add pybind11 doc to knapsack_solver
This commit is contained in:
Laurent Perron
@@ -73,11 +73,11 @@ class BaseKnapsackSolver;
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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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pywrapknapsack_solver.SolverType
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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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solver.init(profits, weights, capacities)
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profit = solver.solve()
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\endcode
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\b C++:
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@@ -16,6 +16,7 @@ load("@pip_deps//:requirements.bzl", "requirement")
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load("@pybind11_bazel//:build_defs.bzl", "pybind_extension")
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load("@rules_python//python:defs.bzl", "py_test")
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load("@bazel_skylib//rules:common_settings.bzl", "bool_flag")
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load("@rules_cc//cc:defs.bzl", "cc_library")
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# OSS solvers
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bool_flag(
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@@ -42,6 +43,12 @@ config_setting(
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},
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)
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cc_library(
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name = "pywrapknapsack_solver_doc",
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hdrs = ["pywrapknapsack_solver_doc.h"],
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visibility = ["//visibility:public"],
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)
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pybind_extension(
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name = "pywrapknapsack_solver",
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srcs = ["pywrapknapsack_solver.cc"],
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@@ -54,6 +61,7 @@ pybind_extension(
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}),
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visibility = ["//visibility:public"],
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deps = [
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":pywrapknapsack_solver_doc",
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"//ortools/algorithms:knapsack_solver_lib",
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"@com_google_absl//absl/strings",
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],
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434
ortools/algorithms/python/knapsack_solver_doc.h
Normal file
434
ortools/algorithms/python/knapsack_solver_doc.h
Normal file
@@ -0,0 +1,434 @@
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/*
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This file contains docstrings for use in the Python bindings.
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Do not edit! They were automatically extracted by pybind11_mkdoc.
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*/
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#define __EXPAND(x) x
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#define __COUNT(_1, _2, _3, _4, _5, _6, _7, COUNT, ...) COUNT
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#define __VA_SIZE(...) __EXPAND(__COUNT(__VA_ARGS__, 7, 6, 5, 4, 3, 2, 1, 0))
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#define __CAT1(a, b) a ## b
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#define __CAT2(a, b) __CAT1(a, b)
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#define __DOC1(n1) __doc_##n1
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#define __DOC2(n1, n2) __doc_##n1##_##n2
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#define __DOC3(n1, n2, n3) __doc_##n1##_##n2##_##n3
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#define __DOC4(n1, n2, n3, n4) __doc_##n1##_##n2##_##n3##_##n4
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#define __DOC5(n1, n2, n3, n4, n5) __doc_##n1##_##n2##_##n3##_##n4##_##n5
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#define __DOC6(n1, n2, n3, n4, n5, n6) __doc_##n1##_##n2##_##n3##_##n4##_##n5##_##n6
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#define __DOC7(n1, n2, n3, n4, n5, n6, n7) __doc_##n1##_##n2##_##n3##_##n4##_##n5##_##n6##_##n7
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#define DOC(...) __EXPAND(__EXPAND(__CAT2(__DOC, __VA_SIZE(__VA_ARGS__)))(__VA_ARGS__))
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#if defined(__GNUG__)
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#pragma GCC diagnostic push
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#pragma GCC diagnostic ignored "-Wunused-variable"
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#endif
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static const char *__doc_operations_research_BaseKnapsackSolver = R"doc()doc";
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static const char *__doc_operations_research_BaseKnapsackSolver_2 = R"doc()doc";
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static const char *__doc_operations_research_BaseKnapsackSolver_BaseKnapsackSolver = R"doc()doc";
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static const char *__doc_operations_research_BaseKnapsackSolver_GetLowerAndUpperBoundWhenItem = R"doc()doc";
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static const char *__doc_operations_research_BaseKnapsackSolver_GetName = R"doc()doc";
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static const char *__doc_operations_research_BaseKnapsackSolver_Init = R"doc()doc";
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static const char *__doc_operations_research_BaseKnapsackSolver_Solve = R"doc()doc";
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static const char *__doc_operations_research_BaseKnapsackSolver_best_solution = R"doc()doc";
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static const char *__doc_operations_research_BaseKnapsackSolver_solver_name = R"doc()doc";
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static const char *__doc_operations_research_KnapsackAssignment = R"doc()doc";
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static const char *__doc_operations_research_KnapsackAssignment_KnapsackAssignment = R"doc()doc";
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static const char *__doc_operations_research_KnapsackAssignment_is_in = R"doc()doc";
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static const char *__doc_operations_research_KnapsackAssignment_item_id = R"doc()doc";
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static const char *__doc_operations_research_KnapsackCapacityPropagator = R"doc()doc";
