.. _c-matrices: 

*************************
Dense and Sparse Matrices
*************************

This chapter describes the two CVXOPT matrix types: 
:class:`matrix <cvxopt.matrix>` objects, used for dense matrix 
computations, and :class:`spmatrix <cvxopt.spmatrix>` objects, used for 
sparse matrix computations.


.. _s-dense-matrices:

Dense Matrices 
==============

A dense matrix is created by calling the function :func:`matrix`.  The 
arguments specify the values of the coefficients, the dimensions, and the 
type (integer, double, or complex) of the matrix.

.. function:: cvxopt.matrix(x[, size[, tc]])

   ``size``  is a tuple of length two with the matrix dimensions.
   The number of rows and/or the number of columns can be zero.

   ``tc``  stands for type code. The possible values are :const:`'i'`, 
   :const:`'d'`, and :const:`'z'`, for integer, real (double), and complex 
   matrices, respectively.  

   ``x``  can be a number, a sequence of numbers, a dense or sparse 
   matrix, a one- or two-dimensional NumPy array, or a list of lists of 
   matrices and numbers.  

   * If ``x`` is a number (Python integer, float, or complex number), 
     a matrix is created with the dimensions specified by ``size`` and 
     with all the coefficients equal to ``x``.  
     The default value of ``size``  is ``(1,1)``, and the default value
     of ``tc`` is the type of ``x``.
     If necessary, the type of ``x`` is converted (from integer to double
     when used to create a matrix of type :const:`'d'`, and from integer or
     double to complex when used to create a matrix of type :const:`'z'`).

     >>> from cvxopt import matrix
     >>> A = matrix(1, (1,4))   
     >>> print(A)
     [ 1  1  1  1]
     >>> A = matrix(1.0, (1,4))   
     >>> print(A)
     [ 1.00e+00  1.00e+00  1.00e+00  1.00e+00]
     >>> A = matrix(1+1j)     
     >>> print(A)
     [ 1.00e+00+j1.00e+00]

   * If ``x`` is a sequence of numbers (list, tuple, array, 
     :mod:`array` array, ...), then the numbers are interpreted as the 
     coefficients of a matrix in column-major order.     
     The length of ``x``  must be equal to the product of 
     ``size[0]`` and ``size[1]``.
     If ``size``  is not specified, a matrix with one column is created. 
     If ``tc``  is not specified, it is determined from the elements of 
     ``x``  (and if that is impossible, for example because ``x``  is
     an empty list, a value :const:`'i'` is used).  
     Type conversion takes place as for scalar ``x``.

     The following example shows several ways to define the same integer 
     matrix.

     >>> A = matrix([0, 1, 2, 3], (2,2))  
     >>> A = matrix((0, 1, 2, 3), (2,2))  
     >>> A = matrix(range(4), (2,2))   
     >>> from array import array
     >>> A = matrix(array('i', [0,1,2,3]), (2,2))
     >>> print(A)
     [ 0  2]
     [ 1  3]

     In Python 2.7 the following also works.

     >>> A = matrix(xrange(4), (2,2))   

   * If ``x``  is a dense or sparse matrix, then the coefficients of 
     ``x``  are copied, in column-major order, to a new matrix of the 
     given size.  
     The total number of elements in the new matrix (the product of 
     ``size[0]`` and ``size[1]``) must be the same as the product 
     of  the dimensions of ``x``.  If ``size``  is not specified, the 
     dimensions of ``x``  are used.  
     The default value of ``tc``  is the type of ``x``. 
     Type conversion takes place when the type of ``x`` differs from 
     ``tc``, in a similar way as for scalar ``x``.  
     
     >>> A = matrix([1., 2., 3., 4., 5., 6.], (2,3))  
     >>> print(A)
     [ 1.00e+00  3.00e+00  5.00e+00]
     [ 2.00e+00  4.00e+00  6.00e+00]
     >>> B = matrix(A, (3,2))  
     >>> print(B)
     [ 1.00e+00  4.00e+00]
     [ 2.00e+00  5.00e+00]
     [ 3.00e+00  6.00e+00]
     >>> C = matrix(B, tc='z')      
     >>> print(C)
     [ 1.00e+00-j0.00e+00  4.00e+00-j0.00e+00]
     [ 2.00e+00-j0.00e+00  5.00e+00-j0.00e+00]
     [ 3.00e+00-j0.00e+00  6.00e+00-j0.00e+00]


     In Python 2.7 NumPy arrays can be converted to matrices.

