Update generated doc
This commit is contained in:
Mizux Seiha
@@ -93,32 +93,168 @@ class _SwigNonDynamicMeta(type):
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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.
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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
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items inside the knapsack.
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Without loss of generality, profits and weights are assumed to be positive.
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From a mathematical point of view, the multi-dimensional knapsack problem
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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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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
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and dimensions are small, and the algorithm runs in pseudo polynomial time).
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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
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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::vector<int64> profits = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
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const std::vector<std::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::vector<int64> 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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.. code-block:: c++
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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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KnapsackSolver solver = new KnapsackSolver(
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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
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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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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"""
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Optimized method for single dimension small problems
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Limited to 64 items and one dimension, this
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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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"""
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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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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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KNAPSACK_MULTIDIMENSION_SCIP_MIP_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_MULTIDIMENSION_SCIP_MIP_SOLVER
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r"""
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SCIP 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 SCIP.
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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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@@ -139,65 +275,288 @@ _pywrapknapsack_solver.KnapsackSolver_swigregister(KnapsackSolver)</code></pre>
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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"></div>
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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,
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- Multi-dimensional knapsack problems,</p>
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<p>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.
|
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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
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items inside the knapsack.
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Without loss of generality, profits and weights are assumed to be positive.</p>
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<p>From a mathematical point of view, the multi-dimensional knapsack problem
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can be modeled by d linear constraints:</p>
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<pre><code>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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</code></pre>
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<p>Then the goal is to maximize:</p>
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<pre><code>Sum(i:1..n)(profit_i * item_i).
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</code></pre>
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<p>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
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and dimensions are small, and the algorithm runs in pseudo polynomial time).
|
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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
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bound search.</p>
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<p>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.:</p>
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<p><strong>Python</strong>:</p>
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<p>.. code-block:: c++</p>
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<pre><code> 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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</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::vector<int64> profits = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
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const std::vector<std::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::vector<int64> 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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</code></pre>
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<p><strong>Java</strong>:</p>
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<p>.. code-block:: c++</p>
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<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 },
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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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KnapsackSolver solver = new KnapsackSolver(
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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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</code></pre></div>
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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">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,
|
||||
|
||||
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
|
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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
|
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can be modeled by d linear constraints:
|
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|
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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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|
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Then the goal is to maximize:
|
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|
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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
|
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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.
|
||||
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
|
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vectors for weights, and a vector of capacities.
|
||||
E.g.:
|
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|
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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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|
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.. code-block:: c++
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|
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const std::vector<int64> profits = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
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const std::vector<std::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::vector<int64> 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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KnapsackSolver solver = new KnapsackSolver(
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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
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KNAPSACK_BRUTE_FORCE_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_BRUTE_FORCE_SOLVER
|
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r"""
|
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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
|
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r"""
|
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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.
|
||||
"""
|
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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
|
||||
|
||||
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
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r"""
|
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CBC Based 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 Integer Programming solver CBC.
|
||||
"""
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KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER
|
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r"""
|
||||
Generic Solver.
|
||||
|
||||
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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KNAPSACK_MULTIDIMENSION_SCIP_MIP_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_MULTIDIMENSION_SCIP_MIP_SOLVER
|
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r"""
|
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SCIP based solver
|
||||
|
||||
This solver can deal with both large number of items and several
|
||||
dimensions. This solver is based on Integer Programming solver SCIP.
|
||||
"""
|
||||
|
||||
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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|
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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."""
|
||||
return _pywrapknapsack_solver.KnapsackSolver_Init(self, profits, weights, capacities)
|
||||
|
||||
def Solve(self) -> "int64":
|
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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"></div>
|
||||
<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"></div>
|
||||
<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"></div>
|
||||
<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"></div>
|
||||
<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"></div>
|
||||
<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>
|
||||
<dt id="pywrapknapsack_solver.KnapsackSolver.KNAPSACK_MULTIDIMENSION_SCIP_MIP_SOLVER"><code class="name">var <span class="ident">KNAPSACK_MULTIDIMENSION_SCIP_MIP_SOLVER</span></code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<div class="desc"><p>SCIP 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 SCIP.</p></div>
|
||||
</dd>
|
||||
</dl>
|
||||
<h3>Instance variables</h3>
|
||||
@@ -219,12 +578,13 @@ _pywrapknapsack_solver.KnapsackSolver_swigregister(KnapsackSolver)</code></pre>
|
||||
<span>def <span class="ident">BestSolutionContains</span></span>(<span>self, item_id: int) -> bool</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<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>
|
||||
@@ -232,12 +592,13 @@ _pywrapknapsack_solver.KnapsackSolver_swigregister(KnapsackSolver)</code></pre>
|
||||
<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"></div>
|
||||
<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>
|
||||
@@ -245,12 +606,13 @@ _pywrapknapsack_solver.KnapsackSolver_swigregister(KnapsackSolver)</code></pre>
|
||||
<span>def <span class="ident">Solve</span></span>(<span>self) -> 'int64'</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<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>
|
||||
@@ -258,12 +620,20 @@ _pywrapknapsack_solver.KnapsackSolver_swigregister(KnapsackSolver)</code></pre>
|
||||
<span>def <span class="ident">set_time_limit</span></span>(<span>self, time_limit_seconds: double) -> 'void'</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<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>
|
||||
|
||||
Reference in New Issue
Block a user