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static const char *__doc_operations_research_KnapsackCapacityPropagator_ComputeProfitBounds = R"doc()doc";
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static const char *__doc_operations_research_KnapsackCapacityPropagator_CopyCurrentStateToSolutionPropagator = R"doc()doc";
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static const char *__doc_operations_research_KnapsackCapacityPropagator_GetAdditionalProfit = R"doc()doc";
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static const char *__doc_operations_research_KnapsackCapacityPropagator_GetNextItemId = R"doc()doc";
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static const char *__doc_operations_research_KnapsackCapacityPropagator_InitPropagator = R"doc()doc";
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static const char *__doc_operations_research_KnapsackCapacityPropagator_KnapsackCapacityPropagator = R"doc()doc";
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static const char *__doc_operations_research_KnapsackCapacityPropagator_UpdatePropagator = R"doc()doc";
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static const char *__doc_operations_research_KnapsackCapacityPropagator_break_item_id = R"doc()doc";
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static const char *__doc_operations_research_KnapsackCapacityPropagator_capacity = R"doc()doc";
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static const char *__doc_operations_research_KnapsackCapacityPropagator_consumed_capacity = R"doc()doc";
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static const char *__doc_operations_research_KnapsackCapacityPropagator_profit_max = R"doc()doc";
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static const char *__doc_operations_research_KnapsackCapacityPropagator_sorted_items = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver_Clear = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver_GetAggregatedProfitUpperBound = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver_GetCurrentProfit = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver_GetLowerAndUpperBoundWhenItem = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver_GetNextItemId = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver_GetNumberOfItems = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver_HasOnePropagator = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver_IncrementalUpdate = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver_Init = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver_KnapsackGenericSolver = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver_MakeNewNode = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver_Solve = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver_UpdateBestSolution = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver_UpdatePropagators = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver_best_solution = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver_best_solution_2 = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver_best_solution_profit = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver_primary_propagator_id = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver_propagators = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver_search_nodes = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver_set_primary_propagator_id = R"doc()doc";
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static const char *__doc_operations_research_KnapsackGenericSolver_state = R"doc()doc";
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static const char *__doc_operations_research_KnapsackItem = R"doc()doc";
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static const char *__doc_operations_research_KnapsackItem_GetEfficiency = R"doc()doc";
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static const char *__doc_operations_research_KnapsackItem_KnapsackItem = R"doc()doc";
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static const char *__doc_operations_research_KnapsackItem_id = R"doc()doc";
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static const char *__doc_operations_research_KnapsackItem_profit = R"doc()doc";
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static const char *__doc_operations_research_KnapsackItem_weight = R"doc()doc";
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static const char *__doc_operations_research_KnapsackPropagator = R"doc()doc";
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static const char *__doc_operations_research_KnapsackPropagator_ComputeProfitBounds = R"doc()doc";
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static const char *__doc_operations_research_KnapsackPropagator_CopyCurrentStateToSolution = R"doc()doc";
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static const char *__doc_operations_research_KnapsackPropagator_CopyCurrentStateToSolutionPropagator = R"doc()doc";
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static const char *__doc_operations_research_KnapsackPropagator_GetNextItemId = R"doc()doc";
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static const char *__doc_operations_research_KnapsackPropagator_Init = R"doc()doc";
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static const char *__doc_operations_research_KnapsackPropagator_InitPropagator = R"doc()doc";
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static const char *__doc_operations_research_KnapsackPropagator_KnapsackPropagator = R"doc()doc";
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static const char *__doc_operations_research_KnapsackPropagator_Update = R"doc()doc";
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static const char *__doc_operations_research_KnapsackPropagator_UpdatePropagator = R"doc()doc";
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static const char *__doc_operations_research_KnapsackPropagator_current_profit = R"doc()doc";
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static const char *__doc_operations_research_KnapsackPropagator_current_profit_2 = R"doc()doc";
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static const char *__doc_operations_research_KnapsackPropagator_items = R"doc()doc";
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static const char *__doc_operations_research_KnapsackPropagator_items_2 = R"doc()doc";
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static const char *__doc_operations_research_KnapsackPropagator_profit_lower_bound = R"doc()doc";
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static const char *__doc_operations_research_KnapsackPropagator_profit_lower_bound_2 = R"doc()doc";
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static const char *__doc_operations_research_KnapsackPropagator_profit_upper_bound = R"doc()doc";
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static const char *__doc_operations_research_KnapsackPropagator_profit_upper_bound_2 = R"doc()doc";
