The LMI Lab is a high-performance package for solving general
LMI problems. It blends simple tools for the specification and manipulation
of LMIs with powerful LMI solvers for three generic LMI problems.
Thanks to a structure-oriented representation of LMIs, the various
LMI constraints can be described in their natural block-matrix form.
Similarly, the optimization variables are specified directly as matrix
variables with some given structure. Once an LMI problem
is specified, it can be solved numerically by calling the appropriate
LMI solver. The three solvers
the computational engine of the LMI portion of Robust Control Toolbox™ software.
Their high performance is achieved through C-MEX implementation and
by taking advantage of the particular structure of each LMI.
The LMI Lab offers tools to
Retrieve information about existing systems of LMIs
Modify existing systems of LMIs
Solve the three generic LMI problems (feasibility problem, linear objective minimization, and generalized eigenvalue minimization)
This chapter gives a tutorial introduction to the LMI Lab as well as more advanced tips for making the most out of its potential.
Any linear matrix inequality can be expressed in the canonical form
L(x) = L0 + x1L1 + . . . + xNLN < 0
L0, L1, . . . , LN are given symmetric matrices
x = (x1, . . . , xN)T ∊ RN is the vector of scalar variables to be determined. We refer to x1, . . . , xN as the decision variables. The names "design variables" and "optimization variables" are also found in the literature.
Even though this canonical expression is generic, LMIs rarely arise in this form in control applications. Consider for instance the Lyapunov inequality
and the variable
is a symmetric matrix. Here the decision variables are the free entries x1, x2, x3 of X and the canonical form of this LMI reads
Clearly this expression is less intuitive and transparent than Equation 4-1. Moreover, the
number of matrices involved in Equation 4-2 grows roughly as n2 /2
if n is the size of the A matrix.
Hence, the canonical form is very inefficient from a storage viewpoint
since it requires storing
matrices of size n when the single n-by-n matrix A would
be sufficient. Finally, working with the canonical form is also detrimental
to the efficiency of the LMI solvers. For these various reasons, the
LMI Lab uses a structured representation of LMIs.
For instance, the expression ATX + XA in
the Lyapunov inequality Equation 4-1 is explicitly described as a function
of the matrix variable X, and only the A matrix
In general, LMIs assume a block matrix form where each block is an affine combination of the matrix variables. As a fairly typical illustration, consider the following LMI drawn from H∞ theory
where A, B, C, D, and N are given matrices and the problem variables are X = XT ∊ Rn×n and γ ∊ R. We use the following terminology to describe such LMIs:
N is called the outer factor, and the block matrix
is called the inner factor. The outer factor needs not be square and is often absent.
X and γ are the matrix variables of the problem. Note that scalars are considered as 1-by-1 matrices.
The inner factor L(X, γ) is a symmetric block matrix, its block structure being characterized by the sizes of its diagonal blocks. By symmetry, L(X, γ) is entirely specified by the blocks on or above the diagonal.
Each block of L(X, γ) is an affine expression in the matrix variables X and γ. This expression can be broken down into a sum of elementary terms. For instance, the block (1,1) contains two elementary terms: ATX and XA.
Terms are either constant or variable. Constant terms are fixed matrices like B and D above. Variable terms involve one of the matrix variables, like XA, XCT, and –γI above.
The LMI (Equation 4-3) is specified by the list of terms in each block, as is any LMI regardless of its complexity.
As for the matrix variables X and γ, they are characterized by their dimensions and structure. Common structures include rectangular unstructured, symmetric, skew-symmetric, and scalar. More sophisticated structures are sometimes encountered in control problems. For instance, the matrix variable X could be constrained to the block-diagonal structure:
Another possibility is the symmetric Toeplitz structure:
Summing up, structured LMI problems are specified by declaring the matrix variables and describing the term content of each LMI. This term-oriented description is systematic and accurately reflects the specific structure of the LMI constraints. There is no built-in limitation on the number of LMIs that you can specify or on the number of blocks and terms in any given LMI. LMI systems of arbitrary complexity can therefore, be defined in the LMI Lab.
The LMI Lab offers tools to specify, manipulate, and numerically solve LMIs. Its main purpose is to
Allow for straightforward description of LMIs in their natural block-matrix form
Provide easy access to the LMI solvers (optimization codes)
Facilitate result validation and problem modification
The structure-oriented description of a given LMI system is
stored as a single vector called the internal representation and
generically denoted by
LMISYS in the sequel. This
vector encodes the structure and dimensions of the LMIs and matrix
variables, a description of all LMI terms, and the related numerical
data. It must be stressed that you need not attempt to read or understand
the content of
LMISYS since all manipulations involving
this internal representation can be performed in a transparent manner
with LMI-Lab tools.
The LMI Lab supports the following functionalities:
LMI systems can be either specified as symbolic matrix expressions
with the interactive graphical user interface
or assembled incrementally with the two commands
The first option is more intuitive and transparent while the second
option is more powerful and flexible.
The interactive function
qualitative queries about LMI systems created with
lmiterm. You can also use
lmiedit to visualize the LMI system produced
by a particular sequence of
General-purpose LMI solvers are provided for the three generic
LMI problems defined in LMI Applications. These solvers can handle very
general LMI systems and matrix variable structures. They return a
feasible or optimal vector of decision variables x*.
The corresponding values of
the matrix variables are given by the function
The solution x* produced by the LMI solvers
is easily validated with the functions
showlmi. This allows a fast check and/or
analysis of the results. With
all variable terms in the LMI system are evaluated for the value x*
of the decision variables. The left and right sides of each LMI then
become constant matrices that can be displayed with
An existing system of LMIs can be modified in two ways:
An LMI can be removed from the system with
A matrix variable X can be deleted
delmvar. It can also
be instantiated, that is, set to some given matrix value. This operation
is performed by
allows, for example, to fix some variables and solve the LMI problem
with respect to the remaining ones.