Abstract. We present a non-smooth optimization technique for non-convex maximum eigen-value functions and for non-smooth functions which are infinite maxima of eigenvalue functions. We prove global convergence of our method in the sense that for an arbitrary starting point, every ac-cumulation point of the sequence of iterates is critical. The method is tested on several problems in feedback control synthesis. Key words. Eigenvalue optimization, trust region method, proximity control, spectral bundle, feedback control synthesis, H∞-norm. 1. Introduction. Eigenvalu
In this paper, we consider smooth convex approximations to the maximum eigenvalue function. To make ...
This paper presents a computation method for pole assignment with eigenvalue and stability robustnes...
With the rapid development of science and technology as well as the cross-integration between the va...
: In this paper we present a nonsmooth algorithm to minimize the maximum eigenvalue of matrices belo...
We present an approximate nonsmooth algorithm to solve a minimization problem, in which the objectiv...
We consider a problem in eigenvalue optimization, in particular finding a local minimizer of the spe...
Optimization of convex functions subject to eigenvalue constraints is intriguing because of peculiar...
This work concerns the global minimization of a prescribed eigenvalue or a weighted sum of prescribe...
The trust-region subproblem of minimizing a quadratic function subject to a norm constraint arises i...
A central drawback of primal-dual interior point methods for semidefinite programs is their lack of ...
We develop an optimization technique to compute local solutions to synthesis problems subject to int...
The trust-region subproblem of minimizing a quadratic function subject to a norm constraint arises i...
We consider the solution of eigenvalue optimization problems involving large symmetric positive defi...
The trust-region subproblem of minimizing a quadratic function subject to a norm constraint arises i...
The Trust-Region Subproblem of minimizing a quadratic function subject to a norm constraint arises i...
In this paper, we consider smooth convex approximations to the maximum eigenvalue function. To make ...
This paper presents a computation method for pole assignment with eigenvalue and stability robustnes...
With the rapid development of science and technology as well as the cross-integration between the va...
: In this paper we present a nonsmooth algorithm to minimize the maximum eigenvalue of matrices belo...
We present an approximate nonsmooth algorithm to solve a minimization problem, in which the objectiv...
We consider a problem in eigenvalue optimization, in particular finding a local minimizer of the spe...
Optimization of convex functions subject to eigenvalue constraints is intriguing because of peculiar...
This work concerns the global minimization of a prescribed eigenvalue or a weighted sum of prescribe...
The trust-region subproblem of minimizing a quadratic function subject to a norm constraint arises i...
A central drawback of primal-dual interior point methods for semidefinite programs is their lack of ...
We develop an optimization technique to compute local solutions to synthesis problems subject to int...
The trust-region subproblem of minimizing a quadratic function subject to a norm constraint arises i...
We consider the solution of eigenvalue optimization problems involving large symmetric positive defi...
The trust-region subproblem of minimizing a quadratic function subject to a norm constraint arises i...
The Trust-Region Subproblem of minimizing a quadratic function subject to a norm constraint arises i...
In this paper, we consider smooth convex approximations to the maximum eigenvalue function. To make ...
This paper presents a computation method for pole assignment with eigenvalue and stability robustnes...
With the rapid development of science and technology as well as the cross-integration between the va...