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static const char *__doc_operations_research_KnapsackPropagator_set_profit_lower_bound = R"doc()doc";
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static const char *__doc_operations_research_KnapsackPropagator_set_profit_upper_bound = R"doc()doc";
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static const char *__doc_operations_research_KnapsackPropagator_state = R"doc()doc";
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static const char *__doc_operations_research_KnapsackPropagator_state_2 = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchNode = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchNode_KnapsackSearchNode = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchNode_assignment = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchNode_assignment_2 = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchNode_current_profit = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchNode_current_profit_2 = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchNode_depth = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchNode_depth_2 = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchNode_next_item_id = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchNode_next_item_id_2 = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchNode_parent = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchNode_parent_2 = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchNode_profit_upper_bound = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchNode_profit_upper_bound_2 = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchNode_set_current_profit = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchNode_set_next_item_id = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchNode_set_profit_upper_bound = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchPath = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchPath_Init = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchPath_KnapsackSearchPath = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchPath_MoveUpToDepth = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchPath_from = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchPath_from_2 = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchPath_to = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchPath_to_2 = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchPath_via = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSearchPath_via_2 = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSolver =
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R"doc(This library solves knapsack problems.
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Problems the library solves include: - 0-1 knapsack problems, - Multi-
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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
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extended from 1 to d dimensions. As an example, this can be useful to
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constrain the maximum number of items inside the knapsack. Without
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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
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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 where item_i is
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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,
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profits and dimensions are small, and the algorithm runs in pseudo
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polynomial time). Unfortunately, when adding conflict constraints the
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problem becomes strongly NP-hard, i.e. there is no pseudo-polynomial
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algorithm to solve it. That's the reason why the most of the following
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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. E.g.:
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**Python:**
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```
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{.py}
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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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```
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**C++:**
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```
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{.cpp}
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const std::vector<int64_t> profits = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
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const std::vector<std::vector<int64_t>> 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_t> 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_t profit = solver.Solve();
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```
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**Java:**
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```
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{.java}
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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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```)doc";
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static const char *__doc_operations_research_KnapsackSolver_BestSolutionContains = R"doc(Returns true if the item 'item_id' is packed in the optimal knapsack.)doc";
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static const char *__doc_operations_research_KnapsackSolver_ComputeAdditionalProfit = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSolver_GetName = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSolver_Init = R"doc(Initializes the solver and enters the problem to be solved.)doc";
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static const char *__doc_operations_research_KnapsackSolver_InitReducedProblem = R"doc()doc";
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static const char *__doc_operations_research_KnapsackSolver_IsSolutionOptimal = R"doc(Returns true if the solution was proven optimal.)doc";
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static const char *__doc_operations_research_KnapsackSolver_KnapsackSolver = R"doc()doc";
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|
||||
static const char *__doc_operations_research_KnapsackSolver_KnapsackSolver_2 = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackSolver_ReduceCapacities = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackSolver_ReduceProblem = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackSolver_Solve = R"doc(Solves the problem and returns the profit of the optimal solution.)doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackSolver_SolverType =
|
||||
R"doc(Enum controlling which underlying algorithm is used.
|
||||
|
||||
This enum is passed to the constructor of the KnapsackSolver object.
