update README.md
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Corentin Le Molgat
@@ -5,76 +5,64 @@ flow problems.
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It contains in particular:
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* well-tuned algorithms (for example, shortest paths and
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[Hamiltonian paths](https://en.wikipedia.org/wiki/Hamiltonian_path)).
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* hard-to-find algorithms (Hamiltonian paths, push-relabel flow algorithms).
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* other, more common algorithms, that are useful to use with graphs from
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`util/graph`.
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* well-tuned algorithms (for example, shortest paths and
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[Hamiltonian paths](https://en.wikipedia.org/wiki/Hamiltonian_path)).
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* hard-to-find algorithms (Hamiltonian paths, push-relabel flow algorithms).
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* other, more common algorithms, that are useful to use with graphs from
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`util/graph`.
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Generic algorithms for shortest paths:
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* [`bounded_dijkstra.h`][bounded_dijkstra_h]: entry point for shortest paths.
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This is the preferred implementation for most needs.
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* [`bidirectional_dijkstra.h`][bidirectional_dijkstra_h]: for large graphs,
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this implementation might be faster than `bounded_dijkstra.h`.
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* [`shortest_paths.h`][shortest_paths_h]: shortest paths that are computed in
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parallel (only if there are several sources). Includes
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[Dijkstra](https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm) and
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[Bellman-Ford](https://en.wikipedia.org/wiki/Bellman%E2%80%93Ford_algorithm)
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algorithms. *Although its name is very generic, only use this implementation
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if parallelism makes sense.*
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* [`bounded_dijkstra.h`][bounded_dijkstra_h]: entry point for shortest paths.
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This is the preferred implementation for most needs.
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* [`bidirectional_dijkstra.h`][bidirectional_dijkstra_h]: for large graphs,
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this implementation might be faster than `bounded_dijkstra.h`.
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* [`shortest_paths.h`][shortest_paths_h]: shortest paths that are computed in
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parallel (only if there are several sources). Includes
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[Dijkstra](https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm) and
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[Bellman-Ford](https://en.wikipedia.org/wiki/Bellman%E2%80%93Ford_algorithm)
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algorithms. *Although its name is very generic, only use this implementation
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if parallelism makes sense.*
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Specific algorithms for paths:
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* [`dag_shortest_path.h`][dag_shortest_path_h]: shortest paths on directed
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acyclic graphs. If you have such a graph, this implementation is likely to
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be the fastest. Unlike most implementations, these algorithms have two
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interfaces: a "simple" one (list of edges and weights) and a standard one
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(taking as input a graph data structure from
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[`//ortools/graph/graph.h`][graph_h]).
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* [`dag_constrained_shortest_path.`][dag_constrained_shortest_path_h]:
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shortest paths on directed acyclic graphs with resource constraints.
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* [`hamiltonian_path.h`][hamiltonian_path_h]: entry point for computing
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minimum [Hamiltonian paths](https://en.wikipedia.org/wiki/Hamiltonian_path)
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and cycles on directed graphs with costs on arcs, using a
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dynamic-programming algorithm.
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* [`eulerian_path.h`][eulerian_path_h]: entry point for computing minimum
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[Eulerian paths](https://en.wikipedia.org/wiki/Eulerian_path) and cycles on
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undirected graphs.
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* [`dag_shortest_path.h`][dag_shortest_path_h]: shortest paths on directed
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acyclic graphs. If you have such a graph, this implementation is likely to be
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the fastest. Unlike most implementations, these algorithms have two interfaces
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: a "simple" one (list of edges and weights) and a standard one (taking as
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input a graph data structure from [`//ortools/graph/graph.h`][graph_h]).
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* [`dag_constrained_shortest_path.`][dag_constrained_shortest_path_h]: shortest
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paths on directed acyclic graphs with resource constraints.
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* [`hamiltonian_path.h`][hamiltonian_path_h]: entry point for computing minimum
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[Hamiltonian paths](https://en.wikipedia.org/wiki/Hamiltonian_path) and cycles
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on directed graphs with costs on arcs, using a dynamic-programming algorithm.
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* [`eulerian_path.h`][eulerian_path_h]: entry point for computing minimum
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[Eulerian paths](https://en.wikipedia.org/wiki/Eulerian_path) and cycles on
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undirected graphs.
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Graph decompositions:
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* [`connected_components.h`][connected_components_h]: entry point for computing
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connected components in an undirected graph. (It does not need an explicit
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graph class.)
