change.txt

Change log for EJML

Date format: year/month/day

----- Version 0.43
2023/XX/XX

- TODO concurrency for complex https://github.com/lessthanoptimal/ejml/issues/176
- TODO Support Java Modules

----- Version 0.42
2023/02/10

- MatrixIO
  * Fixed Matrix Market save. Thanks Adam Logowski for pointing out the issue
  * Vector matrices are supported in matrix market.
  * Removed unsafe and deprecated functions loadBin() and saveBin()
- Negative rows or columns are now illegal
  * Thanks JozsefKutas for pointing out the issue
- Complex Matrix Support
  * Added elementMultiply(mat, mat, mat)
  * Added elementDivide(mat, mat, mat)
  * Added elementPower(math, real, mat)
- ConstMatrix
  * Interface that only implements operations that are read only.
  * Requested by many People
- SimpleMatrix
  * Thanks JozsefKutas for getting this needed refresh started
  * Removed deprecated functions
  * Updated JavaDoc
  * API Consistency: getNumRows, getNumCols
  * Added getRow(), getColumn(), setRow(), setColumn() that works with SimpleMatrix directory and handles all types.
  * Added stripReal() and stripImaginary()
  * Added conjugate() and magnitude()
  * Added fillComplex(), transposeConjugate(), sumComplex(), determinantComplex(), traceComplex()
  * Added elementOp(()->{}) for real and complex matrices. Allows user defined element wise operators.
  * Added simplified functions to create random matrices
  * Modified concurrent trigger to take in account size of both input matrices
- CommonOps_DDRM
  * Added elementBoolean(matrix, lambda) for arbitrary element-wise boolean operators
- MatrixMatrixMult_ZDRM
  * Added concurrent versions of several matrix multiplication ops
- Handling of zero sized matrices
  * Unit tests ensure decompositions and linear solvers can process 0x0, Mx0, 0xN matrices.
  * Requested by JozsefKutas and others. Note: I've never personally had a desire for this...
  * Only rigorously tested with DDRM
- ConvertMatrixData
  * Can now convert boolean matrices into different data types
- Various
  * Ops classes with all static members now have private constructors to drive home the concept of static functions.

----- Version 0.41.1
2022/12/04

- Build
  * Java language is now 17 and binary compatibility is 11
  * Gradle will now download the specified JDK prior to building
  * Uses new external library for auto code generation (no change in logic)
  * All unit tests must extend EjmlStandardJUnit
  * Unit tests which print to stdout will now fail
  * for-each loops being baned in performance code is now enforced with a unit test
- MatrixFeatures_DDRM
  * Fixed isSymmetric() Thanks HGuillemet for reporting the divide by zero bug
- MatrixIO
  * Now defaults to Locale.US for printf. Fixes issue where commas changed.

----- Version 0.41
2021/07/07

- Build
  * Switched to 'maven-publish'
- Solve now produces a correct and easily understood error message
  * Thanks CLOVIS-AI for pointing out the issue
- Regression Testing
  * Added runtime regression using existing JMH benchmarks
- Refactor set(obj) -> setTo(obj)
- CommonOps
  * Fix a rather glaring bug in rref(). Thanks KayakDov
- SimpleMatrix
  * Automatic type conversion. Thanks Florentin Dörre
  * Automatic switching to concurrent algorithms when available

----- Version 0.40
2020/11/04

- Speed up build by only creating EjmlVersion.java when autogenerate is called
- Switched to `org.jetbrains:annotations` from `com.google.code.findbugs:jsr305`
  * Licensing ambiguity and lack of support
  * Thanks Graham for pointing out the issue and the PR
- Added static analysis to code base
  * Error Prone https://errorprone.info/
  * Updated auto code generator to mark code as auto generated for better static analysis
- Strictly enforce usage of @Nullable to public API
  * https://github.com/uber/NullAway
  * Private class fields mostly have NullAway disabled because it doesn't model how they are coupled resulting in pointlessly verbose code
- Fixed build task oneJarBin.
  * Thanks liuyang0717 for the bug report
- Added reduce operations for sparse matrices.
  * Thanks Florentin Dörre for the donation
- Added Semirings for Graph Operations
  * Thanks Florentin Dörre for the donation
  * Thanks Gabor Szarnyas and @breandan for reviewing
- CommonOps_DDRM and CommonOps_DSCC
  * Updating functions to return output if null and use common input sanity check functions
- CommonOps_DSCC
  * duplicatesAdd() is a function which will add elements that are duplicated, turning it into a valid matrix again
  * Removed the functions below since they had a runtime complexity of O(N^2) relative to matrix size instead of O(N)
    - multTransA(S,S,S), multTransB(S,S,S), innerProductLower(S,S,S)
    - Thanks Florentin Dörre for first noticing the performance issue
  * Speed up multTransAB(S,D,D), multTransA(S,D,D), multTransB(S,D,D) by a large margin
- DMatrixSparseCSC
  * If sorted, a binary search is used to lookup rows. Thanks Florentin Dörre.
- ReadMatrixCsv
  * Thanks DEDZTBH for fixing an indexing error when reading complex data types
- Added Concurrent Algorithms
  * First pass at adding this capability. Decompositions and other items need more optimization.
  * Control threads with EjmlConcurrency
  * Must call concurrent functions and factories directly. E.g. CommonOps_DDRM -> CommonOps_MT_DDRM
  * Thanks to mroodschild for getting this effort started with the proof of concept
- Examples
  * New concurrency example
  * New block matrix example
  * New Graph path finding example
- Benchmark
  * Improved coverage of JMH benchmarks as part of an effort to automate the detection of runtime regressions