|
||||
It selects which solving method will be used.)doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackSolver_SolverType_KNAPSACK_64ITEMS_SOLVER =
|
||||
R"doc(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.)doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackSolver_SolverType_KNAPSACK_BRUTE_FORCE_SOLVER =
|
||||
R"doc(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.)doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackSolver_SolverType_KNAPSACK_DIVIDE_AND_CONQUER_SOLVER =
|
||||
R"doc(Divide and Conquer approach for single dimension problems
|
||||
|
||||
Limited to one dimension, this solver is based on a divide and conquer
|
||||
technique and is suitable for larger problems than Dynamic Programming
|
||||
Solver. The time complexity is O(capacity * number_of_items) and the
|
||||
space complexity is O(capacity + number_of_items).)doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackSolver_SolverType_KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER =
|
||||
R"doc(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).)doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackSolver_SolverType_KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER =
|
||||
R"doc(Generic Solver.
|
||||
|
||||
This solver can deal with both large number of items and several
|
||||
dimensions. This solver is based on branch and bound.)doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackSolver_additional_profit = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackSolver_best_solution = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackSolver_is_problem_solved = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackSolver_is_solution_optimal = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackSolver_known_value = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackSolver_mapping_reduced_item_id = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackSolver_set_time_limit =
|
||||
R"doc(Time limit in seconds.
|
||||
|
||||
When a finite time limit is set the solution obtained might not be
|
||||
optimal if the limit is reached.)doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackSolver_set_use_reduction = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackSolver_solver = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackSolver_time_limit = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackSolver_time_limit_seconds = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackSolver_use_reduction = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackSolver_use_reduction_2 = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackState = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackState_GetNumberOfItems = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackState_Init = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackState_KnapsackState = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackState_UpdateState = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackState_is_bound = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackState_is_bound_2 = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackState_is_in = R"doc()doc";
|
||||
|
||||
static const char *__doc_operations_research_KnapsackState_is_in_2 = R"doc()doc";
|
||||
|
||||
#if defined(__GNUG__)
|
||||
#pragma GCC diagnostic pop
|
||||
#endif
|
||||
|
||||
@@ -16,6 +16,7 @@
|
||||
#include <string>
|
||||
|
||||
#include "ortools/algorithms/knapsack_solver.h"
|
||||
#include "ortools/algorithms/python/pywrapknapsack_solver_doc.h"
|
||||
#include "pybind11/pybind11.h"
|
||||
#include "pybind11/pytypes.h"
|
||||
#include "pybind11/stl.h"
|
||||
@@ -26,39 +27,61 @@ namespace py = pybind11;
|
||||
using ::py::arg;
|
||||
|
||||
PYBIND11_MODULE(pywrapknapsack_solver, m) {
|
||||
py::class_<KnapsackSolver>(m, "KnapsackSolver")
|
||||
py::class_<KnapsackSolver>(m, "KnapsackSolver",
|
||||
DOC(operations_research, KnapsackSolver))
|
||||
.def(py::init<KnapsackSolver::SolverType, const std::string&>())
|
||||
.def("init", &KnapsackSolver::Init, arg("profits"), arg("weights"),
|
||||
arg("capacities"))
