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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) SimpleMatrix, and 3) Equations. Procedure provides all capabilities of EJML and almost complete control over memory creation, speed, and specific algorithms. SimpleMatrix provides a simplified subset of the core capabilities in an easy to use flow styled object-oriented 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: v0.32 Date: September 18, 2017 Upgrade Script Change Log
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Code Examples

Demonstrations on how to compute the Kalman gain "K" using each interface in EJML.

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); SimpleMatrix SimpleMatrix S = H.mult(P).mult(H.transpose()).plus(R); SimpleMatrix K = P.mult(H.transpose().mult(S.invert())); Equations eq.process("K = P*H'*inv( H*P*H' + R )");

Functionality

Data Structures	Operations
Fixed Sized Dense Real Row-major Block Dense Complex Row-major	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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Decomposition	Dense Real	Dense Complex	Sparse Real	Sparse Complex
LU	X	X
Cholesky LL	X	X
Cholesky LDL	X
QR	X	X
QRP	X
SVD	X
Eigen-Symmetric	X
Eigen-General	X

Support for floats (32-bit) and doubles (64-bit) is available. Sparse matrix support is only available for basic operations at this time.

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