----- Version 0.39
2020/04/06

- Fixed deserialization issue with SimpleMatrix. Thanks AngledLuffa for the bug report!
- Added sanity checks to matrix size. Thanks LsKoder for the suggestion!
- Added support for sparse float matrices FSCC
  * It appears that the current solvers in EJML based on DSparse are less numerically stable than their
    dense counter parts. More analysis is needed.
  * DXSparse and EJML produce similar results for double precision and doesn't look like DXSparse
    support single precision making it hard to compare. Matlab doesn't support single precision.
  * Unit tests were modified to ensure more numerically stable matrices were passed in
- Fixed bug with DSCC.multAddColA() where col_idx wasn't filled out all the way if there were zero columns
- Added RandomMatrices_DDRM.triangularLower()
- Preview of Kotlin extensions support
- Added a Matrix.create(rows,cols) function
- Added sanity checks to some functions which ensures the same variable isn't passed in twice
  * Thanks for 4kills for the implicit assumption
- MatrixIO
  * Can read and write Matrix Market file format
    https://math.nist.gov/MatrixMarket/formats.html
  * Can parse Matlab formatted text matrix output
- Build
  * Uses Gradle 6.3
  * Requires Java 11 to build but outputs Java 8 byte code

----- Version 0.38
2019/03/13

- Examples
  * Added sparse performance tips
- SimpleOperations
  * Added multTransA
  * Added another variant of plus
- Created SimpleSparseOperations
  * Operations specific to sparse matrices
- CommonOps_DDRM
  * Added symmLowerToFull and symmUpperToFull
  * Added divideRowsCols
  * Added abs() Thanks Víctor Fernández for the pull request
- GrowArray
  * Clarified meaning of grow() by renaming it growInternal() Thanks Florian Enner for pointing out the problem
- Added Matrix.zero() so that all matrices now implement zero()

----- Version 0.37.1
2018/12/25

- Equations
  * Fixed bug with "a = b' - c"
- CommonOps_DDRM
  * scaleRow() and scaleCol()
- Added FancyPrint class

----- Version 0.37
2018/11/11

- CommonOps_DSCC
  * elementMult() resizes internal data. In a test it went from 5600ms to 8ms to compute
    Thanks Gabor Szarnyas for pointing out this issue
  * Added fully sparse solve function
  * fill(), sumCol(), minCol(), maxCol(), sumRow(), minRow(), maxRow()
- CovarianceOps_DDRM
  * Bug fix with 1x1 matrices. Thanks Ciamac
- SimpleMatrix
  * Added more support for sparse and sparse-dense functions
- CommonOps_DDRM
  * invertSPD(), solveSPD()
  * removeColumns()
- UnrolledCholesky
  * Fixed sized matrices
  * Dense row major

----- Version 0.36
2018/09/29

- Fixed Size Matrix
  * Vector.set() for entire vector
  * Outer vector product
  * Assign a matrix and vector from an array
- SingularOps_DDRM
  * Simpler methods and no need to create a decomposition for basic operations
- Equations
  * You can now chain some operations
- CommonOps_DDRM
  * New extract() where upper extents are implied

