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678 lines
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<article id="content">
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<header>
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<h1 class="title">Module <code>pywrapknapsack_solver</code></h1>
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</header>
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<section id="section-intro">
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python"># This file was automatically generated by SWIG (http://www.swig.org).
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# Version 4.0.1
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#
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# Do not make changes to this file unless you know what you are doing--modify
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# the SWIG interface file instead.
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from sys import version_info as _swig_python_version_info
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if _swig_python_version_info < (2, 7, 0):
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raise RuntimeError("Python 2.7 or later required")
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# Import the low-level C/C++ module
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if __package__ or "." in __name__:
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from . import _pywrapknapsack_solver
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else:
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import _pywrapknapsack_solver
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try:
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import builtins as __builtin__
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except ImportError:
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import __builtin__
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def _swig_repr(self):
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try:
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strthis = "proxy of " + self.this.__repr__()
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except __builtin__.Exception:
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strthis = ""
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return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,)
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def _swig_setattr_nondynamic_instance_variable(set):
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def set_instance_attr(self, name, value):
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if name == "thisown":
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self.this.own(value)
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elif name == "this":
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set(self, name, value)
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elif hasattr(self, name) and isinstance(getattr(type(self), name), property):
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set(self, name, value)
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else:
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raise AttributeError("You cannot add instance attributes to %s" % self)
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return set_instance_attr
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def _swig_setattr_nondynamic_class_variable(set):
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def set_class_attr(cls, name, value):
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if hasattr(cls, name) and not isinstance(getattr(cls, name), property):
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set(cls, name, value)
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else:
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raise AttributeError("You cannot add class attributes to %s" % cls)
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return set_class_attr
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def _swig_add_metaclass(metaclass):
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"""Class decorator for adding a metaclass to a SWIG wrapped class - a slimmed down version of six.add_metaclass"""
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def wrapper(cls):
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return metaclass(cls.__name__, cls.__bases__, cls.__dict__.copy())
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return wrapper
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class _SwigNonDynamicMeta(type):
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"""Meta class to enforce nondynamic attributes (no new attributes) for a class"""
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__setattr__ = _swig_setattr_nondynamic_class_variable(type.__setattr__)
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class KnapsackSolver(object):
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r"""
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This library solves knapsack problems.
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Problems the library solves include:
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- 0-1 knapsack problems,
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||
- Multi-dimensional knapsack problems,
|
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Given n items, each with a profit and a weight, given a knapsack of
|
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capacity c, the goal is to find a subset of items which fits inside c
|
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and maximizes the total profit.
|
||
The knapsack problem can easily be extended from 1 to d dimensions.
|
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As an example, this can be useful to constrain the maximum number of
|
||
items inside the knapsack.
|
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Without loss of generality, profits and weights are assumed to be positive.
|
||
|
||
From a mathematical point of view, the multi-dimensional knapsack problem
|
||
can be modeled by d linear constraints:
|
||
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ForEach(j:1..d)(Sum(i:1..n)(weight_ij * item_i) <= c_j
|
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where item_i is a 0-1 integer variable.
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Then the goal is to maximize:
|
||
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Sum(i:1..n)(profit_i * item_i).
|
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|
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There are several ways to solve knapsack problems. One of the most
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efficient is based on dynamic programming (mainly when weights, profits
|
||
and dimensions are small, and the algorithm runs in pseudo polynomial time).
|
||
Unfortunately, when adding conflict constraints the problem becomes strongly
|
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NP-hard, i.e. there is no pseudo-polynomial algorithm to solve it.
|
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That's the reason why the most of the following code is based on branch and
|
||
bound search.
|
||
|
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For instance to solve a 2-dimensional knapsack problem with 9 items,
|
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one just has to feed a profit vector with the 9 profits, a vector of 2
|
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vectors for weights, and a vector of capacities.
