109 lines
4.2 KiB
Python
109 lines
4.2 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright 2016 Mirantis, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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from scipy.optimize import linprog
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from bareon_allocator import errors
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from bareon_allocator.solvers import BaseSolver
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from bareon_allocator.solvers import utils
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class LinearProgrammingScipySolver(BaseSolver):
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"""Linear programming allocator.
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Use Linear Programming method [0] (the method itself has nothing to do
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with computer-programming) in order to formulate and solve the problem
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of spaces allocation on disks, with the best outcome.
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In this implementation scipy is being used since it already implements
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simplex algorithm to find the best feasible solution.
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[0] https://en.wikipedia.org/wiki/Linear_programming
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[1] http://docs.scipy.org/doc/scipy-0.16.0/reference/generated
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/scipy.optimize.linprog.html
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[2] https://en.wikipedia.org/wiki/Simplex_algorithm
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"""
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def solve(self):
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"""Solves linear program.
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:return: solution vector
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"""
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lp_solution = linprog(
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self.linear_program.objective_function_coefficients,
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A_eq=self.linear_program.equality_constraint_matrix or None,
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b_eq=self.linear_program.equality_constraint_vector or None,
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A_ub=self._make_upper_constraint_matrix() or None,
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b_ub=self._make_upper_constraint_vector() or None,
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bounds=self.linear_program.bounds,
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options={"disp": False})
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self._check_errors(lp_solution)
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# Naive implementation of getting integer result
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# from a linear programming algorithm, MIP
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# (mixed integer programming) should be considered
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# instead, but it may have a lot of problems (solution
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# of such equations is NP-hard in some cases),
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# for our practical purposes it's enough to round
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# the number down, in this case we may get `n` megabytes
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# unallocated, where n is len(spaces) * len(disks)
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solution_vector = utils.round_vector_down(lp_solution.x)
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return solution_vector
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def _check_errors(self, solution):
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"""Checks if solution is not found.
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:param solution: solution object from scipy
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:raises: errors.NoSolutionFound if solution is not found
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"""
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if not solution.success:
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raise errors.NoSolutionFound(
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'Allocation is not possible '
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'with specified constraints: {0}'.format(solution.message))
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def _make_upper_constraint_matrix(self):
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"""Merges lower constraint matrix into upper."""
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upper_constraint_matrix = []
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if self.linear_program.upper_constraint_matrix:
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upper_constraint_matrix.extend(
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self.linear_program.upper_constraint_matrix)
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if self.linear_program.lower_constraint_matrix:
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# Swap sign for lower constraint matrix in order to make it
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# upper bound instead of lower bound
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upper_constraint_matrix.extend(
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[-i for i in row] for row in
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self.linear_program.lower_constraint_matrix)
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return upper_constraint_matrix
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def _make_upper_constraint_vector(self):
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"""Merges lower constraint vector into upper."""
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upper_constraint_vector = []
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if self.linear_program.upper_constraint_vector:
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upper_constraint_vector.extend(
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self.linear_program.upper_constraint_vector)
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if self.linear_program.lower_constraint_vector:
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# Swap sign for items in the vector to make it upper bound
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# instead of lower bound
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upper_constraint_vector.extend(
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[-i for i in self.linear_program.lower_constraint_vector])
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return upper_constraint_vector
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