----- Version 0.35
2018/08/24

- UtilEjml
  * solver = safe(solver) ensures solvers of any type don't modify their inputs
  * printFancy() best formatter of floating point numbers to string
- Matrix.print()
  * New default format and added the ability change the format
  * Printing using printFancy()
  * If format is specified as "matlab" it will print out in a format which can be pasted into Matlab
  * Fixed formatting of complex matrices
- Reshape output instead of exception
  * Changed dense matrix multiply to have this behavior
- ConvertDArrays
  * Methods for converting 2D arrays to and from EJML data types
- MatrixVectorMult_DSCC
  * Added mult(Matrix,Vector,Vector)
  * Added multAdd(Matrix,Vector,Vector)
  * Added mult(Vector,Matrix,Vector)
  * Added innerProduct(Vector,Matrix,Vector)
- CommonOps_DSCC
  * Added multAdd(sparse,dense,dense)
  * Added multTransA(sparse,dense,dense) multAddTransA(sparse,dense,dense)
  * Added multTransB(sparse,dense,dense) multAddTransB(sparse,dense,dense)
  * Added multTransAB(sparse,dense,dense) multAddTransAB(sparse,dense,dense)
  * Added extractDiag(sparse,dense)
  * Added innerProductLower(sparse,sparse) B = A'*A. Only lower triangle is saved in B
  * Added symmLowerToFull(sparse,sparse) for converting a lower triangular matrix into a symmetric matrix
  * Added column operations
  * Added multRows() divideRows() multCols() divideCols() multRowsCols() divideRowsCols()
- Added LinearSolverSparseSafe
- LinearSolverSparse
  * Changed lockStructure() to setStructureLocked( boolean )
- MatrixSparseTriplet
  * No longer uses individual classes for each element and instead uses an array
  * Java bloats classes and this should result in a large reduction in memory usage
- Sparse Solver
  * Added functions for solving sparse matrices
  * Improved efficiency of Triangle solve
  * Triangle sparse solve supports lower and upper tall matrices. Plus the transpose.
- SparseMatrix
  * added reshape(rows,cols) so that you don't need to specify nz_length when it doesn't matter.
- Improved exception messages for a few functions
- Changing it so that the output of various functions is reshaped so that it fits the input to make
  development less tedious
- SimpleMatrix
  * Fixed serialization
    - Thanks fa-dev1 for reporting the issue
  * Added elementMinAbs() because elementMaxAbs() was already included
- Examples
  * Improved Levenberg-Marquardt
    - Made it more like a "real" implementation and cleaned up the code

----- Version 0.34
2018/04/13

- SimpleMatrix
  * Added createLike()
  * Removed most hard coded matrix types internally
  * Change simple matrix's constructor to take in an array to remove confusion over float and double
  * Fixed bug in concatRows and concatColumns
  * Renamed set(double) to fill(double)
  * Made set(row,col, value) generic
  * Added automatic conversion of matrix types during an operation.
    - Very simple heuristic. Only ensures that everything is the same matrix type
- Fixed Sized Matrices
  * Added multiplication and scale operations
- QRP
  * Dense real version was incorrectly scaling the threshold for singularity detection
- Created SolveNullSpace
  * Make it easier and faster to find the null space
  * QR, QRP, and SVD
- Equation
  * Added print() This will print to stdout the results of what's inside
  * Fixed bug. Can handle comma inside a brucket block that's in a function call.
    "b=normF([[1 2],1])"
  * Added random matrices and ability to specify the seed
    rand,randn,rng
  * Added ability to print expression's output
  * Added automatic conversion from FMatrixRMaj to DMatrixRMaj
- EjmlVersion
  * Contains version information

----- Version 0.33
2018/01/17

- BMatrixRMaj
  * Added fill()
  * Added sum()
- Relaxed length requirements on permutation vector
  * Thanks Florian Enner for the pull request
- Added MatrixFeatures_D which work on all double real matrices
  * isEquals()
  * isIdentical()
- CommonOps_*DRM
  * More functions throw MatrixDimensionException
  * Some functions will resize input when there's a miss match now
- Operations
  * Prints a useful error message when you try to add,subtract,multiply a 1x1 matrix against a NxM matrix
- Added CommonOps_BDRM
  * transposeSquare()
- CSV Parsing support
  * Supports all dense types now
  * Refactored to reflect new matrix names
  * Added support for writing and reading sparse matrix in CSV

----- Version 0.32
2017/09/18

- Thanks to HEBI Robotics for sponsoring the addition of sparse solvers!
  * http://hebirobotics.com/
- Examples
  * Added new example showing how to use results from an LU decomposition to solve
- CommonOps - RMaj
  * Added concatColumns(A,B)  concatRows(A,B)
- Simple Matrix
  * Added concatColumns(A,B)  concatRows(A,B)
  * Added rows(begin,end) <-- extracts the specified rows
  * Added cols(begin,end) <-- extracts the specified columns
  * Fixed issue with bits() and derived classes. Thanks Florian Enner
  * Redesigned internals to make it more module and easier to add support for different types of matrices
- DMatrixSparseCSC
  * Fixed bug in set(row,col,val) where the first time a non-zero element was added it would assign the wrong value
    Thanks GeoffreyGuettier
  * Added growMaxColumns()
- MatrixFeatures_DSCC
  * Added isSymmetric()
  * Added isOrthogonal()
- ConvertDMatrixStruct
  * Moved functions from ConvertDMatrixSparse into this class
  * Added tolerance for zero when converting from dense to sparse
- RandomMatrices_DSCC
  * Added symmetric()
  * Added symmetricPosDef()
- CommonOps_DSCC
  * Added permuteSymmetric()
  * Added checkDuplicates()
  * Added isPositiveDefinite()
  * Added concatColumns()
  * Added concatRows()
  * Added extractColumn()
  * Added dotInnerColumns()
  * Added solve() dense
  * Added invert() dense
  * Added det()
  * Added removeZeros()
  * Added changeSign()
  * Added elementSum()
  * Added elementMult()
  * Added trace()
  * Fixed various bugs
- CommonOps_DDRM
  * Added permuteRowInv()
- Sparse Decompositions and Solvers
  * Cholesky - Up Looking
  * LU
  * QR
- Sparse Triangular Solver
  * Added solveTranL()
  * Added column pivots to one of the solvers
- Data Types
  * Added DScalar and IScalar
  * Added *GrowArray to act as workspace in sparse algorithms
- SolvePseudoInverseSvd_DDRM
  * Recycles memory for U and V internally
  * Thanks Florian Enner for pointing this out

