decogo.solver.refactory_colgen

Implements Column Generation with user-defined input model

Classes

RefactoryColGen(problem, settings, result)

This class implements Column Generation (CG) algorithm with user-defined input model

class decogo.solver.refactory_colgen.RefactoryColGen(problem, settings, result)

This class implements Column Generation (CG) algorithm with user-defined input model

Parameters:

• problem (DecomposedProblem) – Decomposed problem class, which stores all input data

• settings (Settings) – Settings class

• result (Results) – Class which stores the results

__init__(problem, settings, result)

Constructor method

solve()

Inner approximation algorithm

ia_init(duals=None)

Initialization of inner approximation

Parameters:

duals (ndarray) – Initial dual vector for initialization

column_generation(subset_of_blocks=None, approx_solver=False)

Performs column generation steps (see paper)

Parameters:

• subset_of_blocks (list or None) – apply column generation for reduced block set

• approx_solver (bool) – enables approximate solving of subproblems in column generation

Returns:

Dual solution from IA master problem regarding global constraints

Return type:

ndarray

column_generation_fast_fw()

Performs fast FW column generation steps (see paper)

sub_gradient(direction_vector)

Performs sub-gradient iterations

Parameters:

direction_vector (ndarray) – Initial vector for sub-gradient iterations

Returns:

Final direction vector

Return type:

tuple

generate_column(block_id, direction, heuristic=True, approx_solver=False, x_k=None)

Generates the inner point (and corresponding column) either with MINLP sub-problem or with NLP sub-problem (too heuristically); adds valid local linear cut to heu_oa_master_problem if any

Parameters:

• block_id (int) – Block identifier

• direction (ndarray) – Direction in image space

• heuristic (bool) – Indicates if the sub-problem must be solved heuristically

• approx_solver (bool) – enables approximate solving of subproblems in column generation

• x_k (BlockVector or None) – start_point for solving subproblems

Returns:

Inner point, primal bound, reduced cost of new point, bool value indicating whether new point is generated and corresponding column

Return type:

tuple

global_solve_subproblem(block_id, dir_im_space, compute_reduced_cost=True, x_k=None)

Solves subproblem, adds inner point, (either in compact form or in the original) and computes reduced cost for the new inner point

Parameters:

• block_id (int) – Block identifier

• dir_im_space (ndarray) – Direction in image space

• compute_reduced_cost (bool) – Indicates if reduced cost has to be computed

• heuristic (bool) – Indicates if the sub-problem must be solved heuristically

Returns:

Inner point (feasible point), reduced cost value, primal bound of the sub-problem, dual bound of the sub-problem, bool value indicating whether new inner point was generated, corresponding column to the inner point

Return type:

tuple

get_slack_directions(slacks)

Computes new direction based on the slack values of IA master problem

Parameters:

slacks (list) – Slack values stored as a list of tuples

Returns:

New direction in image space

Return type:

ndarray

local_solve_subproblem(block_id, dir_im_space, x_k=None)

Solves MINLP subproblem approximately, adds inner point (either in compact form or in the original) and computes reduced cost for the new inner point

Parameters:

• block_id (int) – Block identifier

• dir_im_space (ndarray) – Direction in image space

• x_k (BlockVector or None) – start_point for solving subproblems

Returns:

Inner point (feasible point), reduced cost value, primal bound of the sub-problem, dual bound of the sub-problem, bool value indicating whether new inner point was generated, corresponding column to the inner point

Return type:

tuple

find_solution_init(iter_index=None)

Method to generate a feasible solution and therefore reducing the slacks to zero in innerMaster problem

Parameters:

iter_index – number of main iteration when the method is called

Type:

int

find_solution(iter_index=None)

Primal heuristics method

Parameters:

iter_index – number of main iteration when the method is called

Type:

int

print_z_values(z)

Prints sorted z-weights blockwise

Parameters:

z (BlockVector) – Weight vector

fw_column_generation(check_w, duals, t_set)

Performs fast FW column generation steps (see paper)

get_active_block(dir_im_space, t_set=None)

fast_solve_atomic_sub_problem(block_id, dir_im_space, w_k)

Solves MINLP subproblem approximately, adds inner point (either in compact form or in the original) and computes reduced cost for the new inner point

Parameters:

• block_id (int) – Block identifier

• dir_im_space (ndarray) – Direction in image space

• w_k (ndarray) – given column

Returns:

Inner point (feasible point), primal bound of the sub-problem, dual bound of the sub-problem, bool value indicating whether new inner point was generated, corresponding column to the inner point

Return type:

tuple

__abstractmethods__ = frozenset({})

_abc_impl = <_abc_data object>