Solution of Lyapunov, Sylvester and Riccati matrix equations using Julia
Julia 1.6 and higher.
How to Install
pkg> add MatrixEquations
pkg> test MatrixEquations
This collection of Julia functions is an attemp to implement high performance numerical software to solve several classes of Lyapunov, Sylvester and Riccati matrix equations at a performance level comparable with efficient structure exploiting Fortran implementations, as those available in the Systems and Control Library SLICOT. This goal has been fully achieved for Lyapunov and Sylvester equation solvers, for which the codes for both real and complex data perform at practically same performance level as similar functions available in the MATLAB Control System Toolbox (which rely on SLICOT).
The available functions in the
MatrixEquation.jl package cover both standard
and generalized continuous and discrete Lyapunov, Sylvester and Riccati equations for both real and complex data. The functions for the solution of Lyapunov and Sylvester equations rely on efficient structure
exploiting solvers for which the input data are in Schur or generalized Schur forms. A comprehensive set of Lyapunov and Sylvester operators has been implemented, which allow the estimation of condition numbers of these operators. The implementation of Riccati equation solvers employ orthogonal Schur vectors
based methods and their extensions to linear matrix pencil based reduction approaches. The calls of all functions with adjoint (in complex case) or transposed (in real case) arguments are fully supported by appropriate computational algorithms, thus the matrix copying operations are mostly avoided.
The current version of the package includes the following functions:
Solution of Lyapunov equations
- lyapc Solution of the continuous Lyapunov equations
AX+XA'+C = 0and
AXE'+EXA'+C = 0.
- lyapd Solution of discrete Lyapunov equations
AXA'-X +C = 0and
AXA'-EXE'+C = 0.
- plyapc Solution of the positive continuous Lyapunov equations
AX+XA'+BB' = 0and
AXE'+EXA'+BB' = 0.
- plyapd Solution of the positive discrete Lyapunov equations
AXA'-X +C = 0and
AXA'-EXE'+C = 0.
Solution of algebraic Riccati equations
- arec Solution of the continuous Riccati equations
A'X+XA-XRX+Q = 0and
A'X+XA-(XB+S)R^(-1)(B'X+S')+Q = 0.
- garec Solution of the generalized continuous Riccati equation
A'XE+E'XA-(A'XB+S)R^(-1)(B'XA+S')+Q = 0.
- ared Solution of the discrete Riccati equation
A'XA - X - (A'XB+S)(R+B'XB)^(-1)(B'XA+S') + Q = 0.
- gared Solution of the generalized discrete Riccati equation
A'XA - E'XE - (A'XB+S)(R+B'XB)^(-1)(B'XA+S') + Q = 0.
Solution of Sylvester equations and systems
- sylvc Solution of the (continuous) Sylvester equation
AX+XB = C.
- sylvd Solution of the (discrete) Sylvester equation
AXB+X = C.
- gsylv Solution of the generalized Sylvester equation
AXB+CXD = E.
- sylvsys Solution of the Sylvester system of matrix equations
AX+YB = C, DX+YE = F.
- dsylvsys Solution of the dual Sylvester system of matrix equations
AX+DY = C, XB+YE = F.
Norm, condition and separation estimation
- opnorm1 Computation of the 1-norm of a linear operator.
- opnorm1est Estimation of 1-norm of a linear operator.
- oprcondest Estimation of the reciprocal 1-norm condition number of a linear operator.
- opsepest Estimation of the 1-norm separation a linear operator.
The general solvers of Lyapunov and Sylvester equations rely on a set of specialized solvers for real or complex matrices in appropriate Schur forms. For testing purposes, a set of solvers for Sylvester equations has been implemented, which employ the Kronecker-product expansion of the equations. These solvers are not recommended for large order matrices. The norms, reciprocal condition numbers and separations can be estimated for a comprehensive set of predefined Lyapunov and Sylvester operators. A complete list of implemented functions is available here.
The collection of tools can be extended by adding new functionality, such as expert solvers, which additionally compute error bounds and condition estimates, or solvers for new classes of Riccati equations, as those arising in game-theoretic optimization problems. Further performance improvements are still possible (e.g., in some positive Lyapunov solvers by employing specially taylored solvers for the underlying particular Sylvester equations) or by employing blocking based variants of solvers for Lyapunov and Sylvester equations.