----- Version 0.31

2017/05/18

- Changed minimum Java version from 6 to 7
- Added SimpleEVD.getEigenvalues()
- Added SimpleSVD.getSingularValues()
- Fixed issue with generics and SimpleEVD and SimpleSVD
- Auto generated float 32-bit support of all 64-bit code
- SimpleMatrix
  * Added support for float 32-bit matrices
  * Replaced extractDiag() with diag() and changed behavior.
- Fixed Sized Matrix
  * Added MatrixFeatures
  * Added NormOps
  * FixedOps
    - Discovered a bug in a unit test
    - Fixed bugs in elementAbsMax() elementAbsMin() trace()
    - Improved the speed of element-wise max and min operations
- New naming for matrices (see readme)
- New naming for operation classes (see readme)
- Operations API
  * added minCol(), maxCol(), minRow(), maxRow()
- Sparse matrix support for real values
  * Compressed Sparse Column (CSC) a.k.a. Compressed Column
  * Triplet
  * Basic operation up to triangular solve
- A script has been provided that will perform most of the refactorings:
  convert_to_ejml31.py
- Fixed a minor printing glitch for dense matrices.  There was an extra space
- Equations
  * Assignment to a submatrix now works with variables
    A((2+i):10,1:20) = 5   <--- this works now
  * Added sum(), sum(A,0), sum(A,1)
  *       min(A,0), max(A,0), min(A,1), max(A,1),
- Modules now have "ejml-" as a suffice to avoid collisions with other libraries
- equations module has been moved into ejml-simple for dependency reasons

----- Version 0.30

2016/11/09

- Thanks Peter Fodar for fixing misleading javadoc
- Fixed bug in computation of eigenvectors where the first eigenvalue was complex it would get messed up
  *  Thanks user343 for reporting the bug!
- Complex matrix multiplication
  * added multTransA variants
  * added multTransB variants
  * added multTransAB variants
- Added the following complex decompositions
  * Hessenberg Similar Decomposition
  * Tridiagonal Similar Decomposition
- Added MatrixFeatures.isLowerTriangle()
- Added createLike() to all matrices
  * Creates a new matrix that is the same size and shape.  filled with zeros initially
- Fixed CRandomMatrices.createHermitian()
- Fixed CMatrixFeatures.isHermitian()

----- Version 0.29

2016/01/23

- CommonOps
  * Added extract with int[] for rows and columns or just indexes
  * Added support for matrix element-wise inequality functions and boolean matrix
    - This does go a little bit beyond the strict "linear algebra" only scope
    - Primarily driven by adding features to Equations
- Equations
  * Can now create vectors using integer sequences
    a = [5:10]
    a = [ 2 3 4 0,-2, 4 ]

    Commas are needed when using negative numbers or else it will think you're doing a-b instead of a and -b.

  * Now now reference submatrices using integer sequences.  Works in 1D and 2D.
    b = A(1 2 3 2 0 , 4: )   <-- 2D sub-matrix
    c = A(4:3:20 3 4 5)      <-- reference elements by index in the sequence. c will be a row matrix

  * Sub-matrix also works for assignment
    d(3,5:) = f

  * Added macros
    Example: "macro m2m(a) = a*2*a"

- Added DenseMatrixBool
  * boolean matrix

----- Version 0.28

2015/07/09

- Equations
  * Fixed bug where bounds for a submatrix-scalar assignment was being checked using col,row instead of row,col
    Thanks lenhhoxung for reporting this bug
- FixedOps
  * Added vector equivalents for all element-wise matrix operations
  * Added multAdd operators

