MathNet.Numerics.MKL.Win-x64 3.0.0

The ID prefix of this package has been reserved for one of the owners of this package by Prefix Reserved
.NET 5.0 .NET Standard 2.0 .NET Framework 4.6.1
dotnet add package MathNet.Numerics.MKL.Win-x64 --version 3.0.0
NuGet\Install-Package MathNet.Numerics.MKL.Win-x64 -Version 3.0.0
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="MathNet.Numerics.MKL.Win-x64" Version="3.0.0" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add MathNet.Numerics.MKL.Win-x64 --version 3.0.0
#r "nuget: MathNet.Numerics.MKL.Win-x64, 3.0.0"
#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 MathNet.Numerics.MKL.Win-x64 as a Cake Addin
#addin nuget:?package=MathNet.Numerics.MKL.Win-x64&version=3.0.0

// Install MathNet.Numerics.MKL.Win-x64 as a Cake Tool
#tool nuget:?package=MathNet.Numerics.MKL.Win-x64&version=3.0.0

Intel oneAPI MKL native libraries for Math.NET Numerics on Windows.

Product Compatible and additional computed target framework versions.
.NET net5.0 is compatible.  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. 
.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 is compatible.  net462 was computed.  net463 was computed.  net47 was computed.  net471 was computed.  net472 was computed.  net48 is compatible.  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)
Additional computed target framework(s)
Learn more about Target Frameworks and .NET Standard.

NuGet packages (5)

Showing the top 5 NuGet packages that depend on MathNet.Numerics.MKL.Win-x64:

Package Downloads
Microsoft.Quantum.Research.Simulation The ID prefix of this package has been reserved for one of the owners of this package by

Quantum research libraries for quantum simulation (non-commercial).


Captcha Recognition for DE UI based on Nerual Nets


Hierarchical Temporal Memory (HTM)


Nyaong C# Library Math



GitHub repositories (1)

Showing the top 1 popular GitHub repositories that depend on MathNet.Numerics.MKL.Win-x64:

Repository Stars
CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.
Version Downloads Last updated
3.0.0 25,742 4/3/2022
3.0.0-beta3 1,778 2/19/2022
3.0.0-beta2 184 12/29/2021
3.0.0-beta1 152 12/23/2021
2.6.0-beta3 171 12/19/2021
2.6.0-beta2 177 12/9/2021
2.5.0 73,544 1/1/2021
2.4.0 31,421 5/22/2020
2.3.0 266,083 2/14/2018
2.2.0 42,779 10/30/2016
2.1.0 1,634 9/8/2016
2.0.0 7,866 9/26/2015
1.8.0 8,957 5/9/2015
1.7.0 2,619 12/31/2014
1.6.0 2,124 6/21/2014
1.5.0 1,145 6/15/2014
1.4.0 1,472 3/1/2014
1.3.0 1,803 5/1/2013
1.2.1 1,736 2/4/2013
1.2.0 1,261 2/3/2013

New binary names and package structure with runtime folders
With Intel oneAPI 2022
Note that MathNet.Numerics.Providers.MKL.dll is required for this to work with Numerics v5