TreesearchLib 1.1.0

There is a newer version of this package available.
See the version list below for details.
dotnet add package TreesearchLib --version 1.1.0
NuGet\Install-Package TreesearchLib -Version 1.1.0
This command is intended to be used within the Package Manager Console in Visual Studio, as it uses the NuGet module's version of Install-Package.
<PackageReference Include="TreesearchLib" Version="1.1.0" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add TreesearchLib --version 1.1.0
#r "nuget: TreesearchLib, 1.1.0"
#r directive can be used in F# Interactive and Polyglot Notebooks. Copy this into the interactive tool or source code of the script to reference the package.
// Install TreesearchLib as a Cake Addin
#addin nuget:?package=TreesearchLib&version=1.1.0

// Install TreesearchLib as a Cake Tool
#tool nuget:?package=TreesearchLib&version=1.1.0

TreesearchLib

TreesearchLib is a C# framework for modeling optimization problems as search trees and a collection of algorithms to identify good solutions for those problems. It includes exhaustive algorithms, as well as heuristics.

Modeling optimization problems is performed by implementing a problem state class. This class maintains the decisions that have been taken, as well as the next choices, i.e., branches in the search tree. It is possible to compute bounds, which algorithms may use to discard parts of the tree.

Check out the SampleApp to see implementations of the following problems:

  • ChooseSmallestProblem - a fun problem which searches small values in the sequence of random number seeds
  • Knapsack - the famous {0, 1}-Knapsack, implemented using reversible search (allowing to undo moves), as well as non-reversible
  • TSP - the Berlin52 instance of the TSPLIB
  • SchedulingProblem - a very simple scheduling problem

These samples should give you an idea on how to use the framework for problem modeling.

The algorithms that are included are:

  • Branch and bound (depth-first search), sequential and parallel
  • Breadth-first search, sequential and parallel
  • Limited Discrepancy Search, sequential only
  • Beam Search, sequental and parallel
  • Monotonic Beam Search, sequential only
  • Rake Search (and a Rake+Beam combination), sequential and parallel
  • Pilot Method, sequential and parallel
  • Monte Carlo Tree Search, sequential only

New hybrid algorithms can be implemented, also by making use of the existing algorithms.

Product Compatible and additional computed target framework versions.
.NET net5.0 was computed.  net5.0-windows was computed.  net6.0 was computed.  net6.0-android was computed.  net6.0-ios was computed.  net6.0-maccatalyst was computed.  net6.0-macos was computed.  net6.0-tvos was computed.  net6.0-windows was computed.  net7.0 was computed.  net7.0-android was computed.  net7.0-ios was computed.  net7.0-maccatalyst was computed.  net7.0-macos was computed.  net7.0-tvos was computed.  net7.0-windows was computed.  net8.0 was computed.  net8.0-android was computed.  net8.0-browser was computed.  net8.0-ios was computed.  net8.0-maccatalyst was computed.  net8.0-macos was computed.  net8.0-tvos was computed.  net8.0-windows was computed. 
.NET Core netcoreapp2.0 was computed.  netcoreapp2.1 was computed.  netcoreapp2.2 was computed.  netcoreapp3.0 was computed.  netcoreapp3.1 was computed. 
.NET Standard netstandard2.0 is compatible.  netstandard2.1 was computed. 
.NET Framework net461 was computed.  net462 was computed.  net463 was computed.  net47 was computed.  net471 was computed.  net472 is compatible.  net48 was computed.  net481 was computed. 
MonoAndroid monoandroid was computed. 
MonoMac monomac was computed. 
MonoTouch monotouch was computed. 
Tizen tizen40 was computed.  tizen60 was computed. 
Xamarin.iOS xamarinios was computed. 
Xamarin.Mac xamarinmac was computed. 
Xamarin.TVOS xamarintvos was computed. 
Xamarin.WatchOS xamarinwatchos was computed. 
Compatible target framework(s)
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Version Downloads Last updated
1.3.0 402 4/9/2023
1.2.1 193 4/7/2023
1.2.0 221 4/3/2023
1.1.2 191 3/31/2023
1.1.1 225 3/25/2023
1.1.0 208 3/23/2023
1.0.1 492 1/13/2023
1.0.0 282 1/13/2023

Add parallel implementations of the following algorithms and heuristics:
   - Depth-first Search
   - Breadth-first Search
   - Beam Search
   - Rake Search
   - Rake and Beam Search
   - Pilot Method

   Add a new Scheduling example with three different objectives