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Title | la4j - Linear Algebra for |
Description | Installation la4j - Linear Algebra for |
Keywords | la4j,Java,java,matrix library,library,matrix,linear,linear algebra,sparse,dense |
WebSite | la4j.org |
Host IP | 192.30.252.153 |
Location | United States |
Site | Rank |
US$1,167,536
Last updated: 2023-05-13 18:36:46
la4j.org has Semrush global rank of 9,065,511. la4j.org has an estimated worth of US$ 1,167,536, based on its estimated Ads revenue. la4j.org receives approximately 134,716 unique visitors each day. Its web server is located in United States, with IP address 192.30.252.153. According to SiteAdvisor, la4j.org is safe to visit. |
Purchase/Sale Value | US$1,167,536 |
Daily Ads Revenue | US$1,078 |
Monthly Ads Revenue | US$32,332 |
Yearly Ads Revenue | US$387,981 |
Daily Unique Visitors | 8,982 |
Note: All traffic and earnings values are estimates. |
Host | Type | TTL | Data |
la4j.org. | A | 21600 | IP: 192.30.252.153 |
la4j.org. | NS | 21600 | NS Record: ns-cloud-a4.googledomains.com. |
la4j.org. | NS | 21600 | NS Record: ns-cloud-a3.googledomains.com. |
la4j.org. | NS | 21600 | NS Record: ns-cloud-a1.googledomains.com. |
la4j.org. | NS | 21600 | NS Record: ns-cloud-a2.googledomains.com. |
The la4j library is open source and 100% Java library that provides Linear Algebra primitives (matrices and vectors) and algorithms. The library was initially designed to be lightweight and simple tool for passionate Java developers. It has been started as student project and turned into one of the most popular Java packages for matrices and vectors. Installation The details on latest stable version might be found at Github releases page . <dependency> <groupId>org.la4j</groupId> <artifactId>la4j</artifactId> <version>0.6.0</version> </dependency> Features The key features of the la4j library are: Great performance allied with beautiful design No dependencies and tiny size (~150kb jar) Fluent object-oriented/functional API Sparse ( CRS , CCS ) and dense (1D/2D arrays) matrices Linear systems solving (Gaussian, Jacobi, Zeidel, Square Root, Sweep and other) Matrices decomposition (Eigenvalues/Eigenvectors, SVD, QR, LU, Cholesky and other) |
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