optimatika/ojAlgo

oj! Algorithms http://ojalgo.org

Download

Step 1. Add the JitPack repository to your build file

Add it in your root settings.gradle at the end of repositories:

	dependencyResolutionManagement {
		repositoriesMode.set(RepositoriesMode.FAIL_ON_PROJECT_REPOS)
		repositories {
			mavenCentral()
			maven { url 'https://jitpack.io' }
		}
	}

Add it in your settings.gradle.kts at the end of repositories:

	dependencyResolutionManagement {
		repositoriesMode.set(RepositoriesMode.FAIL_ON_PROJECT_REPOS)
		repositories {
			mavenCentral()
			maven { url = uri("https://jitpack.io") }
		}
	}

Add to pom.xml

	<repositories>
		<repository>
		    <id>jitpack.io</id>
		    <url>https://jitpack.io</url>
		</repository>
	</repositories>

Add it in your build.sbt at the end of resolvers:

 
    resolvers += "jitpack" at "https://jitpack.io"

Add it in your project.clj at the end of repositories:

 
    :repositories [["jitpack" "https://jitpack.io"]]

Step 2. Add the dependency

	dependencies {
		implementation 'com.github.optimatika:ojalgo:53.3.0'
	}

	dependencies {
		implementation("com.github.optimatika:ojalgo:53.3.0")
	}

	<dependency>
	    <groupId>com.github.optimatika</groupId>
	    <artifactId>ojalgo</artifactId>
	    <version>53.3.0</version>
	</dependency>


                            
    libraryDependencies += "com.github.optimatika" % "ojalgo" % "53.3.0"


                            
    :dependencies [[com.github.optimatika/ojalgo "53.3.0"]]

Readme

oj! Algorithms

oj! Algorithms - ojAlgo - is Open Source Java code that has to do with mathematics, linear algebra and optimisation.

High Performance on a Rich Feature Set with Zero Dependencies

ojAlgo is the fastest pure Java linear algebra library available. That statement is backed by the latest Java Matrix Benchmark results – that’s a third party independent benchmark (not written by anyone associated with ojAlgo).
Optimisation (mathematical programming) tools including LP, QP and MIP solvers – again this is pure Java with zero dependencies.
A collection of “array” classes that can be sparse or dense and arbitrarily large. They can be used as 1-, 2- or N/Any-dimensional arrays, and may contain/handle a multitude of different number types including complex numbers, rational numbers and quaternions. The memory for the arrays can alternatively be allocated off heap or in a file. The linear algebra part of ojAlgo builds on these arrays – they’re fast and efficient.
A growing collection of utilities for data science, including Artificial Neural Networks, clustering and a collection of tools for reading/writing/processing data
Various other things like time series, random numbers, stochastic processes, descriptive statistics…

General information about ojAlgo is available at the project web site: http://ojalgo.org/

Artifacts

ojAlgo is available at The Central (Maven) Repository to be used with your favourite dependency management tool.

<!-- https://mvnrepository.com/artifact/org.ojalgo/ojalgo -->
<dependency>
    <groupId>org.ojalgo</groupId>
    <artifactId>ojalgo</artifactId>
    <version>X.Y.Z</version>
</dependency>

Documentation and Support

User documentation is available in the form of blog posts at the ojAlgo web site: http://ojalgo.org/

Programming questions related to ojAlgo are best asked at stack overflow. Just remember to actually mention ojAlgo and tag the question using 'ojalgo' and whatever other tags you find suitable.

Bug reports, or any issue with existing code, should be posted at GitHub: https://github.com/optimatika/ojAlgo/issues

https://github.com/optimatika/ojAlgo/discussions may be used to discuss anything related to ojAlgo.

ojAlgo is Open Source, and you are strongly encouraged to clone or fork this repository and work directly with the source code. The source code is (part of) the documentation, and you should read it.

All example code (from the blog posts) in a multi-file gist: https://gist.github.com/apete/b3278dc2f8c2db6a00369c211ba321db