|
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E.g.:
|
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**Python**:
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.. code-block:: c++
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profits = [ 1, 2, 3, 4, 5, 6, 7, 8, 9 ]
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weights = [ [ 1, 2, 3, 4, 5, 6, 7, 8, 9 ],
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[ 1, 1, 1, 1, 1, 1, 1, 1, 1 ]
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]
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capacities = [ 34, 4 ]
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solver = pywrapknapsack_solver.KnapsackSolver(
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pywrapknapsack_solver.KnapsackSolver
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.KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER,
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'Multi-dimensional solver')
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solver.Init(profits, weights, capacities)
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profit = solver.Solve()
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**C++**:
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.. code-block:: c++
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const std::vectorint64 profits = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
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const std::vectorstd::vector<int64 weights =
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{ { 1, 2, 3, 4, 5, 6, 7, 8, 9 },
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{ 1, 1, 1, 1, 1, 1, 1, 1, 1 } };
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const std::vectorint64 capacities = { 34, 4 };
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KnapsackSolver solver(
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KnapsackSolver::KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER,
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"Multi-dimensional solver");
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solver.Init(profits, weights, capacities);
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const int64 profit = solver.Solve();
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**Java**:
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||
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.. code-block:: c++
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||
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final long[] profits = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
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final long[][] weights = { { 1, 2, 3, 4, 5, 6, 7, 8, 9 },
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{ 1, 1, 1, 1, 1, 1, 1, 1, 1 } };
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final long[] capacities = { 34, 4 };
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||
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KnapsackSolver solver = new KnapsackSolver(
|
||
KnapsackSolver.SolverType.KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER,
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"Multi-dimensional solver");
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solver.init(profits, weights, capacities);
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final long profit = solver.solve();
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||
"""
|
||
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||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
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__repr__ = _swig_repr
|
||
KNAPSACK_BRUTE_FORCE_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_BRUTE_FORCE_SOLVER
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r"""
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Brute force method.
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||
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Limited to 30 items and one dimension, this
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solver uses a brute force algorithm, ie. explores all possible states.
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Experiments show competitive performance for instances with less than
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15 items.
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"""
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||
KNAPSACK_64ITEMS_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_64ITEMS_SOLVER
|
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r"""
|
||
Optimized method for single dimension small problems
|
||
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||
Limited to 64 items and one dimension, this
|
||
solver uses a branch & bound algorithm. This solver is about 4 times
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||
faster than KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER.
|
||
"""
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||
KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER
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r"""
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Dynamic Programming approach for single dimension problems
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Limited to one dimension, this solver is based on a dynamic programming
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algorithm. The time and space complexity is O(capacity *
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number_of_items).
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"""
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KNAPSACK_MULTIDIMENSION_CBC_MIP_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_MULTIDIMENSION_CBC_MIP_SOLVER
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r"""
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CBC Based Solver
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This solver can deal with both large number of items and several
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dimensions. This solver is based on Integer Programming solver CBC.
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"""
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KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER
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r"""
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Generic Solver.
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||
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This solver can deal with both large number of items and several
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dimensions. This solver is based on branch and bound.
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"""
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def __init__(self, *args):
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_pywrapknapsack_solver.KnapsackSolver_swiginit(self, _pywrapknapsack_solver.new_KnapsackSolver(*args))
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__swig_destroy__ = _pywrapknapsack_solver.delete_KnapsackSolver
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def Init(self, profits: "std::vector< int64 > const &", weights: "std::vector< std::vector< int64 > > const &", capacities: "std::vector< int64 > const &") -> "void":
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r"""Initializes the solver and enters the problem to be solved."""
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return _pywrapknapsack_solver.KnapsackSolver_Init(self, profits, weights, capacities)
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def Solve(self) -> "int64":
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r"""Solves the problem and returns the profit of the optimal solution."""
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return _pywrapknapsack_solver.KnapsackSolver_Solve(self)
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def BestSolutionContains(self, item_id: "int") -> "bool":
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r"""Returns true if the item 'item_id' is packed in the optimal knapsack."""
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return _pywrapknapsack_solver.KnapsackSolver_BestSolutionContains(self, item_id)
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def set_use_reduction(self, use_reduction: "bool") -> "void":
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return _pywrapknapsack_solver.KnapsackSolver_set_use_reduction(self, use_reduction)
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def set_time_limit(self, time_limit_seconds: "double") -> "void":
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r"""
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Time limit in seconds.
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When a finite time limit is set the solution obtained might not be optimal
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if the limit is reached.
