MathNet.Numerics.FSharp.Signed
3.17.0
Prefix Reserved
See the version list below for details.
dotnet add package MathNet.Numerics.FSharp.Signed --version 3.17.0
NuGet\Install-Package MathNet.Numerics.FSharp.Signed -Version 3.17.0
<PackageReference Include="MathNet.Numerics.FSharp.Signed" Version="3.17.0" />
<PackageVersion Include="MathNet.Numerics.FSharp.Signed" Version="3.17.0" />
<PackageReference Include="MathNet.Numerics.FSharp.Signed" />
paket add MathNet.Numerics.FSharp.Signed --version 3.17.0
#r "nuget: MathNet.Numerics.FSharp.Signed, 3.17.0"
#addin nuget:?package=MathNet.Numerics.FSharp.Signed&version=3.17.0
#tool nuget:?package=MathNet.Numerics.FSharp.Signed&version=3.17.0
Math.NET Numerics is the numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use. Supports .Net 4.0.
Product | Versions Compatible and additional computed target framework versions. |
---|---|
.NET Framework | net40 is compatible. net403 was computed. net45 was computed. net451 was computed. net452 was computed. net46 was computed. net461 was computed. net462 was computed. net463 was computed. net47 was computed. net471 was computed. net472 was computed. net48 was computed. net481 was computed. |
-
- FSharp.Core (>= 3.1.2.5)
- MathNet.Numerics.Signed (= 3.17.0)
NuGet packages
This package is not used by any NuGet packages.
GitHub repositories
This package is not used by any popular GitHub repositories.
Version | Downloads | Last updated |
---|---|---|
5.0.0 | 1,696 | 4/3/2022 |
5.0.0-beta02 | 208 | 4/3/2022 |
5.0.0-beta01 | 195 | 3/6/2022 |
5.0.0-alpha16 | 205 | 2/27/2022 |
5.0.0-alpha15 | 213 | 2/27/2022 |
5.0.0-alpha14 | 211 | 2/27/2022 |
5.0.0-alpha11 | 214 | 2/27/2022 |
5.0.0-alpha10 | 199 | 2/19/2022 |
5.0.0-alpha09 | 206 | 2/13/2022 |
5.0.0-alpha08 | 219 | 12/23/2021 |
5.0.0-alpha07 | 206 | 12/19/2021 |
5.0.0-alpha06 | 226 | 12/19/2021 |
5.0.0-alpha05 | 213 | 12/19/2021 |
5.0.0-alpha04 | 230 | 12/19/2021 |
5.0.0-alpha03 | 214 | 12/5/2021 |
5.0.0-alpha02 | 264 | 7/11/2021 |
5.0.0-alpha01 | 350 | 6/27/2021 |
4.15.0 | 770 | 1/7/2021 |
4.14.0 | 615 | 1/1/2021 |
4.13.0 | 475 | 12/30/2020 |
4.12.0 | 700 | 8/2/2020 |
4.11.0 | 827 | 5/24/2020 |
4.10.0 | 651 | 5/24/2020 |
4.9.1 | 657 | 4/12/2020 |
4.9.0 | 681 | 10/13/2019 |
4.8.1 | 755 | 6/11/2019 |
4.8.0 | 733 | 6/2/2019 |
4.8.0-beta02 | 535 | 5/30/2019 |
4.8.0-beta01 | 557 | 4/28/2019 |
4.7.0 | 1,005 | 11/11/2018 |
4.6.0 | 934 | 10/19/2018 |
4.5.0 | 1,233 | 5/22/2018 |
4.4.1 | 1,214 | 5/6/2018 |
3.20.2 | 9,339 | 1/22/2018 |
3.20.1 | 1,227 | 1/13/2018 |
3.20.0 | 1,283 | 7/15/2017 |
3.20.0-beta01 | 906 | 5/31/2017 |
3.19.0 | 1,180 | 4/29/2017 |
3.18.0 | 1,162 | 4/9/2017 |
3.17.0 | 1,224 | 1/15/2017 |
3.16.0 | 1,155 | 1/3/2017 |
3.15.0 | 1,181 | 12/27/2016 |
3.14.0-beta03 | 966 | 11/20/2016 |
3.14.0-beta02 | 944 | 11/15/2016 |
3.14.0-beta01 | 934 | 10/30/2016 |
3.13.1 | 1,223 | 9/6/2016 |
3.13.0 | 1,151 | 8/18/2016 |
3.12.0 | 1,247 | 7/3/2016 |
3.11.1 | 1,508 | 4/24/2016 |
3.11.0 | 1,502 | 2/13/2016 |
3.10.0 | 1,342 | 12/30/2015 |
3.9.0 | 1,412 | 11/25/2015 |
3.8.0 | 1,354 | 9/26/2015 |
3.7.1 | 1,354 | 9/21/2015 |
3.7.0 | 1,483 | 5/9/2015 |
3.6.0 | 1,547 | 3/22/2015 |
3.5.0 | 1,461 | 1/10/2015 |
3.4.0 | 1,306 | 1/4/2015 |
3.3.0 | 1,451 | 11/26/2014 |
3.3.0-beta2 | 1,198 | 10/25/2014 |
3.3.0-beta1 | 1,097 | 9/28/2014 |
3.2.3 | 1,513 | 9/6/2014 |
3.2.2 | 1,330 | 9/5/2014 |
3.2.1 | 1,365 | 8/5/2014 |
3.2.0 | 1,324 | 8/5/2014 |
3.1.0 | 1,354 | 7/20/2014 |
3.0.2 | 1,359 | 6/26/2014 |
3.0.1 | 1,346 | 6/24/2014 |
3.0.0 | 1,327 | 6/21/2014 |
3.0.0-beta05 | 1,083 | 6/20/2014 |
3.0.0-beta04 | 1,105 | 6/15/2014 |
3.0.0-beta03 | 1,123 | 6/5/2014 |
3.0.0-beta02 | 1,102 | 5/29/2014 |
3.0.0-beta01 | 1,302 | 4/14/2014 |
3.0.0-alpha9 | 1,185 | 3/29/2014 |
3.0.0-alpha8 | 1,164 | 2/26/2014 |
3.0.0-alpha7 | 1,077 | 12/30/2013 |
3.0.0-alpha6 | 1,137 | 12/2/2013 |
3.0.0-alpha5 | 1,220 | 10/2/2013 |
Random: random sources (all except crypto) now support ephemeral serialization.
Linear Algebra: explicit impl to copy a range of a row of a sparse matrix to a range of a sparse vector ~arthurvb
Linear Algebra: explicitly demand fully modifiable matrix where needed, fixes issues with diagonal matrices.
FFT: leverage new matrix internal array access approach in 2D matrix transformations.