This function is simply a wrapper to
KWDual with a similar interface to
mixsqp for solving the same problem as
KWDual for details.
mixkwdual(L, w = rep(1, nrow(L)), ...)
Matrix specifying the optimization problem to be solved.
In the context of mixture-model fitting,
L[j,k] should be
the value of the kth mixture component density at the jth data
L should be a numeric matrix with at least two
columns, with all entries being non-negative and finite (and not
missing). For large matrices, it is preferrable that the matrix is
stored in double-precision; see
An optional numeric vector, with one entry for each row of
L, specifying the "weights" associated with the rows of
L. All weights must be finite, non-negative and not
missing. Internally, the weights are normalized to sum to 1,
which does not change the problem, but does change the value of the
objective function reported. By default, all weights are equal.
Additional arguments passed to
A list object with the following elements:
The estimated solution to the convex optimization problem.
The value of the objective function at
The return status from MOSEK.
For more information on this output, see