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"""
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return _pywrapknapsack_solver.KnapsackSolver_set_time_limit(self, time_limit_seconds)
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# Register KnapsackSolver in _pywrapknapsack_solver:
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_pywrapknapsack_solver.KnapsackSolver_swigregister(KnapsackSolver)</code></pre>
|
||
</details>
|
||
</section>
|
||
<section>
|
||
</section>
|
||
<section>
|
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</section>
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<section>
|
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</section>
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<section>
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<h2 class="section-title" id="header-classes">Classes</h2>
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<dl>
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<dt id="pywrapknapsack_solver.KnapsackSolver"><code class="flex name class">
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<span>class <span class="ident">KnapsackSolver</span></span>
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<span>(</span><span>*args)</span>
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</code></dt>
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<dd>
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<div class="desc"><p>This library solves knapsack problems.</p>
|
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<p>Problems the library solves include:
|
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- 0-1 knapsack problems,
|
||
- Multi-dimensional knapsack problems,</p>
|
||
<p>Given n items, each with a profit and a weight, given a knapsack of
|
||
capacity c, the goal is to find a subset of items which fits inside c
|
||
and maximizes the total profit.
|
||
The knapsack problem can easily be extended from 1 to d dimensions.
|
||
As an example, this can be useful to constrain the maximum number of
|
||
items inside the knapsack.
|
||
Without loss of generality, profits and weights are assumed to be positive.</p>
|
||
<p>From a mathematical point of view, the multi-dimensional knapsack problem
|
||
can be modeled by d linear constraints:</p>
|
||
<pre><code>ForEach(j:1..d)(Sum(i:1..n)(weight_ij * item_i) <= c_j
|
||
where item_i is a 0-1 integer variable.
|
||
</code></pre>
|
||
<p>Then the goal is to maximize:</p>
|
||
<pre><code>Sum(i:1..n)(profit_i * item_i).
|
||
</code></pre>
|
||
<p>There are several ways to solve knapsack problems. One of the most
|
||
efficient is based on dynamic programming (mainly when weights, profits
|
||
and dimensions are small, and the algorithm runs in pseudo polynomial time).
|
||
Unfortunately, when adding conflict constraints the problem becomes strongly
|
||
NP-hard, i.e. there is no pseudo-polynomial algorithm to solve it.
|
||
That's the reason why the most of the following code is based on branch and
|
||
bound search.</p>
|
||
<p>For instance to solve a 2-dimensional knapsack problem with 9 items,
|
||
one just has to feed a profit vector with the 9 profits, a vector of 2
|
||
vectors for weights, and a vector of capacities.
|
||
E.g.:</p>
|
||
<p><strong>Python</strong>:</p>
|
||
<p>.. code-block:: c++</p>
|
||
<pre><code> profits = [ 1, 2, 3, 4, 5, 6, 7, 8, 9 ]
|
||
weights = [ [ 1, 2, 3, 4, 5, 6, 7, 8, 9 ],
|
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[ 1, 1, 1, 1, 1, 1, 1, 1, 1 ]
|
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]
|
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capacities = [ 34, 4 ]
|
||
|
||
solver = pywrapknapsack_solver.KnapsackSolver(
|
||
pywrapknapsack_solver.KnapsackSolver
|
||
.KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER,
|
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'Multi-dimensional solver')
|
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solver.Init(profits, weights, capacities)
|
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profit = solver.Solve()
|
||
</code></pre>
|
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<p><strong>C++</strong>:</p>
|
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<p>.. code-block:: c++</p>
|
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<pre><code> const std::vectorint64 profits = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
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const std::vectorstd::vector<int64 weights =
|
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{ { 1, 2, 3, 4, 5, 6, 7, 8, 9 },
|
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{ 1, 1, 1, 1, 1, 1, 1, 1, 1 } };
|
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const std::vectorint64 capacities = { 34, 4 };
|
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|
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KnapsackSolver solver(
|
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KnapsackSolver::KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER,
|
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"Multi-dimensional solver");
|
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solver.Init(profits, weights, capacities);
|
||
const int64 profit = solver.Solve();
|
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</code></pre>
|
||
<p><strong>Java</strong>:</p>
|
||
<p>.. code-block:: c++</p>
|
||
<pre><code> final long[] profits = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
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final long[][] weights = { { 1, 2, 3, 4, 5, 6, 7, 8, 9 },
|
||
{ 1, 1, 1, 1, 1, 1, 1, 1, 1 } };
|
||
final long[] capacities = { 34, 4 };
|
||
|
||
KnapsackSolver solver = new KnapsackSolver(
|
||
KnapsackSolver.SolverType.KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER,
|
||
"Multi-dimensional solver");
|
||
solver.init(profits, weights, capacities);
|
||
final long profit = solver.solve();
|
||
</code></pre></div>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">class KnapsackSolver(object):
|
||
r"""
|
||
This library solves knapsack problems.