----- Version 0.27

2015/04/01

- Added SimpleMatrix.randomNormal() for drawing numbers from a normal distribution with zero mean
- Added EjmlUnitTests.assertEquals() and similar for SimpleMatrix
- Removed DenseMatrix64F.setReshape()
  * Matrix.set(matrix) will now reshape the matrix that's being assigned
- Triangle quality now just uses diagonal elements to scale results
- Support for complex matrices
  * Thanks IHMC (http://ihmc.us) for funding parts of this addition
  * Basic operations (e.g. multiplication, addition, ... etc)
  * LU Decomposition + Linear Solver
  * QR Decomposition + Linear Solver
  * Cholesky Decomposition + Linear Solver
  * Square Matrices: inverse, solve, determinant
  * Overdetermined: solve
- ComplexMath64F
  * Added sqrt(Complex64F)
- Tweaked matrix inheritance to better support the addition of complex matrices
- Added RandomMatrices setGaussian() and createGaussian()
- Changed how SimpleMatrix computes its threshold for singular values
  * Farley Lai noticed this issue
- Added SingularOps.singularThreshold()
- Added no argument rank and nullity for SVD using default threshold.
- SimpleMatrix.loadCSV() now supports derived types
- Added primitive 32bit data structures to make adding 32bit support in the future smoother
- Equation
  * 1x1 matrix can be assigned to a double scalar
  * When referencing a single element in a matrix it will be extracted as a scalar and not a 1x1 matrix.
  * Added sqrt() to parser
  * lookupDouble() will now work on 1x1 matrices
- CommonOps
  * Added dot(a,b) for dot product between two vectors
  * Added extractRow and extractColumn
- FixedOps
  * Added extractRow and extractColumn.  Thanks nknize for inspiring this modification with a pull request
  * Added subtract and subtractEquals.  Thanks nknize for the pull request
- Added determinant to Cholesky decomposition interface
- Added getDecomposition() to LinearSolver to provide access to internal classes,
  which can be useful in some specialized cases.  Alternatives were very ugly.

----- Version 0.26

2014/9/14

- Switched most of the build system over to Gradle
- Equations
  * Symbolic way to perform linear algebra
  * Similar to Matlab/Octave
- SimpleMatrix
  * Added plus( double )
  * Added elementDiv()
- Linear Solvers
  * Exposed inner decomposition algorithms and some data structures
- CommonOps
  * Trace will now work on non-square matrices
  * changeSign() has a two argument version
  * left and right double-matrix division.
      *** WARNING **** The meaning of CommonOps.divide(double,Matrix) and similar has changed
  * Added subtract() matrix-double and double-matrix
- QRP yet again uses a relative threshold.  No idea why it was set to a global one
- Matrix Multiplication now fully supports multiplication by rows or columns with zero
- Element-wise pow, exp, log to CommonOps and SimpleMatrix
- High level interface for LDL
- Renamed Principle to Principal in PCA
  * Thanks sergei.skarupo for being the first person to point out this error
- Fixed case where SolvePseudoInverseSvd would blow up if input matrix was zero
- SVD and EVD can gracefully handle matrix with size 0

----- Version 0.25

2014/6/13

- Fixed bug in CovarianceRandomDraw where it was modifying the input matrix
  * Thanks Alexandre Bouchard for finding this bug
- Added suffix to low level implementations so that you can tell by looking at the class name the type
  of matrix it takes in as input
  * D64 = DenseMatrix64F
  * B64 = BlockMatrix64F
- Moved interfaces into their own package.  Before they resided in dense.
- Fixed bad matrix dimension check in LinearSolverChol
  * Added unit tests which can catch the error
  * Thanks Illya Kokshenev for finding and reporting
- SimpleMatrix
  * inverse and solve now check to see if output contains uncountable numbers.
  * Thanks sylvain.rouard for the suggestion

----- Version 0.24

2013/12/26

- CHANGED LICENSE FROM LGPL to Apache 2.0
  * Don't agree with Free Software Foundation's interpretation for how the LGPL applies to Java jars.
  * Even if it did behave the way I wanted it to, it wouldn't do much other than scare some people away.
- Added fixed sized square matrices and vectors
  * Auto code generator for standard operations
  * Matrix 2x2, 3x3, 4x4, 5x5, 6x6
  * Vector 2, 3, 4, 5, 6
- Created an interface for Matrix64F and renamed the old one into ReshapeMatrix64F
- CommonOps:  Added checks which ensure the input matrix is not modified for det() and inv()

----- Version 0.23

2013/06/21

- Modified Matrix64F so that it can be serialized using beans.  Added setNumRows() and setNumCols()
- Added user configurable threshold in SolvePseudoInverseSvd

----- Version 0.22

2013/04/09

- Fixed bug in MatrixFeatures.isPositiveDefinite() where the input was being modified
  * Added a check for isModified() flag in this and several other functions
  * Thanks Eriklan Dodinh for reporting the bug
- MatrixIO.loadCSV() now will throw IOException() if an incorrect matrix size is specified
  * Thanks Ioannis P. for reporting this bug
- LUDecompositionNR now returns false for singular matrices.
  * Thanks Luke Nezda for reporting the bug
- Moved LUDecompositionNR into experimental directory since its use is not recommended, but is still a good algorithm.
- Clarified ordering of coefficients in polynomial root example
  * Thanks Rahul for getting confused by the lack of documentation

