I was wondering whether any of the well-known matrix libraries for Java, such as Colt or EJML, actually provide similar functionality as MatLab? For instance, I can't seem to find anywhere in the definition of their API simple method to add or subtract two matrices/vectors by each other, which seems to be the most common operation used. Am I missing something?
Answer
Some Java libraries for linear algebra are:
- Apache Commons Math: http://commons.apache.org/proper/commons-math/
- jeigen - a wrapper for eigen - https://github.com/hughperkins/jeigen (includes complex and rarely found feature like matrix exponential and matrix logarithm)
- jblas http://mikiobraun.github.io/jblas/ (also features more complex functions like matrix exponential, also very fast).
- Colt http://acs.lbl.gov/software/colt/
- JAMA http://math.nist.gov/javanumerics/jama/
- UJMP - http://sourceforge.net/projects/ujmp/
EDIT maybe we can extend this list whenever one comes across and you know - the world keeps moving:
- ojAlgo - http://ojalgo.org/ has promising benchmarks
- Efficient Java Matrix Library (EJML) - http://ejml.org
- ParallelColt - https://sites.google.com/site/piotrwendykier/software/parallelcolt
- la4j - http://la4j.org/
- MTJ - https://github.com/fommil/matrix-toolkits-java
- nd4j - https://nd4j.org/ lets you choose underlying native implementations like cuda or openBlas
Note: Personally: I use Apache Commons Math and Colt in my own project (http://www.finmath.net). While commons math is actively developed, I found that Colt is still faster in some tasks (like Eigenvalue decomposition). For that reason I use some kind of wrapper which allows me to quickly switch the underlying library (I only need a few things like solving systems of equations and Eigenvalue decomposition).