|
||
|
||
Problems the library solves include:
|
||
- 0-1 knapsack problems,
|
||
- Multi-dimensional knapsack problems,
|
||
|
||
Given n items, each with a profit and a weight, given a knapsack of
|
||
capacity c, the goal is to find a subset of items which fits inside c
|
||
and maximizes the total profit.
|
||
The knapsack problem can easily be extended from 1 to d dimensions.
|
||
As an example, this can be useful to constrain the maximum number of
|
||
items inside the knapsack.
|
||
Without loss of generality, profits and weights are assumed to be positive.
|
||
|
||
From a mathematical point of view, the multi-dimensional knapsack problem
|
||
can be modeled by d linear constraints:
|
||
|
||
ForEach(j:1..d)(Sum(i:1..n)(weight_ij * item_i) <= c_j
|
||
where item_i is a 0-1 integer variable.
|
||
|
||
Then the goal is to maximize:
|
||
|
||
Sum(i:1..n)(profit_i * item_i).
|
||
|
||
There are several ways to solve knapsack problems. One of the most
|
||
efficient is based on dynamic programming (mainly when weights, profits
|
||
and dimensions are small, and the algorithm runs in pseudo polynomial time).
|
||
Unfortunately, when adding conflict constraints the problem becomes strongly
|
||
NP-hard, i.e. there is no pseudo-polynomial algorithm to solve it.
|
||
That's the reason why the most of the following code is based on branch and
|
||
bound search.
|
||
|
||
For instance to solve a 2-dimensional knapsack problem with 9 items,
|
||
one just has to feed a profit vector with the 9 profits, a vector of 2
|
||
vectors for weights, and a vector of capacities.
|
||
E.g.:
|
||
|
||
**Python**:
|
||
|
||
.. code-block:: c++
|
||
|
||
profits = [ 1, 2, 3, 4, 5, 6, 7, 8, 9 ]
|
||
weights = [ [ 1, 2, 3, 4, 5, 6, 7, 8, 9 ],
|
||
[ 1, 1, 1, 1, 1, 1, 1, 1, 1 ]
|
||
]
|
||
capacities = [ 34, 4 ]
|
||
|
||
solver = pywrapknapsack_solver.KnapsackSolver(
|
||
pywrapknapsack_solver.KnapsackSolver
|
||
.KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER,
|
||
'Multi-dimensional solver')
|
||
solver.Init(profits, weights, capacities)
|
||
profit = solver.Solve()
|
||
|
||
**C++**:
|
||
|
||
.. code-block:: c++
|
||
|
||
const std::vectorint64 profits = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
|
||
const std::vectorstd::vector<int64 weights =
|
||
{ { 1, 2, 3, 4, 5, 6, 7, 8, 9 },
|
||
{ 1, 1, 1, 1, 1, 1, 1, 1, 1 } };
|
||
const std::vectorint64 capacities = { 34, 4 };
|
||
|
||
KnapsackSolver solver(
|
||
KnapsackSolver::KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER,
|
||
"Multi-dimensional solver");
|
||
solver.Init(profits, weights, capacities);
|
||
const int64 profit = solver.Solve();
|
||
|
||
**Java**:
|
||
|
||
.. code-block:: c++
|
||
|
||
final long[] profits = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
|
||
final long[][] weights = { { 1, 2, 3, 4, 5, 6, 7, 8, 9 },
|
||
{ 1, 1, 1, 1, 1, 1, 1, 1, 1 } };
|
||
final long[] capacities = { 34, 4 };
|
||
|
||
KnapsackSolver solver = new KnapsackSolver(
|
||
KnapsackSolver.SolverType.KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER,
|
||
"Multi-dimensional solver");
|
||
solver.init(profits, weights, capacities);
|
||
final long profit = solver.solve();
|
||
"""
|
||
|
||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
|
||
__repr__ = _swig_repr
|
||
KNAPSACK_BRUTE_FORCE_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_BRUTE_FORCE_SOLVER
|
||
r"""
|
||
Brute force method.
|
||
|
||
Limited to 30 items and one dimension, this
|
||
solver uses a brute force algorithm, ie. explores all possible states.
|
||
Experiments show competitive performance for instances with less than
|
||
15 items.