----- Version 0.21

2012/12/04

- Removed Android build option from ant because there is no need for such an option
- Changed behavior of CommonOps.rref() so that numUnknowns <= 0 causes it to be set to a reasonable default
- Fixed bug in SimpleSVD where getSingularValue() did not return ordered singular values
- SingularOps.nullVector() now lets you specify if it is the left or right null space
- Added SafeSvd, which is a wrapper class that ensures that the input to SVD is not modified

----- Version 0.20

2012/08/22

- Fixed bug in SingularOps.nullity where it did not handle wide matrices correctly. 
  * Thanks arnavkumar for pointing out the bug
- Reworked null-space
  * SingularOps.nullSpace() is now nullVector()
  * SingularOps.nullSpace now returns the entire null space not just one vector
  * Thanks arnavkumar for pointing out the non-standard implementation
- Changed behavior of MatrixFeatures.isDiagonalPositive() so that it will return false if NaN is found
- Under certain conditions SvdImplicitQrDecompose will use BidiagonalDecompositionTall instead of the default
  algorithm.
- Moved factories and related interfaces into their own package outside of the "alg" package, which was intended for
  specific implementations.
- DecompositionFactory has been cleaned up by reducing the number of functions, making input parameters more consistent,
  and improved JavaDoc
- Changed CommonOps.set(matrix,value) to CommonOps.fill(matrix,value)
- Added function QRPDecomposition.setSingularThreshold() so that the user has more control over this important threshold
  * The singular threshold in QRP is now absolute and not automatically relative.
  * To replicate the original behavior do the following:
       decomp.setSingularThreshold( CommonOps.elementMaxAbs(A)*UtilEjml.EPS )
- SvdImplicitQrDecompose now takes in a parameter which specifies if tall bidiagonal decomposition can be used over
  concerns of its stability.  These concerns are mostly theoretical since a few simple tests show the stability to be
  almost the same.
- SimpleBase now implements Serializable
- Accessors in SingularValueDecomposition now can take in an optional storage matrix.
- Removed support for LinearSolver with Gauss-Jordan elimination
  * The provided implementation was very slow and didn't utilize any of the advantages of GJ
- Added support for transforming an augmented matrix into Reduced Row Echelon Form (RREF)
  * Implementation using Gauss-Jordan with row pivots
  * Added CommonOps.rref()

----- Version 0.19

2012/04/23

- Added example showing file IO to examples directory
- Changed MatrixIO.saveXML and MatrixIO.loadXML to saveBin and loadBin because it uses a binary format not XML
- Added save/load CSV to SimpleBase
- Implemented VectorVectorMult.rank1Update
  * Before the function did nothing
- Fixed bug in LinearSolverQrHouseCol where it was not handled a change in matrix size correctly
- Added new variant of SingularOps.descendingOrder() which takes an array of singular values as input
- Added reshape(row,cols) because adding false to reshape(row,cols) all the time was annoying me.
- Added multInner() and multOuter() for performing inner and outer matrix products.  Faster than
  using generalized algorithms.
- Changed behavior of VectorVectorMult.innerProdA so that it does not require 'A' to be square
- Improved Pseudo Inverse
  * Refactored by changing package and renaming
  * Added QR pivot based pseudo inverse classes
- Improved QR Pivot so that it has better stability
  * Normalization is done on a column by column basis instead of by the whole matrix.
- QR decomposition using extracted columns now invokes more generalized code for householder operations
  * Should improve performance for larger matrices, and slightly degrade for small
- Added SimpleMatrix.setRow() SimpleMatrix.setColumn()
  * Writes elements in an array to a row/column.
- Moved decomposition and linear solver factories and interfaces into a factories package.
  * Was placed in alg.dense package, which is for specific implementations

----- Version 0.18

2011/12/04

- Added support for reading and writing CSV formatted matrices
  * Requested by various people.
- Changed SolvePseudoInverse to use SVD, making it much more robust and will never fail now.
  * Same behavior as matlab pinv() function
  * Requested by mwolff.
  * Updated CommonOps.pinv() to use SolvePseudoInverse
  * Added pinv() to SimpleMatrix
- Added getSingularValue() to SimpleSVD class.
- Rewrote JavaDOC for reshape() because it was miss leading
  * sh10151 pointed this out
- Removed one of the CommonOps.insert() functions since it was identical to CommonOps.extract()
- Created optimized versions of CommonOps.extract
  * Seems to boost performance on small matrices but not large
- Added QR decomposition with column pivots
- Added solver which uses QR with column pivots
- BidiagonalDecompositionTall now passes all of its unit tests

