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 [https://github.com/lessthanoptimal/ejml/blob/master/convert_to_ejml31.py v0.31 Upgrade Script]   [https://github.com/lessthanoptimal/ejml/blob/master/convert_to_ejml31.py v0.31 Upgrade Script]  
    
−   [https://github.com/lessthanoptimal/ejml/blob/v0.37/change.txt Change Log]  +   [https://github.com/lessthanoptimal/ejml/blob/v0.37.1/change.txt Change Log] 
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Revision as of 20:22, 25 December 2018
Efficient Java Matrix Library (EJML) is a linear algebra library for manipulating real/complex/dense/sparse 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.

 



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 



Decomposition  Dense Real  Dense Complex  Sparse Real  Sparse Complex 

LU  X  X  X  
Cholesky LL  X  X  X  
Cholesky LDL  X  
QR  X  X  X  
QRP  X  
SVD  X  
EigenSymmetric  X  
EigenGeneral  X 
Support for floats (32bit) and doubles (64bit) 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.