|
||
"""
|
||
KNAPSACK_64ITEMS_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_64ITEMS_SOLVER
|
||
r"""
|
||
Optimized method for single dimension small problems
|
||
|
||
Limited to 64 items and one dimension, this
|
||
solver uses a branch & bound algorithm. This solver is about 4 times
|
||
faster than KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER.
|
||
"""
|
||
KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER
|
||
r"""
|
||
Dynamic Programming approach for single dimension problems
|
||
|
||
Limited to one dimension, this solver is based on a dynamic programming
|
||
algorithm. The time and space complexity is O(capacity *
|
||
number_of_items).
|
||
"""
|
||
KNAPSACK_MULTIDIMENSION_CBC_MIP_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_MULTIDIMENSION_CBC_MIP_SOLVER
|
||
r"""
|
||
CBC Based Solver
|
||
|
||
This solver can deal with both large number of items and several
|
||
dimensions. This solver is based on Integer Programming solver CBC.
|
||
"""
|
||
KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER
|
||
r"""
|
||
Generic Solver.
|
||
|
||
This solver can deal with both large number of items and several
|
||
dimensions. This solver is based on branch and bound.
|
||
"""
|
||
|
||
def __init__(self, *args):
|
||
_pywrapknapsack_solver.KnapsackSolver_swiginit(self, _pywrapknapsack_solver.new_KnapsackSolver(*args))
|
||
__swig_destroy__ = _pywrapknapsack_solver.delete_KnapsackSolver
|
||
|
||
def Init(self, profits: "std::vector< int64 > const &", weights: "std::vector< std::vector< int64 > > const &", capacities: "std::vector< int64 > const &") -> "void":
|
||
r"""Initializes the solver and enters the problem to be solved."""
|
||
return _pywrapknapsack_solver.KnapsackSolver_Init(self, profits, weights, capacities)
|
||
|
||
def Solve(self) -> "int64":
|
||
r"""Solves the problem and returns the profit of the optimal solution."""
|
||
return _pywrapknapsack_solver.KnapsackSolver_Solve(self)
|
||
|
||
def BestSolutionContains(self, item_id: "int") -> "bool":
|
||
r"""Returns true if the item 'item_id' is packed in the optimal knapsack."""
|
||
return _pywrapknapsack_solver.KnapsackSolver_BestSolutionContains(self, item_id)
|
||
|
||
def set_use_reduction(self, use_reduction: "bool") -> "void":
|
||
return _pywrapknapsack_solver.KnapsackSolver_set_use_reduction(self, use_reduction)
|
||
|
||
def set_time_limit(self, time_limit_seconds: "double") -> "void":
|
||
r"""
|
||
Time limit in seconds.
|
||
|
||
When a finite time limit is set the solution obtained might not be optimal
|
||
if the limit is reached.
|
||
"""
|
||
return _pywrapknapsack_solver.KnapsackSolver_set_time_limit(self, time_limit_seconds)</code></pre>
|
||
</details>
|
||
<h3>Class variables</h3>
|
||
<dl>
|
||
<dt id="pywrapknapsack_solver.KnapsackSolver.KNAPSACK_64ITEMS_SOLVER"><code class="name">var <span class="ident">KNAPSACK_64ITEMS_SOLVER</span></code></dt>
|
||
<dd>
|
||
<div class="desc"><p>Optimized method for single dimension small problems</p>
|
||
<p>Limited to 64 items and one dimension, this
|
||
solver uses a branch & bound algorithm. This solver is about 4 times
|
||
faster than KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER.</p></div>
|
||
</dd>
|
||
<dt id="pywrapknapsack_solver.KnapsackSolver.KNAPSACK_BRUTE_FORCE_SOLVER"><code class="name">var <span class="ident">KNAPSACK_BRUTE_FORCE_SOLVER</span></code></dt>
|
||
<dd>
|
||
<div class="desc"><p>Brute force method.</p>
|
||
<p>Limited to 30 items and one dimension, this
|
||
solver uses a brute force algorithm, ie. explores all possible states.