----- Version 0.17

2011/06/28

- Moved general and symmetric EVD to DecompositionFactory from EigenOps
- Moved show() from MatrixIO to MatrixVisualization to help with Android development.
  * added "jar nogui" to ant build script which will exclude GUI related files.
- Added CommonOps.elementDiv()
  * Thanks to leokury for the suggestion
- Many functions now handle NaN more intelligently in MatrixFeatures
  * Thanks to kaspar.thommen for pointing out these flaws.
- Adding Maven support
  * Can now download from the central repository
  * Requested by soren
- Added sumRows() and sumCols() to CommonOps
  * Requested by b.broeksema
- Fixed bug in inducedP2 where the max singular value was not always returned
  * Found by Antonino Freno

----- Version 0.16

2011/02/23

- Removed SVD code based on NR
- Fixed bug in BlockMatrix64HouseholderQR.applyQ()
 * improved unit test to detect the bug
- Changed contract for LinearSolver and added functions to determine if the input
  is being modified or not.
- Genericified LinearSolver
  * Removed LinearBlockSolver
- Genericified DecompositionInterface
  * Removed block decomposition interfaces
- Clarified behavior of functions in MatrixFeatures.
- Added EjmlUnitTests
  * converted many internal unit tests to use this class
- Changed MatrixIO to take a PrintStream as an input
- Adding support for toString()
- EVD and SVD now modify their inputs by default
- Added unit test for PCA
- Added in-place converting to and from row-major to block matrices.
- Trangular block solver can handle unaligned input
- Modified isIdentical and added isEquals to MatrixFeatures.
  * Resulting from a discussion with Kaspar Thommen
- SimpleMatrix has been made more easy to extend by adding a protected function that declares
  new instances of SimpleMatrix. Now function calls will return the correct type.
- TridiagonalSimilarDecomposition is now an interface and related code has been made more
  general purpose.
- Added a QR decomposition that supports bidiagonalization for tall matrices
  * Results in significantly faster SVD when U is not needed.
  * Not used yet in SVD because QR decomposition without column pivots is unstable for singular matrices.
- Modified BidiagonalizeDecomposition interface
- SVD and EVD QR algorithm take in diagonal and off diagonal as inputs not the matrix.
- Added dense matrix copy triangle and isEqualsTriangle
- Changed logo to more accurately represent a Householder reflector.
- Pushed SimpleMatrix into its own package.
  * To make it easy to extend, SimpleMatrix now extends SimpleBase
  * Added dot() and isVector() is SimpleMatrix.
- LinearSolverFactory and DecompositionFactory now take in the matrix's size
- SimpleMatrix now accepts variable arguments.

----- Version 0.15

2010/11/20

- Minor speed ups in LU, determinant, transpose
- Fixed a bug in MatrixComponent.  Negative elements were not handled correctly.
- More clearly defined what LinearSolver.quality() is and made it invariant of the matrix's scale.
- Added wrap to DenseMatrix64F for handling raw data.
- Added PrincipleComponentAnalysis example
- Changed DecompositionInterface
- Added unsafe_get() and unsafe_set() to Matrix64F and implementing classes.
- Cholesky and QR decompositions for a block matrix and associated helper functions.
- Simplistic functions have had the hand inlining removed. Suggested by Kaspar Thommen.
  * After marking it was found that sometimes more complex functions had their performance
    significantly degraded after they started to use very light weight wrappers.
  * In some cases it seems to slightly improve performance when setters/getters are used.
  * Yes these changes were statistically significant.
- Changed behavior of CommonOps.extract() to make its behavior match other submatrix operations better
- Added the ability to create a new matrix if a null dst is provided in several CommonOps functions.
- Moved examples into their own java package.  Makes selectively running those unit tests easier in IntelliJ
- Added elementMult() to SimpleMatrix. Suggested by Nikita Rokotyan.
- Fixed bug in the different QR decomposition implementations for compact QR.
  * Added compact QR to generic tests
- Fixed bug in LinearSolverFactory.symmetric() where LU instead of cholesky was being created.
- EjmlParameters.MEMORY provides a way for it to select algorithms which are fast but memory hogs or
  slower but more memory efficient.

----- Version 0.14

2010/08/07

- Removed left over debugging print statement from SvdImplicitQrAlgorithm.
- For reflectors (used in QR) code has been added that can switch between two normalization
  methods.  This can significant speed things up and improve accuracy.
- Added underflow stability benchmarks for some decompositions
- Correct some issues in SVD and Symm EVD
- Seed up SVD by removing need for some divisions
- Symm EVD now uses the Wilkinson shift
- Fixed a bug in kron().
- Added several new functions to SimpleMatrix and cleaned up function names.
- Fixed a bug in QR decomposition where compact flag was incorrectly handled.
- Added QRExample* to example source code to demonstrate using extract and insert
- All QR decompositions now works on matrices with any shape
- Added set() to MatrixIterator
- Added additional checks to matrix multiply that makes sure 'c' is not the same matrix as 'a' or ''b'
  - Suggested by xcolwell
- Moved IO functions to MatrixIO
  - Provide a way to save/read DenseMatrix64F and SimpleMatrix to/from a file.
- Fixed and added more block matrix operations.
- Creates a new class to store transpose algorithms.
   * Including a new block one that is about 40% faster on large non-square matrices.
- Added quality() to LinearSolver to provide a quick way to validate results.
- Added MatrixIO.show() for visually showing a matrix's state.

