jblas – Linear Algebra for Java
If you are really impatient, I’d suggest you read the Classes
Overview below and otherwise stick to the API Documentation for the
classes like DoubleMatrix.
The main goals of jblas were to provide very high performance, close
to what you get from state-of-the-art BLAS and LAPACK
easy of use, which means that in the ideal case, you can just
mechanically translate a matrix expression from formulas to Java
In all brevity, here is what you need to know to get started:
- There exist four classes: FloatMatrix,
ComplexFloatMatrix and ComplexDoubleMatrix in the package
org.jblas which represent real a
nd complex matrices in single and
- Higher-level routines for solving equations, or computing
eigenvalues are grouped in classes like Eigen, Solve, or Geometry.
- To construct a new matrix, you can either use the constructor, or
one of the factory methods
ones (constructs a matrix of all ones),
rand (entries uniformly distributed between 0 and 1),
randn (entries normally distributed),
eye (unit matrix),
(matrix with given diagonal). Dimensions are specified in the order
“row”, “column”. The number of columns defaults to 1 if omitted
(meaning that you construct a row vector, if you supply just one
- To access elements, you use
get. Methods also exist for
reading or writing a whole column, row, or submatrix.
- There exist only two-dimensional matrices. Vectors are matrices
whose number of columns or rows are 1. This has turned out to be
much more convenient thatn having separated classes.
- Every math operator maps to a short mnemonic name. For example, +
add, – becomes
sub, * becomes
mul, / becomes
and so on.
- Often, you can pass a double or float value, or a matric with only
one element as the argument to a method, for example, to add the
same value to all elements of the matrix.
mul is element-wise multiplication. Matrix-matrix multiplication
- Often, you can add an “i” to a method to have it work “in-place”. For
addi is like
What is missing right now:
- Right now, the four classes more or less exist next to each other,
with no abstract superclass. This makes the classes pretty
straightforward, but the downside is that you cannot have a function
which works with any kind of matrices.
- No support for sparse matrices.
- Not all of LAPACK is covered, only things I’m using myself. In
principle, there is little overhead in adding further functions as
the generation of wrappers is automatic, but I’d rather include a
function from LAPACK only after I’m sure it does what it’s supposed
to do. In other words, I’ll happily add anything somebody needs as
long as he can check whether the method works as it should.
- jblas uses double and float arrays to store the matrix. Whenever you
call a native function, the array is first copied. This means that
it doesn’t make much sense to call a native routine if its
computation is linear in the size of the data, but this includes
most of BLAS Level 1 and Level 2. jblas therefore uses Java
implementation for things like vector addition, or even
matrix-vector multiplication and is therefore not as fast as native
BLAS. Currently, I’m contemplating some caching schemes to improve