Difference between revisions of "Main Page"

From Efficient Java Matrix Library
Jump to navigation Jump to search
Line 16: Line 16:
 
{|style="font-size:120%; text-align:left;"
 
{|style="font-size:120%; text-align:left;"
 
|-
 
|-
| '''Version:''' ''v0.28''
+
| '''Version:''' ''v0.29''
 
|-
 
|-
| '''Date:''' ''August 9, 2015''  
+
| '''Date:''' ''January 23, 2015''  
 
|-
 
|-
 
| [[Change Log]]
 
| [[Change Log]]

Revision as of 14:57, 23 January 2016


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) 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: v0.29
Date: January 23, 2015
Change Log
Download
Manual
JavaDoc
Message Board
Bug Reports
FAQ
Acknowledgments
Performance
Users


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
.

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