MediaWiki API result

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                        "user": "Peter",
                        "timestamp": "2023-09-24T04:33:04Z",
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                                "*": "__NOTOC__ \n\n<center>\n{| style=\"width:640pt;\"\n| align=\"center\" |\n[[File:Ejml_logo.gif]]\n|-\n|\n\nEfficient 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.\n\n\nEJML 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.\n|}\n\n{|\n| colspan=\"3\" align=\"center\" |\n{|style=\"font-size:120%; text-align:left;\"\n|-\n| '''Version:''' ''v0.43.1''\n|-\n| '''Date:''' ''September 23, 2023'' \n|-\n| [https://github.com/lessthanoptimal/ejml/blob/master/convert_to_ejml31.py v0.31 Upgrade Script]\n|-\n| [https://github.com/lessthanoptimal/ejml/blob/v0.43.1/change.txt Change Log]\n|}\n|- valign=\"top\" \n| width=\"220pt\" |\n{| width=\"200pt\" border=\"1\"  align=\"center\" style=\"font-size:120%; text-align:center; border-collapse:collapse; background-color:#ffffee;\"\n|-\n| [[Download|Download]]\n|-\n| [[manual|Manual]]\n|-\n| [http://ejml.org/javadoc/ JavaDoc]\n|}\n| width=\"220pt\" |\n{| width=\"200pt\" border=\"1\"  align=\"center\" style=\"font-size:120%; text-align:center; border-collapse:collapse;  background-color:#ffffee;\"\n|-\n| [http://groups.google.com/group/efficient-java-matrix-library-discuss Message Board]\n|-\n| [https://github.com/lessthanoptimal/ejml/issues Bug Reports]\n|-\n| [[Frequently Asked Questions|FAQ]]\n|-\n| [[Kotlin|Kotlin]]\n|}\n| width=\"220pt\" |\n{| width=\"200pt\" border=\"1\"  align=\"center\" style=\"font-size:120%; text-align:center; border-collapse:collapse;  background-color:#ffffee;\"\n|-\n| [[Acknowledgments|Acknowledgments]]\n|-\n| [[Performance|Performance]]\n|-\n| [[Users|Users]]\n|}\n|}\n\n== News 2023 ==\n\n{| width=\"500pt\" |\n| -\n|\n* SimpleMatrix is going through bit of a rejuvenation\n* SimpleMatrix has much improved support for complex matrices\n* Introduced ConstMatrix for when you want to restrict access to read only\n|}\n\n== Code Examples ==\n\nDemonstrations on how to compute the Kalman gain \"K\" using each interface in EJML.\n\n{| width=\"500pt\" |\n|-\n|\n'''Procedural'''\n<syntaxhighlight lang=\"java\">\nmult(H,P,c);\nmultTransB(c,H,S);\naddEquals(S,R);\nif( !invert(S,S_inv) )\n    throw new RuntimeException(\"Invert failed\");\nmultTransA(H,S_inv,d);\nmult(P,d,K);\n</syntaxhighlight>\n\n'''SimpleMatrix'''\n<syntaxhighlight lang=\"java\">\nSimpleMatrix S = H.mult(P).mult(H.transpose()).plus(R);\nSimpleMatrix K = P.mult(H.transpose().mult(S.invert()));\n</syntaxhighlight>\n\n'''Equations'''\n<syntaxhighlight lang=\"java\">\neq.process(\"K = P*H'*inv( H*P*H' + R )\");\n</syntaxhighlight>\n|}\n\n== Functionality == \n\n{| class=\"wikitable\" width=\"650pt\" border=\"1\" |\n!  Data Structures || Operations\n|-\n| style=\"vertical-align:top;\" |\n* Fixed Sized\n** Matrix 2x2 to 6x6\n** Vector 2 to 6\n* Dense Real\n** Row-major\n** Block\n* Dense Complex\n** Row-major\n* Sparse Real\n** Compressed Column\n\n| style=\"vertical-align:top;\" |\n* Full support for floats and doubles\n* Basic Operators (addition, multiplication, ... )\n* Matrix Manipulation (extract, insert, combine, ... )\n* Linear Solvers (linear, least squares, incremental, ... )\n* Decompositions (LU, QR, Cholesky, SVD, Eigenvalue, ...)\n* Matrix Features (rank, symmetric, definitiveness, ... )\n* Random Matrices (covariance, orthogonal, symmetric, ... )\n* Unit Testing\n|}.  \n\n\n{| class=\"wikitable\" width=\"650pt\" border=\"1\" |\n! style=\"width: 40%;\" | Decomposition || style=\"width: 15%;\" |Dense Real || style=\"width: 15%;\" |Dense Complex || style=\"width: 15%;\" |Sparse Real  || style=\"width: 15%;\" |Sparse Complex\n|-\n| LU || style=\"text-align:center;\" | X || style=\"text-align:center;\" | X || style=\"text-align:center;\" | X || \n|-\n| Cholesky LL || style=\"text-align:center;\" | X || style=\"text-align:center;\" | X || style=\"text-align:center;\" | X || \n|-\n| Cholesky LDL || style=\"text-align:center;\" | X || style=\"text-align:center;\" | || || \n|-\n| QR || style=\"text-align:center;\" | X || style=\"text-align:center;\" | X || style=\"text-align:center;\" | X || \n|-\n| QRP || style=\"text-align:center;\" | X || style=\"text-align:center;\" | || || \n|-\n| SVD || style=\"text-align:center;\" | X || style=\"text-align:center;\" | || || \n|-\n| Eigen-Symmetric || style=\"text-align:center;\" | X || style=\"text-align:center;\" | || || \n|-\n| Eigen-General || style=\"text-align:center;\" | X || style=\"text-align:center;\" | || || \n|}\nSupport for floats (32-bit) and doubles (64-bit) is available.  Sparse matrix support is only available for basic operations at this time.\n</center>"
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