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Efficient Java Matrix Library (EJML) is a [http://en.wikipedia.org/wiki/Linear_algebra 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.
 
Efficient Java Matrix Library (EJML) is a [http://en.wikipedia.org/wiki/Linear_algebra 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 [http://math.nist.gov/javanumerics/jama/ Jama].  ''Equations'' is a symbolic interface, similar in spirit to Matlab and other [http://en.wikipedia.org/wiki/Computer_algebra_system CAS], that provides a compact way of writing equations.
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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 [http://math.nist.gov/javanumerics/jama/ Jama].  ''Equations'' is a symbolic interface, similar in spirit to [http://www.mathworks.com/products/matlab/ Matlab] and other [http://en.wikipedia.org/wiki/Computer_algebra_system CAS], that provides a compact way of writing equations.
 
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* Random Matrices (covariance, orthogonal, symmetric, ... )
 
* Random Matrices (covariance, orthogonal, symmetric, ... )
 
* Unit Testing
 
* 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.
 
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Revision as of 21:19, 14 March 2015


Ejml logo.gif

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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Acknowledgments
Performance
Users


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
  • 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
.

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