Difference between revisions of "Main Page"
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+  }  
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+  == News ==  
+  
+  { width="500pt"   
+     
+    
+  * Read and write EJML in Matlab format with [https://github.com/HebiRobotics/MFL MFL] from HEBI Robotics  
+  * Graph BLAS continues to be flushed out with masks being added to latest SNAPSHOT  
+  * Concurrency/threading has been added to some operations  
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Support for floats (32bit) and doubles (64bit) is available. Sparse matrix support is only available for basic operations at this time.  Support for floats (32bit) and doubles (64bit) is available. Sparse matrix support is only available for basic operations at this time.  
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Revision as of 19:44, 17 February 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
 

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.