josiann.parallel.moves.base.ParallelMove

class josiann.parallel.moves.base.ParallelMove(*, bounds=None, repr_attributes=(), **kwargs)[source]

Base class for building moves that solve optimization problems in parallel.

Instantiate a Move.

Parameters:

bounds (Optional[ndarray[Any, dtype[Union[float64, int64]]]] (default: None)) – optional sequence of (min, max) bounds for values to propose in each dimension.
repr_attributes (tuple[str, ...] (default: ())) – list of attribute names to include in the string representation of this Move.
kwargs (Any) –

Methods

`ParallelMove.__init__`	Instantiate a Move.
`ParallelMove.get_proposal`	Generate a new proposed vector x.
`ParallelMove.set_bounds`	Set bounds for the move.

Methods

ParallelMove.__init__(*, bounds=None, repr_attributes=(), **kwargs)

Instantiate a Move.

Parameters:

bounds (Optional[ndarray[Any, dtype[Union[float64, int64]]]] (default: None)) – optional sequence of (min, max) bounds for values to propose in each dimension.

repr_attributes (tuple[str, ...] (default: ())) – list of attribute names to include in the string representation of this Move.

kwargs (Any) –

ParallelMove.get_proposal(x, state)

Generate a new proposed vector x.

Parameters:

x (ndarray[Any, dtype[TypeVar(DT_ARR, bound= Union[float64, int64])]]) – current vector x of shape (ndim,).

state (State) – current state of the SA algorithm.

Return type:

ndarray[Any, dtype[TypeVar(DT_ARR, bound= Union[float64, int64])]]

Returns:

New proposed vector x of shape (ndim,).

ParallelMove.set_bounds(bounds)

Set bounds for the move.

Parameters:

bounds (Union[tuple[float, float], Sequence[tuple[float, float]], None]) – sequence of (min, max) bounds for values to propose in each dimension.

Return type:

None