This function is simply a wrapper to
`KWDual`

with a similar interface to
`mixsqp`

for solving the same problem as `mixsqp`

. See
`mixsqp`

and `KWDual`

for details.

mixkwdual(L, w = rep(1, nrow(L)), ...)

## Arguments

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
point. `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 `storage.mode` . |

w |
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 `KWDual` . |

## Value

A list object with the following elements:

xThe estimated solution to the convex optimization problem.

valueThe value of the objective function at `x`

.

statusThe return status from MOSEK.

For more information on this output, see

KWDual and

mosek.

## See also