|
||||
.def("solve", &KnapsackSolver::Solve)
|
||||
.def("init", &KnapsackSolver::Init,
|
||||
DOC(operations_research, KnapsackSolver, Init), arg("profits"),
|
||||
arg("weights"), arg("capacities"))
|
||||
.def("solve", &KnapsackSolver::Solve,
|
||||
DOC(operations_research, KnapsackSolver, Solve))
|
||||
.def("best_solution_contains", &KnapsackSolver::BestSolutionContains,
|
||||
DOC(operations_research, KnapsackSolver, BestSolutionContains),
|
||||
arg("item_id"))
|
||||
.def("is_solution_optimal", &KnapsackSolver::IsSolutionOptimal)
|
||||
.def("is_solution_optimal", &KnapsackSolver::IsSolutionOptimal,
|
||||
DOC(operations_research, KnapsackSolver, IsSolutionOptimal))
|
||||
.def("set_time_limit", &KnapsackSolver::set_time_limit,
|
||||
DOC(operations_research, KnapsackSolver, set_time_limit),
|
||||
arg("time_limit_seconds"))
|
||||
.def("set_use_reduction", &KnapsackSolver::set_use_reduction,
|
||||
DOC(operations_research, KnapsackSolver, set_use_reduction),
|
||||
arg("use_reduction"));
|
||||
|
||||
py::enum_<KnapsackSolver::SolverType>(m, "SolverType")
|
||||
py::enum_<KnapsackSolver::SolverType>(
|
||||
m, "SolverType", DOC(operations_research, KnapsackSolver, SolverType))
|
||||
.value("KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER",
|
||||
KnapsackSolver::SolverType::
|
||||
KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER)
|
||||
KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER,
|
||||
DOC(operations_research, KnapsackSolver, SolverType,
|
||||
KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER))
|
||||
.value("KNAPSACK_BRUTE_FORCE_SOLVER",
|
||||
KnapsackSolver::SolverType::KNAPSACK_BRUTE_FORCE_SOLVER)
|
||||
KnapsackSolver::SolverType::KNAPSACK_BRUTE_FORCE_SOLVER,
|
||||
DOC(operations_research, KnapsackSolver, SolverType,
|
||||
KNAPSACK_BRUTE_FORCE_SOLVER))
|
||||
.value("KNAPSACK_64ITEMS_SOLVER",
|
||||
KnapsackSolver::SolverType::KNAPSACK_64ITEMS_SOLVER)
|
||||
KnapsackSolver::SolverType::KNAPSACK_64ITEMS_SOLVER,
|
||||
DOC(operations_research, KnapsackSolver, SolverType,
|
||||
KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER))
|
||||
.value("KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER",
|
||||
KnapsackSolver::SolverType::KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER)
|
||||
KnapsackSolver::SolverType::KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER,
|
||||
DOC(operations_research, KnapsackSolver, SolverType,
|
||||
KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER))
|
||||
#if defined(USE_CBC)
|
||||
.value("KNAPSACK_MULTIDIMENSION_CBC_MIP_SOLVER",
|
||||
KnapsackSolver::SolverType::KNAPSACK_MULTIDIMENSION_CBC_MIP_SOLVER)
|
||||
KnapsackSolver::SolverType::KNAPSACK_MULTIDIMENSION_CBC_MIP_SOLVER,
|
||||
DOC(operations_research, KnapsackSolver, SolverType,
|
||||
KNAPSACK_MULTIDIMENSION_CBC_MIP_SOLVER))
|
||||
#endif // USE_CBC
|
||||
#if defined(USE_SCIP)
|
||||
.value(
|
||||
"KNAPSACK_MULTIDIMENSION_SCIP_MIP_SOLVER",
|
||||
KnapsackSolver::SolverType::KNAPSACK_MULTIDIMENSION_SCIP_MIP_SOLVER)
|
||||
KnapsackSolver::SolverType::KNAPSACK_MULTIDIMENSION_SCIP_MIP_SOLVER,
|
||||
DOC(operations_research, KnapsackSolver, SolverType,
|
||||
KNAPSACK_MULTIDIMENSION_SCIP_MIP_SOLVER))
|
||||
#endif // USE_SCIP
|
||||
.value("KNAPSACK_DIVIDE_AND_CONQUER_SOLVER",
|
||||
KnapsackSolver::SolverType::KNAPSACK_DIVIDE_AND_CONQUER_SOLVER)
|
||||
KnapsackSolver::SolverType::KNAPSACK_DIVIDE_AND_CONQUER_SOLVER,
|
||||
DOC(operations_research, KnapsackSolver, SolverType,
|
||||
KNAPSACK_DIVIDE_AND_CONQUER_SOLVER))
|
||||
.export_values();
|
||||
}
|
||||
|
||||
295
ortools/algorithms/python/pywrapknapsack_solver_doc.h
Normal file
295
ortools/algorithms/python/pywrapknapsack_solver_doc.h
Normal file
@@ -0,0 +1,295 @@
|
||||
// Copyright 2010-2022 Google LLC
|
||||
// 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.
|
||||
|
||||
#ifndef OR_TOOLS_ALGORITHMS_PYTHON_PYWRAPKNAPSACK_SOLVER_DOC_H_
|
||||
#define OR_TOOLS_ALGORITHMS_PYTHON_PYWRAPKNAPSACK_SOLVER_DOC_H_
|
||||
|
||||
/*
|
||||
This file contains docstrings for use in the Python bindings.
