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Consult the [//meta.wikimedia.org/wiki/Help:Contents User's Guide] for information on using the wiki software.
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<center>
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{| style="width:640pt;"
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| align="center" |
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[[File:Ejml_logo.gif]]
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|-
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== Getting started ==
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Efficient Java Matrix Library (EJML) is a [http://en.wikipedia.org/wiki/Linear_algebra 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.
* [//www.mediawiki.org/wiki/Special:MyLanguage/Manual:Configuration_settings Configuration settings list]
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* [https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]
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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 [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.
* [//www.mediawiki.org/wiki/Special:MyLanguage/Localisation#Translation_resources Localise MediaWiki for your language]
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{|
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| colspan="3" align="center" |
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{|style="font-size:120%; text-align:left;"
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|-
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| '''Version:''' ''v0.41''
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|-
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| '''Date:''' ''July 7, 2021''
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|-
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| [https://github.com/lessthanoptimal/ejml/blob/master/convert_to_ejml31.py v0.31 Upgrade Script]
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|-
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| [https://github.com/lessthanoptimal/ejml/blob/v0.41/change.txt Change Log]
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|}
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|- valign="top"
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| width="220pt" |
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{| width="200pt" border="1"  align="center" style="font-size:120%; text-align:center; border-collapse:collapse; background-color:#ffffee;"
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|-
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| [[Download|Download]]
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|-
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| [[manual|Manual]]
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|-
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| [http://ejml.org/javadoc/ JavaDoc]
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|}
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| width="220pt" |
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{| width="200pt" border="1"  align="center" style="font-size:120%; text-align:center; border-collapse:collapse;  background-color:#ffffee;"
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|-
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| [http://groups.google.com/group/efficient-java-matrix-library-discuss Message Board]
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|-
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| [https://github.com/lessthanoptimal/ejml/issues Bug Reports]
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|-
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| [[Frequently Asked Questions|FAQ]]
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|-
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| [[Kotlin|Kotlin]]
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|}
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| width="220pt" |
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{| width="200pt" border="1"  align="center" style="font-size:120%; text-align:center; border-collapse:collapse;  background-color:#ffffee;"
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|-
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| [[Acknowledgments|Acknowledgments]]
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|-
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| [[Performance|Performance]]
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|-
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| [[Users|Users]]
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|}
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|}
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== News 2021 ==
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{| width="500pt" |
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| -
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|
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* Read and write EJML in Matlab format with [https://github.com/HebiRobotics/MFL MFL] from HEBI Robotics
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* Graph BLAS continues to be flushed out with masks being added to latest SNAPSHOT
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* Concurrency/threading has been added to some operations
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|}
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== Code Examples ==
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Demonstrations on how to compute the Kalman gain "K" using each interface in EJML.
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{| width="500pt" |
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|-
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|
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'''Procedural'''
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<syntaxhighlight lang="java">
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mult(H,P,c);
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multTransB(c,H,S);
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addEquals(S,R);
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if( !invert(S,S_inv) )
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    throw new RuntimeException("Invert failed");
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multTransA(H,S_inv,d);
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mult(P,d,K);
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</syntaxhighlight>
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'''SimpleMatrix'''
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<syntaxhighlight lang="java">
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SimpleMatrix S = H.mult(P).mult(H.transpose()).plus(R);
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SimpleMatrix K = P.mult(H.transpose().mult(S.invert()));
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</syntaxhighlight>
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'''Equations'''
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<syntaxhighlight lang="java">
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eq.process("K = P*H'*inv( H*P*H' + R )");
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</syntaxhighlight>
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|}
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== Functionality ==
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{| class="wikitable" width="650pt" border="1" |
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!  Data Structures || Operations
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|-
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| style="vertical-align:top;" |
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* Fixed Sized
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** Matrix 2x2 to 6x6
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** Vector 2 to 6
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* Dense Real
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** Row-major
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** Block
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* Dense Complex
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** Row-major
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* Sparse Real
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** Compressed Column
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| style="vertical-align:top;" |
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* Full support for floats and doubles
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* Basic Operators (addition, multiplication, ... )
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* Matrix Manipulation (extract, insert, combine, ... )
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* Linear Solvers (linear, least squares, incremental, ... )
 +
* Decompositions (LU, QR, Cholesky, SVD, Eigenvalue, ...)
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* Matrix Features (rank, symmetric, definitiveness, ... )
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* Random Matrices (covariance, orthogonal, symmetric, ... )
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* Unit Testing
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|}. 
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{| class="wikitable" width="650pt" border="1" |
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! style="width: 40%;" | Decomposition || style="width: 15%;" |Dense Real || style="width: 15%;" |Dense Complex || style="width: 15%;" |Sparse Real  || style="width: 15%;" |Sparse Complex
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|-
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| LU || style="text-align:center;" | X || style="text-align:center;" | X || style="text-align:center;" | X ||
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|-
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| Cholesky LL || style="text-align:center;" | X || style="text-align:center;" | X || style="text-align:center;" | X ||
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|-
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| Cholesky LDL || style="text-align:center;" | X || style="text-align:center;" | || ||
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|-
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| QR || style="text-align:center;" | X || style="text-align:center;" | X || style="text-align:center;" | X ||
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|-
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| QRP || style="text-align:center;" | X || style="text-align:center;" | || ||
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|-
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| SVD || style="text-align:center;" | X || style="text-align:center;" | || ||
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|-
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| Eigen-Symmetric || style="text-align:center;" | X || style="text-align:center;" | || ||
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|-
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| Eigen-General || style="text-align:center;" | X || style="text-align:center;" | || ||
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|}
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Support for floats (32-bit) and doubles (64-bit) is available.  Sparse matrix support is only available for basic operations at this time.
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</center>

Latest revision as of 08:58, 7 July 2021


Ejml logo.gif

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.


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: v0.41
Date: July 7, 2021
v0.31 Upgrade Script
Change Log
Download
Manual
JavaDoc
Message Board
Bug Reports
FAQ
Kotlin
Acknowledgments
Performance
Users

News 2021

-
  • Read and write EJML in Matlab format with 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

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
    • Matrix 2x2 to 6x6
    • Vector 2 to 6
  • Dense Real
    • Row-major
    • Block
  • Dense Complex
    • Row-major
  • Sparse Real
    • Compressed Column
  • Full support for floats and doubles
  • 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
.


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
Eigen-Symmetric X
Eigen-General X

Support for floats (32-bit) and doubles (64-bit) is available. Sparse matrix support is only available for basic operations at this time.