pympcc.IterationInfo¶

class pympcc.IterationInfo(epsilon, x, obj, status, message, comp_residual, comp_residual_mean, n_ipopt_iter, iter_time, kkt_residual=None, restoration_iter_count=0, entered_restoration=False)[source]¶

Bases: object

Diagnostic snapshot for one NLP solve in an iterative strategy.

Parameters:

epsilon (float)
x (ndarray)
obj (float)
status (int)
message (str)
comp_residual (float)
comp_residual_mean (float)
n_ipopt_iter (int)
iter_time (float)
kkt_residual (float | None)
restoration_iter_count (int)
entered_restoration (bool)

__init__(epsilon, x, obj, status, message, comp_residual, comp_residual_mean, n_ipopt_iter, iter_time, kkt_residual=None, restoration_iter_count=0, entered_restoration=False)¶

Parameters:

epsilon (float)
x (ndarray)
obj (float)
status (int)
message (str)
comp_residual (float)
comp_residual_mean (float)
n_ipopt_iter (int)
iter_time (float)
kkt_residual (float | None)
restoration_iter_count (int)
entered_restoration (bool)

Return type:

None

Methods

__init__(epsilon, x, obj, status, message, ...)

Attributes

`entered_restoration`
`kkt_residual`
`restoration_iter_count`
`epsilon`
`x`
`obj`
`status`
`message`
`comp_residual`
`comp_residual_mean`
`n_ipopt_iter`
`iter_time`

epsilon: float¶

x: ndarray¶

obj: float¶

status: int¶

message: str¶

comp_residual: float¶

comp_residual_mean: float¶

n_ipopt_iter: int¶

iter_time: float¶

kkt_residual: float | None = None¶

restoration_iter_count: int = 0¶

entered_restoration: bool = False¶