|
||
Experiments show competitive performance for instances with less than
|
||
15 items.</p></div>
|
||
</dd>
|
||
<dt id="pywrapknapsack_solver.KnapsackSolver.KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER"><code class="name">var <span class="ident">KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER</span></code></dt>
|
||
<dd>
|
||
<div class="desc"><p>Dynamic Programming approach for single dimension problems</p>
|
||
<p>Limited to one dimension, this solver is based on a dynamic programming
|
||
algorithm. The time and space complexity is O(capacity *
|
||
number_of_items).</p></div>
|
||
</dd>
|
||
<dt id="pywrapknapsack_solver.KnapsackSolver.KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER"><code class="name">var <span class="ident">KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER</span></code></dt>
|
||
<dd>
|
||
<div class="desc"><p>Generic Solver.</p>
|
||
<p>This solver can deal with both large number of items and several
|
||
dimensions. This solver is based on branch and bound.</p></div>
|
||
</dd>
|
||
<dt id="pywrapknapsack_solver.KnapsackSolver.KNAPSACK_MULTIDIMENSION_CBC_MIP_SOLVER"><code class="name">var <span class="ident">KNAPSACK_MULTIDIMENSION_CBC_MIP_SOLVER</span></code></dt>
|
||
<dd>
|
||
<div class="desc"><p>CBC Based Solver</p>
|
||
<p>This solver can deal with both large number of items and several
|
||
dimensions. This solver is based on Integer Programming solver CBC.</p></div>
|
||
</dd>
|
||
</dl>
|
||
<h3>Instance variables</h3>
|
||
<dl>
|
||
<dt id="pywrapknapsack_solver.KnapsackSolver.thisown"><code class="name">var <span class="ident">thisown</span></code></dt>
|
||
<dd>
|
||
<div class="desc"><p>The membership flag</p></div>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")</code></pre>
|
||
</details>
|
||
</dd>
|
||
</dl>
|
||
<h3>Methods</h3>
|
||
<dl>
|
||
<dt id="pywrapknapsack_solver.KnapsackSolver.BestSolutionContains"><code class="name flex">
|
||
<span>def <span class="ident">BestSolutionContains</span></span>(<span>self, item_id: int) -> bool</span>
|
||
</code></dt>
|
||
<dd>
|
||
<div class="desc"><p>Returns true if the item 'item_id' is packed in the optimal knapsack.</p></div>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def BestSolutionContains(self, item_id: "int") -> "bool":
|
||
r"""Returns true if the item 'item_id' is packed in the optimal knapsack."""
|
||
return _pywrapknapsack_solver.KnapsackSolver_BestSolutionContains(self, item_id)</code></pre>
|
||
</details>
|
||
</dd>
|
||
<dt id="pywrapknapsack_solver.KnapsackSolver.Init"><code class="name flex">
|
||
<span>def <span class="ident">Init</span></span>(<span>self, profits: std::vector< int64 > const &, weights: std::vector< std::vector< int64 > > const &, capacities: std::vector< int64 > const &) -> 'void'</span>
|
||
</code></dt>
|
||
<dd>
|
||
<div class="desc"><p>Initializes the solver and enters the problem to be solved.</p></div>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def Init(self, profits: "std::vector< int64 > const &", weights: "std::vector< std::vector< int64 > > const &", capacities: "std::vector< int64 > const &") -> "void":
|
||
r"""Initializes the solver and enters the problem to be solved."""
|
||
return _pywrapknapsack_solver.KnapsackSolver_Init(self, profits, weights, capacities)</code></pre>
|
||
</details>
|
||
</dd>
|
||
<dt id="pywrapknapsack_solver.KnapsackSolver.Solve"><code class="name flex">
|
||
<span>def <span class="ident">Solve</span></span>(<span>self) -> 'int64'</span>
|
||
</code></dt>
|
||
<dd>
|
||
<div class="desc"><p>Solves the problem and returns the profit of the optimal solution.</p></div>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def Solve(self) -> "int64":
|
||
r"""Solves the problem and returns the profit of the optimal solution."""
|
||
return _pywrapknapsack_solver.KnapsackSolver_Solve(self)</code></pre>
|
||
</details>
|
||
</dd>
|
||
<dt id="pywrapknapsack_solver.KnapsackSolver.set_time_limit"><code class="name flex">
|
||
<span>def <span class="ident">set_time_limit</span></span>(<span>self, time_limit_seconds: double) -> 'void'</span>
|
||
</code></dt>
|
||
<dd>
|
||
<div class="desc"><p>Time limit in seconds.</p>
|
||
<p>When a finite time limit is set the solution obtained might not be optimal
|
||
if the limit is reached.</p></div>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def set_time_limit(self, time_limit_seconds: "double") -> "void":
|
||
r"""
|
||
Time limit in seconds.