----- Version 0.13

2010/07/08

- Fixed SingularOps.descendingOrder() bug.
   * Thanks xcolwell for reporting it.
- Fixed a bug in RandomMatrices.createOrthogonal()
- Cleaned up Matrix64F and D1Matrix64F
- Updated java doc for several classes
- Added SVD and EVD to SimpleMatrix
- SVD can now selectively compute U and V
- SVD is computed all at once.
   - Seems to perform slightly faster than original code and is simpler.
   - There is a loss in performance for 2 by 2 matrices.  
- SVD getU() and getV() can now indicate if the transpose is returned or not.
- SVD fixed issue dealing with very small numbers canceling out and causing a divided by zero error
- SVD changed exceptional shift to use a random rotation
- Moved quality() functions inside of DecompositionFactory
- Symmetric EVD
  * Fixed rare problem that could happen if two eigenvalues are identical and off diagonal element is small it will never converge.
  * Added code to make it more robust to very small numbers
- Generic EVD
  * Fixed long standing issue with eigenvectors and repeat eigenvalues
  * Made unit test tolerant to small round off errors causing an eigen value to become imaginary
- Added a version string to UtilEjml



----- Version 0.12

2010/06/17

- Added PolynomialFit example code.
- Updated comments to reflect the addition of LinearSolvers
- Added MatrixIterator to provide another way of accessing elements inside a matrix
- Can just compute eigenvalues.
- Fixed an array index out of bounds error when computing general eigenvectors
- Improved accuracy of general EVD when computing eigenvalues and eigenvectors
  when dealing with nearly identical eigenvalues.  Thanks to Ex'ratt for finding this and the previous problem.
- Auto code for unrolling determinant by minor.
- Auto code generator for unrolling inverse by minor
- Created experimental code directory
  
----- Version 0.11

2010/04/07

- Updated LevenbergMarquardt example.
- Added the ability to save the old data in reshape() if the matrix grows in size.
- Added Kronecker product
- Added LinearSolverFactory
- Changed constructor in DenseMatrix64F so that var-args can be used.
- Can add random numbers to a matrix
- Added null space to SingularOps
- Added a function in SingularOps to rearrange the SVD such that the singular values are in descending order.

----- Version 0.10.1

2010/02/18

- Fixed a bug with large symmetric matrices where eigen pairs would be incorrectly associated with each other.
- Made the general eigenvalue decomposition algorithm less buggy.  It can now handle the case where
  eigenvalues appear in a different order the second time, but doesn't solve for repeat values correctly.

----- Version 0.10

2010/02/16

- fixed a LU pivot matrix bug
- SVD Implicit QR algorithm (much faster than old)
- Optimized rank1update for large matrices
- Various refactoring and other tweaks.

----- Version 0.9

2010/01/29

- Added the QR Algorithm for eigenvalue decomposition for Symmetric matrices.
- improved eigenvalue unit tests exposing some problems with generic EVD
- various refactoring

----- Version 0.8

2010/01/15

- Usability improvements
- Fixed matrix multiply switching rules for some of the operators
- Added automatic switching to matrix vector multiply to some operators in CommonOps
- More random matrices: orthonormal, orthogonal, normal span, and with specific singular values.
- More matrix norms, including induced norms and all the p vector norms.
- Eigenvalue decomposition for general matrices.
- More functions in EigenOps and preexisting ones improved
- LU now works for rectangular matrices
- LU now generates a pivot matrix (I guess it is really PLU)
- various refactoring

----- Version 0.7

2009/11/10

- Created a linear solver interface.
- QR Update.
  * Two implementations.  One is more straight forward and the other is much faster.
- Created a linear solver interface that allows data points to be added and removed.
- Made decomposition and linear solver algorithms use lazy initialization.

----- Version 0.6

2009/10/26

- Added QR Decomposition
- Added Leibniz formula for determinants.
  * This required a permutation algorithm to be added also.
- Fixed various bugs
- Improved SimpleMatrix to make it more useful


----- Version 0.5

2009/9/19

- Added another type of matrix multiplication that creates a temporary column
  * This significant improved performance in some areas

----- Version 0.4

2009/9/5

- Added two more Cholesky decomposition algorithm; block and LDL.
- Moved significant magic numbers in to EjmlParamters to provide a central place for configuration.
- Various other refactorings and bug fixes.

----- Version 0.3

2009/8/27

- This is the initial public distribution.  Has most of the final functionality.