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* [`strongly_connected_components.h`][strongly_connected_components_h]: entry
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point for computing the strongly connected components of a directed graph.
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* [`cliques.h`][cliques_h]: entry point for computing maximum cliques and
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clique covers in a directed graph, based on the Bron-Kerbosch algorithm.(It
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does not need an explicit graph class.)
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* [`cliques.h`][cliques_h]: entry point for computing maximum cliques and clique
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covers in a directed graph, based on the Bron-Kerbosch algorithm.(It does not
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need an explicit graph class.)
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Flow algorithms:
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* [`linear_assignment.h`][linear_assignment_h]: entry point for solving linear
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sum assignment problems (classical assignment problems where the total cost
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is the sum of the costs of each arc used) on directed graphs with arc costs,
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based on the Goldberg-Kennedy push-relabel algorithm.
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* [`max_flow.h`][max_flow_h]: entry point for computing maximum flows on
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directed graphs with arc capacities, based on the Goldberg-Tarjan
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push-relabel algorithm.
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* [`min_cost_flow.h`][min_cost_flow_h]: entry point for computing minimum-cost
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flows on directed graphs with arc capacities, arc costs, and
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supplies/demands at nodes, based on the Goldberg-Tarjan push-relabel
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algorithm.
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* [`linear_assignment.h`][linear_assignment_h]: entry point for solving linear
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sum assignment problems (classical assignment problems where the total cost is
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the sum of the costs of each arc used) on directed graphs with arc costs,
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based on the Goldberg-Kennedy push-relabel algorithm.
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* [`max_flow.h`][max_flow_h]: entry point for computing maximum flows on
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directed graphs with arc capacities, based on the Goldberg-Tarjan push-relabel
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algorithm.
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* [`min_cost_flow.h`][min_cost_flow_h]: entry point for computing minimum-cost
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flows on directed graphs with arc capacities, arc costs, and supplies/demands
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at nodes, based on the Goldberg-Tarjan push-relabel algorithm.
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## Class design
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@@ -201,21 +189,18 @@ node.
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## Wrappers
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* [`python`](python): the SWIG code that makes the wrapper available in Python
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and its unit tests.
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* [`java`](java): the SWIG code that makes the wrapper available in Java and
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its unit tests.
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* [`csharp`](csharp): the SWIG code that makes the wrapper available in C# and
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its unit tests.
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* [`python`](python): This directory contains the `pybind11` bindings to make
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these C++ libraries available in Python.
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* [`java`](java): the SWIG code that makes the wrapper available in Java and its
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unit tests.
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* [`csharp`](csharp): the SWIG code that makes the wrapper available in C# and
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its unit tests.
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## Samples
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You can find some canonical examples in [`samples`][samples].
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<!-- Links used throughout the document. -->
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[graph_h]: ../graph/graph.h
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[bounded_dijkstra_h]: ../graph/bounded_dijkstra.h
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[bidirectional_dijkstra_h]: ../graph/bidirectional_dijkstra.h
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@@ -12,21 +12,16 @@ performance and numerical stability.
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* [`primal_dual_hybrid_gradient.h`][primal_dual_hybrid_gradient_h]: The main
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entry point for the solver, which takes a `QuadraticProgram` and solver
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parameters.
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* [`quadratic_program.h`][quadratic_program_h]: Defines the `QuadraticProgram`
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struct to represent the optimization problem, including objective vectors,
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constraint matrices, and bounds.
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* [`quadratic_program_io.h`][quadratic_program_io_h]: Provides utilities to read
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quadratic programs from various file formats, including MPS and MPModelProto.
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* [`sharded_quadratic_program.h`][sharded_quadratic_program_h] and
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[`sharder.h`][sharder_h]: These provide the infrastructure for sharding
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problem data and performing parallel computations.
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* [`scheduler.h`][scheduler_h]: A thread scheduling interface that supports
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multiple backends (e.g. Eigen's thread pools).
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* [`iteration_stats.h`][iteration_stats_h] and
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[`termination.h`][termination_h]: Contain logic for computing convergence and
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infeasibility statistics and checking termination criteria.
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@@ -46,7 +41,6 @@ performance and numerical stability.
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* [`samples/`](samples): This directory provides example usage of the library.
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<!-- Links used throughout the document. -->
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[primal_dual_hybrid_gradient_h]: ../pdlp/primal_dual_hybrid_gradient.h
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[quadratic_program_h]: ../pdlp/quadratic_program.h
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[quadratic_program_io_h]: ../pdlp/quadratic_program_io.h
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