|
||
|
||
When a finite time limit is set the solution obtained might not be optimal
|
||
if the limit is reached.
|
||
"""
|
||
return _pywrapknapsack_solver.KnapsackSolver_set_time_limit(self, time_limit_seconds)</code></pre>
|
||
</details>
|
||
</dd>
|
||
<dt id="pywrapknapsack_solver.KnapsackSolver.set_use_reduction"><code class="name flex">
|
||
<span>def <span class="ident">set_use_reduction</span></span>(<span>self, use_reduction: bool) -> 'void'</span>
|
||
</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def set_use_reduction(self, use_reduction: "bool") -> "void":
|
||
return _pywrapknapsack_solver.KnapsackSolver_set_use_reduction(self, use_reduction)</code></pre>
|
||
</details>
|
||
</dd>
|
||
</dl>
|
||
</dd>
|
||
</dl>
|
||
</section>
|
||
</article>
|
||
<nav id="sidebar">
|
||
<header>
|
||
<a class="homelink" rel="home" title="OR-Tools Home" href="https://google.github.io/or-tools/">
|
||
<img src="https://developers.google.com/optimization/images/orLogo.png" alt=""> OR-Tools
|
||
</a>
|
||
</header>
|
||
<h1>Index</h1>
|
||
<div class="toc">
|
||
<ul></ul>
|
||
</div>
|
||
<ul id="index">
|
||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||
<ul>
|
||
<li>
|
||
<h4><code><a title="pywrapknapsack_solver.KnapsackSolver" href="#pywrapknapsack_solver.KnapsackSolver">KnapsackSolver</a></code></h4>
|
||
<ul class="">
|
||
<li><code><a title="pywrapknapsack_solver.KnapsackSolver.BestSolutionContains" href="#pywrapknapsack_solver.KnapsackSolver.BestSolutionContains">BestSolutionContains</a></code></li>
|
||
<li><code><a title="pywrapknapsack_solver.KnapsackSolver.Init" href="#pywrapknapsack_solver.KnapsackSolver.Init">Init</a></code></li>
|
||
<li><code><a title="pywrapknapsack_solver.KnapsackSolver.KNAPSACK_64ITEMS_SOLVER" href="#pywrapknapsack_solver.KnapsackSolver.KNAPSACK_64ITEMS_SOLVER">KNAPSACK_64ITEMS_SOLVER</a></code></li>
|
||
<li><code><a title="pywrapknapsack_solver.KnapsackSolver.KNAPSACK_BRUTE_FORCE_SOLVER" href="#pywrapknapsack_solver.KnapsackSolver.KNAPSACK_BRUTE_FORCE_SOLVER">KNAPSACK_BRUTE_FORCE_SOLVER</a></code></li>
|
||
<li><code><a title="pywrapknapsack_solver.KnapsackSolver.KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER" href="#pywrapknapsack_solver.KnapsackSolver.KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER">KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER</a></code></li>
|
||
<li><code><a title="pywrapknapsack_solver.KnapsackSolver.KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER" href="#pywrapknapsack_solver.KnapsackSolver.KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER">KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER</a></code></li>
|
||
<li><code><a title="pywrapknapsack_solver.KnapsackSolver.KNAPSACK_MULTIDIMENSION_CBC_MIP_SOLVER" href="#pywrapknapsack_solver.KnapsackSolver.KNAPSACK_MULTIDIMENSION_CBC_MIP_SOLVER">KNAPSACK_MULTIDIMENSION_CBC_MIP_SOLVER</a></code></li>
|
||
<li><code><a title="pywrapknapsack_solver.KnapsackSolver.Solve" href="#pywrapknapsack_solver.KnapsackSolver.Solve">Solve</a></code></li>
|
||
<li><code><a title="pywrapknapsack_solver.KnapsackSolver.set_time_limit" href="#pywrapknapsack_solver.KnapsackSolver.set_time_limit">set_time_limit</a></code></li>
|
||
<li><code><a title="pywrapknapsack_solver.KnapsackSolver.set_use_reduction" href="#pywrapknapsack_solver.KnapsackSolver.set_use_reduction">set_use_reduction</a></code></li>
|
||
<li><code><a title="pywrapknapsack_solver.KnapsackSolver.thisown" href="#pywrapknapsack_solver.KnapsackSolver.thisown">thisown</a></code></li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
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</nav>
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