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Approximately solve constant-matrix, upper bound µ-synthesis problem

[QOPT,BND] = cmsclsyn(R,U,V,BlockStructure); [QOPT,BND] = cmsclsyn(R,U,V,BlockStructure,opt); [QOPT,BND] = cmsclsyn(R,U,V,BlockStructure,opt,qinit); [QOPT,BND] = cmsclsyn(R,U,V,BlockStructure,opt,'random',N)

` cmsclsyn` approximately
solves the constant-matrix, upper bound µ-synthesis problem by
minimization,

for given matrices *R* ∊ **C**^{n}x_{m}, *U* ∊ **C**^{n}x_{r}, *V* ∊ **C**^{t}x_{m}, and a set Δ ⊂ **C**^{m}x_{n}. This applies to constant matrix data
in *R, U,* and *V*.

`[QOPT,BND] = cmsclsyn(R,U,V,BlockStructure)` minimizes,
by choice of Q. `QOPT` is the optimum value of Q,
the upper bound of `mussv(R+U*Q*V,BLK), BND`. The
matrices `R,U` and` V` are constant
matrices of the appropriate dimension.` BlockStructure` is
a matrix specifying the perturbation blockstructure as defined for `mussv`.

`[QOPT,BND] = cmsclsyn(R,U,V,BlockStructure,OPT)` uses
the options specified by `OPT` in the calls to `mussv`.
See `mussv` for more information. The default value
for `OPT` is `'cUsw'`.

`[QOPT,BND] = cmsclsyn(R,U,V,BlockStructure,OPT,QINIT)` initializes
the iterative computation from Q = `QINIT`. Because
of the nonconvexity of the overall problem, different starting points
often yield different final answers. If `QINIT` is
an N-D array, then the iterative computation is performed multiple
times - the `i`'th optimization is initialized at
Q = `QINIT(:,:,i).` The output arguments are associated
with the best solution obtained in this brute force approach.

`[QOPT,BND] = cmsclsyn(R,U,V,BlockStructure,OPT,'random',N)` initializes
the iterative computation from `N` random instances
of `QINIT`. If `NCU` is the number
of columns of `U`, and `NRV` is
the number of rows of `V`, then the approximation
to solving the constant matrix µ synthesis problem is two-fold:
only the upper bound for µ is minimized, and the minimization
is not convex, hence the optimum is generally not found. If `U` is
full column rank, or `V` is full row rank, then the
problem can (and is) cast as a convex problem, [Packard, Zhou, Pandey
and Becker], and the global optimizer (for the upper bound for µ)
is calculated.

Packard, A.K., K. Zhou, P. Pandey, and G. Becker, "A
collection of robust control problems leading to LMI's," *30th
IEEE Conference on Decision and Control,* Brighton, UK,
1991, p. 1245–1250.

`dksyn` | `hinfsyn` | `mussv` | `robustperf` | `robuststab`

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