Difference between revisions of "Main Page"
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−   '''Version:''' ''v0.  +   '''Version:''' ''v0.41'' 
    
−   '''Date:''' ''  +   '''Date:''' ''July 7, 2021'' 
    
 [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.  +   [https://github.com/lessthanoptimal/ejml/blob/v0.41/change.txt Change Log] 
}  }  
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Latest revision as of 07:58, 7 July 2021
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 small and large, dense and sparse, real and complex 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.

 



News 2021
 

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.