|
||||
Do not edit! They were automatically extracted by pybind11_mkdoc.
|
||||
*/
|
||||
|
||||
#define __EXPAND(x) x
|
||||
#define __COUNT(_1, _2, _3, _4, _5, _6, _7, COUNT, ...) COUNT
|
||||
#define __VA_SIZE(...) __EXPAND(__COUNT(__VA_ARGS__, 7, 6, 5, 4, 3, 2, 1, 0))
|
||||
#define __CAT1(a, b) a##b
|
||||
#define __CAT2(a, b) __CAT1(a, b)
|
||||
#define __DOC1(n1) __doc_##n1
|
||||
#define __DOC2(n1, n2) __doc_##n1##_##n2
|
||||
#define __DOC3(n1, n2, n3) __doc_##n1##_##n2##_##n3
|
||||
#define __DOC4(n1, n2, n3, n4) __doc_##n1##_##n2##_##n3##_##n4
|
||||
#define __DOC5(n1, n2, n3, n4, n5) __doc_##n1##_##n2##_##n3##_##n4##_##n5
|
||||
#define __DOC6(n1, n2, n3, n4, n5, n6) \
|
||||
__doc_##n1##_##n2##_##n3##_##n4##_##n5##_##n6
|
||||
#define __DOC7(n1, n2, n3, n4, n5, n6, n7) \
|
||||
__doc_##n1##_##n2##_##n3##_##n4##_##n5##_##n6##_##n7
|
||||
#define DOC(...) \
|
||||
__EXPAND(__EXPAND(__CAT2(__DOC, __VA_SIZE(__VA_ARGS__)))(__VA_ARGS__))
|
||||
|
||||
#if defined(__GNUG__)
|
||||
#pragma GCC diagnostic push
|
||||
#pragma GCC diagnostic ignored "-Wunused-variable"
|
||||
#endif
|
||||
|
||||
static const char* __doc_operations_research_KnapsackSolver =
|
||||
R"doc(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:**
|
||||
|
||||
```
|
||||
{.py}
|
||||
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++:**
|
||||
|
||||
```
|
||||
{.cpp}
|
||||
const std::vector<int64_t> profits = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
|
||||
const std::vector<std::vector<int64_t>> weights =
|
||||
{ { 1, 2, 3, 4, 5, 6, 7, 8, 9 },
|
||||
{ 1, 1, 1, 1, 1, 1, 1, 1, 1 } };
|
||||
const std::vector<int64_t> capacities = { 34, 4 };
|
||||
|
||||
KnapsackSolver solver(
|
||||
KnapsackSolver::KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER,
|
||||
"Multi-dimensional solver");
|
||||
solver.Init(profits, weights, capacities);
|
||||
const int64_t profit = solver.Solve();
|
||||
```
|
||||
|
||||
**Java:**
|
||||
|
||||
```
|
||||
{.java}
|
||||
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();
|
||||
```)doc";
|
||||
|
||||
static const char*
|
||||
__doc_operations_research_KnapsackSolver_BestSolutionContains = // NOLINT
|
||||
R"doc(Returns true if the item 'item_id' is packed in the optimal knapsack.)doc";
|
||||
|
||||
static const char*
|
||||
__doc_operations_research_KnapsackSolver_ComputeAdditionalProfit =
|
||||
R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackSolver_GetName =
|
||||
R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackSolver_Init =
|
||||
R"doc(Initializes the solver and enters the problem to be solved.)doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackSolver_InitReducedProblem =
|
||||
R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackSolver_IsSolutionOptimal =
|
||||
R"doc(Returns true if the solution was proven optimal.)doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackSolver_KnapsackSolver =
|
||||
R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackSolver_KnapsackSolver_2 =
|
||||
R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackSolver_ReduceCapacities =
|
||||
R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackSolver_ReduceProblem =
|
||||
R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackSolver_Solve =
|
||||
R"doc(Solves the problem and returns the profit of the optimal solution.)doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackSolver_SolverType =
|
||||
R"doc(Enum controlling which underlying algorithm is used.
|
||||
|
||||
This enum is passed to the constructor of the KnapsackSolver object.
