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Efficient Java Matrix Library (EJML) is a linear algebra library for manipulating dense matrices. Its design goals are; 1) to be as computationally and memory efficient as possible for both small and large matrices, and 2) to be accessible to both novices and experts. These goals are accomplished by dynamically selecting the best algorithms to use at runtime, clean API, and multiple interfaces. EJML is free, written in 100% Java and has been released under an Apache v2.0 license. EJML has three distinct ways to interact with it: 1) procedural, 2) object oriented, and 3) equations. Procedure provides all capabilities of EJML and almost complete control over memory creation, speed, and specific algorithms. Object oriented provides a simplified subset of the core capabilities in an easy to use API, inspired by Jama. Equations is a symbolic interface, similar in spirit to Matlab and other CAS, that provides a compact way of writing equations.

Version: v0.26 Date: September 15, 2014
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Code Examples

Below are code examples demonstrating how to compute the Kalman gain, "K", using the three different interfaces in EJML.

Equations eq.process("K = P*H'*inv( H*P*H' + R )"); Object Oriented SimpleMatrix S = H.mult(P).mult(H.transpose()).plus(R); SimpleMatrix K = P.mult(H.transpose().mult(S.invert())); Procedural mult(H,P,c); multTransB(c,H,S); addEquals(S,R); if( !invert(S,S_inv) ) throw new RuntimeException("Invert failed"); multTransA(H,S_inv,d); mult(P,d,K);

Functionality

Data Structures	Operations
Fixed Sized Dense Real Row-major Block Dense Complex Row-major Incomplete Support	Basic Operators (addition, multiplication, ... ) Matrix Manipulation (extract, insert, combine, ... ) Linear Solvers (linear, least squares, incremental, ... ) Decompositions (LU, QR, Cholesky, SVD, Eigenvalue, ...) Matrix Features (rank, symmetric, definitiveness, ... ) Random Matrices (covariance, orthogonal, symmetric, ... ) Unit Testing

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EJML is currently a single threaded library only. Multi threaded work will start once block implementations of SVD and Eigenvalue are finished.