bareon-allocator/bareon_allocator/solvers/linear_program.py
Evgeniy L b1cef8c4a0 Refactoring of allocation solvers.
Implemented layering so it will allow to have multiple
solver engines.

Implements blueprint: dynamic-allocation

Change-Id: I7ed1ec0216fb9778b4fa5be4fb4f6141a0e26fc9
2016-04-13 15:11:56 +03:00

98 lines
3.5 KiB
Python

# -*- coding: utf-8 -*-
# Copyright 2016 Mirantis, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
class LinearProgram(object):
"""LinearProgram object is abstract way to describe linear program."""
MAXIMIZE = 'maximize'
MINIMIZE = 'minimize'
# Linear Program
LP_TYPE_LP = 'lp'
# Mixed Integer Program
LP_TYPE_MIP = 'mip'
def __init__(
self,
x_amount=0,
optimization_type=MAXIMIZE,
lp_type=LP_TYPE_LP,
objective_function_coefficients=None,
equality_constraint_matrix=None,
lower_constraint_matrix=None,
upper_constraint_matrix=None,
equality_constraint_vector=None,
lower_constraint_vector=None,
upper_constraint_vector=None):
self.lp_type = lp_type
self.objective_function_optimization_type = optimization_type
# Coefficients of the linear objective minimization function.
# During iteration over vertexes the function is used to identify
# if current solution (vertex) satisfies the equation more, than
# previous one.
# Example of equation: c[0]*x1 + c[1]*x2
self.objective_function_coefficients = objective_function_coefficients
# Matrices which, gives values of the equality/inequality
# constraints, when multiplied by x.
self.equality_constraint_matrix = equality_constraint_matrix
self.lower_constraint_matrix = lower_constraint_matrix
self.upper_constraint_matrix = upper_constraint_matrix
# Vectors in combination with equality matrices give
# equality/inequality system of linear equations.
self.equality_constraint_vector = equality_constraint_vector
self.lower_constraint_vector = lower_constraint_vector
self.upper_constraint_vector = upper_constraint_vector
# Amount unknown of variables.
self.x_amount = x_amount
# A list of tuples which represents min and max possible values for
# each variable.
self.bounds = [(0, None) for _ in xrange(self.x_amount)]
def minimize_objective_function(self):
"""Minimize objective function."""
self.objective_function_optimization_type = self.MINIMIZE
def maximize_objective_function(self):
"""Maximize objective function."""
self.objective_function_optimization_type = self.MAXIMIZE
def set_type_lp(self):
"""Set type of linear program to Linear Program.
Is default, produces real number result, without any integer
constraints.
"""
self.lp_type = self.LP_TYPE_LP
def set_type_mip(self):
""""Set type of linear program to Mixed Integer Program.
This type may include integer constraints, as result wider range of
operations may be available.
Note: Not all linear programming solvers support this type.
See: https://en.wikipedia.org/wiki/Integer_programming
"""
self.lp_type = self.LP_TYPE_MIP