optimization algorithms, the radius of the trust region is reduced, kept c...
The trust-region subproblem of minimizing a quadratic function subject to a norm constraint arises i...
Abstract. We consider methods for large-scale unconstrained minimization based on finding an approxi...
In this article, we propose a method for solving the trust-region subproblem when a limited-memory s...
In this work, we consider methods for large-scale and nonconvex unconstrained optimization. We propo...
Limited-memory quasi-Newton methods and trust-region methods represent two efficient approaches used...
Limited memory quasi-Newton methods and trust-region methods represent two efficient approaches used...
We present a matrix-free algorithm for the large-scale trust-region subproblem. Our algorithm relies...
Trust region algorithms are a class of recently developed algorithms for solving optimization proble...
Abstract. We consider methods for large-scale unconstrained minimization based on finding an approxi...
In this paper, a trust-region algorithm is proposed for large-scale nonlinear equations, where the l...
An improved trust region method for unconstrained optimization Jun Liu In this paper, a new trust re...
The trust-region subproblem of minimizing a quadratic function subject to a norm constraint arises i...
We present new algorithms for computing local minimizers of the trust-region subproblem (TRS). This...
Trust-region methods are amongst the most commonly used methods in unconstrained mathematical optimi...
In classical trust-region optimization algorithms, the radius of the trust region is reduced, kept c...
The trust-region subproblem of minimizing a quadratic function subject to a norm constraint arises i...
Abstract. We consider methods for large-scale unconstrained minimization based on finding an approxi...
In this article, we propose a method for solving the trust-region subproblem when a limited-memory s...
In this work, we consider methods for large-scale and nonconvex unconstrained optimization. We propo...
Limited-memory quasi-Newton methods and trust-region methods represent two efficient approaches used...
Limited memory quasi-Newton methods and trust-region methods represent two efficient approaches used...
We present a matrix-free algorithm for the large-scale trust-region subproblem. Our algorithm relies...
Trust region algorithms are a class of recently developed algorithms for solving optimization proble...
Abstract. We consider methods for large-scale unconstrained minimization based on finding an approxi...
In this paper, a trust-region algorithm is proposed for large-scale nonlinear equations, where the l...
An improved trust region method for unconstrained optimization Jun Liu In this paper, a new trust re...
The trust-region subproblem of minimizing a quadratic function subject to a norm constraint arises i...
We present new algorithms for computing local minimizers of the trust-region subproblem (TRS). This...
Trust-region methods are amongst the most commonly used methods in unconstrained mathematical optimi...
In classical trust-region optimization algorithms, the radius of the trust region is reduced, kept c...
The trust-region subproblem of minimizing a quadratic function subject to a norm constraint arises i...
Abstract. We consider methods for large-scale unconstrained minimization based on finding an approxi...