This class wraps around the PIQP C++ Solver and
exposes methods and fields of the C++ object. Users will never
need to directly create instances this class and should use the
more user-friendly functions piqp() and solve_piqp().
Methods
Method new()
Create a new piqp_model object
Usage
piqp_model$new(
P,
c,
A,
b,
G,
h,
x_lb,
x_ub,
settings = list(),
dense_backend,
dims
)Arguments
Pdense or sparse matrix of class dgCMatrix or coercible into such, must be positive semidefinite
cnumeric vector
Adense or sparse matrix of class dgCMatrix or coercible into such
bnumeric vector
Gdense or sparse matrix of class dgCMatrix or coercible into such
hnumeric vector
x_lba numeric vector of lower bounds
x_uba numeric vector of upper bounds
settingslist with optimization parameters
dense_backenda flag indicating if the dense solver is to be used
dimsthe dimensions of the problem, a named list containing
n,pandm.
Method update()
Update the current piqp_model with new data
Usage
piqp_model$update(
P = NULL,
c = NULL,
A = NULL,
b = NULL,
G = NULL,
h = NULL,
x_lb = NULL,
x_ub = NULL
)Arguments
Pdense or sparse matrix of class dgCMatrix or coercible into such, must be positive semidefinite
cnumeric vector
Adense or sparse matrix of class dgCMatrix or coercible into such
bnumeric vector
Gdense or sparse matrix of class dgCMatrix or coercible into such
hnumeric vector
x_lba numeric vector of lower bounds
x_uba numeric vector of upper bounds
settingslist with optimization parameters
dense_backenda flag indicating if the dense solver is to be used
dimsthe dimensions of the problem, a named list containing
n,pandm.
Method update_settings()
Update the current settings with new values for this model
Usage
piqp_model$update_settings(new_settings = list())Arguments
new_settingsa list of named values for settings, default empty list; see
piqp_settings()for a comprehensive list of defaults