|
||||
It selects which solving method will be used.)doc";
|
||||
|
||||
static const char*
|
||||
__doc_operations_research_KnapsackSolver_SolverType_KNAPSACK_64ITEMS_SOLVER = // NOLINT
|
||||
R"doc(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.)doc";
|
||||
|
||||
static const char*
|
||||
__doc_operations_research_KnapsackSolver_SolverType_KNAPSACK_BRUTE_FORCE_SOLVER = // NOLINT
|
||||
R"doc(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.)doc";
|
||||
|
||||
static const char*
|
||||
__doc_operations_research_KnapsackSolver_SolverType_KNAPSACK_DIVIDE_AND_CONQUER_SOLVER = // NOLINT
|
||||
R"doc(Divide and Conquer approach for single dimension problems
|
||||
|
||||
Limited to one dimension, this solver is based on a divide and conquer
|
||||
technique and is suitable for larger problems than Dynamic Programming
|
||||
Solver. The time complexity is O(capacity * number_of_items) and the
|
||||
space complexity is O(capacity + number_of_items).)doc";
|
||||
|
||||
static const char*
|
||||
__doc_operations_research_KnapsackSolver_SolverType_KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER = // NOLINT
|
||||
R"doc(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).)doc";
|
||||
|
||||
static const char*
|
||||
__doc_operations_research_KnapsackSolver_SolverType_KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER = // NOLINT
|
||||
R"doc(Generic Solver.
|
||||
|
||||
This solver can deal with both large number of items and several
|
||||
dimensions. This solver is based on branch and bound.)doc";
|
||||
|
||||
static const char*
|
||||
__doc_operations_research_KnapsackSolver_SolverType_KNAPSACK_MULTIDIMENSION_CBC_MIP_SOLVER = // NOLINT
|
||||
R"doc(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.)doc";
|
||||
|
||||
static const char*
|
||||
__doc_operations_research_KnapsackSolver_SolverType_KNAPSACK_MULTIDIMENSION_SCIP_MIP_SOLVER = // NOLINT
|
||||
R"doc(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.)doc";
|
||||
static const char* __doc_operations_research_KnapsackSolver_additional_profit =
|
||||
R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackSolver_best_solution =
|
||||
R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackSolver_is_problem_solved =
|
||||
R"doc()doc";
|
||||
|
||||
static const char*
|
||||
__doc_operations_research_KnapsackSolver_is_solution_optimal = R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackSolver_known_value =
|
||||
R"doc()doc";
|
||||
|
||||
static const char*
|
||||
__doc_operations_research_KnapsackSolver_mapping_reduced_item_id =
|
||||
R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackSolver_set_time_limit =
|
||||
R"doc(Time limit in seconds.
|
||||
|
||||
When a finite time limit is set the solution obtained might not be
|
||||
optimal if the limit is reached.)doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackSolver_set_use_reduction =
|
||||
R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackSolver_solver =
|
||||
R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackSolver_time_limit =
|
||||
R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackSolver_time_limit_seconds =
|
||||
R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackSolver_use_reduction =
|
||||
R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackSolver_use_reduction_2 =
|
||||
R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackState = R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackState_GetNumberOfItems =
|
||||
R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackState_Init = R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackState_KnapsackState =
|
||||
R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackState_UpdateState =
|
||||
R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackState_is_bound =
|
||||
R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackState_is_bound_2 =
|
||||
R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackState_is_in = R"doc()doc";
|
||||
|
||||
static const char* __doc_operations_research_KnapsackState_is_in_2 =
|
||||
R"doc()doc";
|
||||
|
||||
#if defined(__GNUG__)
|
||||
#pragma GCC diagnostic pop
|
||||
#endif
|
||||
|
||||
#endif // OR_TOOLS_ALGORITHMS_PYTHON_PYWRAPKNAPSACK_SOLVER_DOC_H_
|
||||
@@ -83,4 +83,3 @@ def code_sample_java(name):
|
||||
def code_sample_cc_py(name):
|
||||
code_sample_cc(name = name)
|
||||
code_sample_py(name = name)
|
||||
|
||||
|
||||
Reference in New Issue
Block a user