A numerical comparison of solvers for large-scale, continuous-time algebraic Riccati equations and LQR problems
arXiv:1811.00850 · doi:10.1137/18M1220960
Abstract
In this paper, we discuss numerical methods for solving large-scale continuous-time algebraic Riccati equations. These methods have been the focus of intensive research in recent years, and significant progress has been made in both the theoretical understanding and efficient implementation of various competing algorithms. There are several goals of this manuscript: first, to gather in one place an overview of different approaches for solving large-scale Riccati equations, and to point to the recent advances in each of them. Second, to analyze and compare the main computational ingredients of these algorithms, to detect their strong points and their potential bottlenecks. And finally, to compare the effective implementations of all methods on a set of relevant benchmark examples, giving an indication of their relative performance.
References in corpus (2)
Cited by in corpus (6)
- A low-rank solution method for Riccati equations with indefinite quadratic terms
- Optimal control of network-coupled subsystems: Spectral decomposition and low-dimensional solutions
- Distributed Control of Descriptor Networks: A Convex Procedure for Augmented Sparsity
- On a family of low-rank algorithms for large-scale algebraic Riccati equations
- Stochastic algebraic Riccati equations are almost as easy as deterministic ones theoretically
- The intrinsic Toeplitz structure and its applications in algebraic Riccati equations