pybnb.common

Basic definitions and utilities.

Copyright by Gabriel A. Hackebeil (gabe.hackebeil@gmail.com).

class pybnb.common.ProblemSense(value)

An enumeration.

minimize = 1

The objective sense defining a minimization problem.

maximize = -1

The objective sense defining a maximization problem.

pybnb.common.inf = inf

A floating point constant set to float('inf').

pybnb.common.nan = nan

A floating point constant set to float('nan').

class pybnb.common.QueueStrategy(value)

Strategies for prioritizing nodes in the central dispatcher queue. For all strategies, ties are broken by insertion order.

bound = 'bound'

The node with the worst bound is always selected next.

objective = 'objective'

The node with the best objective is always selected next.

breadth = 'breadth'

The node with the smallest tree depth is always selected next (i.e., breadth-first search).

depth = 'depth'

The node with the largest tree depth is always selected next (i.e., depth-first search).

local_gap = 'local_gap'

The node with the largest gap between its local objective and bound is always selected next.

fifo = 'fifo'

Nodes are served in first-in, first-out order.

lifo = 'lifo'

Nodes are served in last-in, first-out order.

random = 'random'

Nodes are assigned a random priority before entering the queue.

custom = 'custom'

The node with the largest value stored in the queue_priority attribute is always selected next. Users are expected to assign a priority to all nodes returned from the branch method on their problem.

class pybnb.common.SolutionStatus(value)

Possible values assigned to the solution_status attribute of a SolverResults object returned from a solve.

optimal = 'optimal'

Indicates that the best objective is finite and close enough to the global bound to satisfy the optimality tolerances used for the solve.

feasible = 'feasible'

Indicates that the best objective is finite but not close enough to the global bound to satisfy the optimality tolerances used for the solve.

infeasible = 'infeasible'

Indicates that both the best objective and global bound are equal to the infeasible objective value (+inf or -inf depending on the sense).

unbounded = 'unbounded'

Indicates that both the best objective and global bound are equal to the unbounded objective value (+inf or -inf depending on the sense).

invalid = 'invalid'

Indicates that the global bound is not a valid bound on the best objective found. This may be due to an ill-defined problem or other numerical issues.

unknown = 'unknown'

Indicates that the global bound is finite, but no feasible (finite) objective was found.

class pybnb.common.TerminationCondition(value)

Possible values assigned to the termination_condition attribute of a SolverResults object returned from a solve.

optimality = 'optimality'

The dispatcher terminated the solve based on optimality criteria.

objective_limit = 'objective_limit'

The dispatcher terminated the solve based on the user-supplied limit on the objective or bound being satisfied.

node_limit = 'node_limit'

The dispatcher terminated the solve due to the user-supplied explored node limit being surpassed.

time_limit = 'time_limit'

The dispatcher terminated the solve due to the user-supplied time limit being surpassed.

queue_empty = 'queue_empty'

The dispatcher terminated the solve due to the node queue becoming empty.

queue_limit = 'queue_limit'

The dispatcher terminated the solve due to the user-supplied queue size limit being exceeded.

interrupted = 'interrupted'

Solve termination was initiated by SIGINT or SIGUSR signal event.