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$new(
P,
c,
A,
b,
G,
h,
x_lb,
x_ub,
settings = list(),
dense_backend,
dims
)
Arguments
P
dense or sparse matrix of class dgCMatrix or coercible into such, must be positive semidefinite
c
numeric vector
A
dense or sparse matrix of class dgCMatrix or coercible into such
b
numeric vector
G
dense or sparse matrix of class dgCMatrix or coercible into such
h
numeric vector
x_lb
a numeric vector of lower bounds
x_ub
a numeric vector of upper bounds
settings
list with optimization parameters
dense_backend
a flag indicating if the dense solver is to be used
dims
the dimensions of the problem, a named list containing
n
,p
andm
.
Method update()
Update the current piqp_model with new data
Usage
piqp_model$update(
P = NULL,
c = NULL,
A = NULL,
b = NULL,
G = NULL,
h = NULL,
x_lb = NULL,
x_ub = NULL
)
Arguments
P
dense or sparse matrix of class dgCMatrix or coercible into such, must be positive semidefinite
c
numeric vector
A
dense or sparse matrix of class dgCMatrix or coercible into such
b
numeric vector
G
dense or sparse matrix of class dgCMatrix or coercible into such
h
numeric vector
x_lb
a numeric vector of lower bounds
x_ub
a numeric vector of upper bounds
settings
list with optimization parameters
dense_backend
a flag indicating if the dense solver is to be used
dims
the dimensions of the problem, a named list containing
n
,p
andm
.
Method update_settings()
Update the current settings with new values for this model
Usage
piqp_model$update_settings(new_settings = list())
Arguments
new_settings
a list of named values for settings, default empty list; see
piqp_settings()
for a comprehensive list of defaults