Efficient Java Matrix Library

This article has multiple issues. Please help improve it or discuss these issues on the talk page. (Learn how and when to remove these messages) The topic of this article may not meet Wikipedia's notability guidelines for products and services. Please help to demonstrate the notability of the topic by citing reliable secondary sources that are independent of the topic and provide significant coverage of it beyond a mere trivial mention. If notability cannot be shown, the article is likely to be merged, redirected, or deleted. Find sources: "Efficient Java Matrix Library" – news · newspapers · books · scholar · JSTOR (January 2023) (Learn how and when to remove this message) A major contributor to this article appears to have a close connection with its subject. It may require cleanup to comply with Wikipedia's content policies, particularly neutral point of view. Please discuss further on the talk page. (January 2023) (Learn how and when to remove this message) (Learn how and when to remove this message)

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 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. The procedural style provides all capabilities of EJML and almost complete control over matrix creation, speed, and specific algorithms. The SimpleMatrix style provides a simplified subset of the core capabilities in an easy to use flow-styled object-oriented API, inspired by JAMA. The Equations style provides a symbolic interface, similar in spirit to Matlab and other CAS, that provides a compact way of writing equations.^[1]

EJML provides the following capabilities for dense matrices.

• 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, ... )
• Different Internal Formats (row-major, block)
• Unit Testing

Computing the Kalman gain:

eq.process("K = P*H'*inv( H*P*H' + R )");

Computing Kalman gain:

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);

Example of singular value decomposition (SVD):

SimpleSVD s = matA.svd();
SimpleMatrix U = s.getU();
SimpleMatrix W = s.getW();
SimpleMatrix V = s.getV();

Example of matrix multiplication:

SimpleMatrix result = matA.mult(matB);

Use of a DecompositionFactory to compute a Singular Value Decomposition with a Dense Double Row Major matrix (DDRM):^[2]

SingularValueDecomposition_F64<DenseMatrix64F> svd = 
    DecompositionFactory_DDRM.svd(true, true, true);

if (!DecompositionFactory.decomposeSafe(svd, matA))
    throw new DetectedException("Decomposition failed.");

DenseMatrix64F U = svd.getU(null, false);
DenseMatrix64F S = svd.getW(null);
DenseMatrix64F V = svd.getV(null, false);

Example of matrix multiplication:

CommonOps_DDRM.mult(matA, matB, result);

• List of numerical libraries

• Efficient Java Matrix Library (EJML) homepage