     >>> from numpy import array
     >>> x = array([[1., 2., 3.], [4., 5., 6.]])
     >>> x
     array([[ 1.  2.  3.]
            [ 4.  5.  6.]])
     >>> print(matrix(x))               
     [ 1.00e+00  2.00e+00  3.00e+00]
     [ 4.00e+00  5.00e+00  6.00e+00]
     
   * If ``x`` is a list of lists of dense or sparse matrices 
     and numbers (Python integer, float, or complex), 
     then each element of ``x`` is interpreted as a 
     block-column stored in column-major order. 
     If ``size`` is not specified, the block-columns are juxtaposed
     to obtain a matrix with ``len(x)``  block-columns.
     If ``size`` is specified, then the matrix with ``len(x)``
     block-columns is resized by copying its elements in column-major 
     order into a matrix of the dimensions given by ``size``.  
     If ``tc`` is not specified, it is determined from the elements of 
     ``x`` (and if that is impossible, for example because ``x`` is
     a list of empty lists, a value :const:`'i'` is used).  
     The same rules for type conversion apply as for scalar ``x``.
     
     >>> print(matrix([[1., 2.], [3., 4.], [5., 6.]]))
     [ 1.00e+00  3.00e+00  5.00e+00]
     [ 2.00e+00  4.00e+00  6.00e+00]
     >>> A1 = matrix([1, 2], (2,1))
     >>> B1 = matrix([6, 7, 8, 9, 10, 11], (2,3))
     >>> B2 = matrix([12, 13, 14, 15, 16, 17], (2,3))
     >>> B3 = matrix([18, 19, 20], (1,3))
     >>> C = matrix([[A1, 3.0, 4.0, 5.0], [B1, B2, B3]])
     >>> print(C)
     [ 1.00e+00  6.00e+00  8.00e+00  1.00e+01]
     [ 2.00e+00  7.00e+00  9.00e+00  1.10e+01]
     [ 3.00e+00  1.20e+01  1.40e+01  1.60e+01]
     [ 4.00e+00  1.30e+01  1.50e+01  1.70e+01]
     [ 5.00e+00  1.80e+01  1.90e+01  2.00e+01]

     A matrix with a single block-column can be represented by a single 
     list (i.e., if ``x`` is a list of lists, and has length one, 
     then the argument ``x`` can be replaced by ``x[0]``).

     >>> D = matrix([B1, B2, B3])
     >>> print(D)
     [  6   8  10]
     [  7   9  11]
     [ 12  14  16]
     [ 13  15  17]
     [ 18  19  20]


.. _s-sparse-matrices:

Sparse Matrices
===============

A general :class:`spmatrix <cvxopt.spmatrix>` object can be thought of as 
a *triplet description* of a sparse matrix, i.e., a list of entries of the
matrix, with for each entry the value, row index, and column index.  
Entries that are not included in the list are assumed to be zero.  
For example, the sparse matrix

.. math:: 

    A = \left[ \begin{array}{rrrrr}
        0 & 2 & 0 & 0 & 3 \\
        2 & 0 & 0 & 0 & 0 \\
       -1 & -2 & 0 & 4 & 0 \\
        0 & 0 & 1 & 0 & 0 \end{array} \right]

has the triplet description 

.. math::

    (2,1,0), \qquad (-1,2,0), \qquad (2,0,1), \qquad (-2,2,1), \qquad
    (1,3,2), \qquad (4,2,3), \qquad (3,0,4).

The list may include entries with a zero value, so triplet descriptions 
are not necessarily unique.  The list

.. math::

    (2,1,0), \qquad (-1,2,0), \qquad (0,3,0), \qquad (2,0,1), \qquad 
    (-2,2,1), \qquad (1,3,2), \qquad (4,2,3), \qquad (3,0,4)

is another triplet description of the same matrix.

An :class:`spmatrix` object corresponds to a particular 
triplet description of a sparse matrix.  We will refer to the entries in
the triplet description as the *nonzero entries* of the object, 
even though they may have a numerical value zero.

Three functions are provided to create sparse matrices. 
The first, :func:`spmatrix`, 
constructs a sparse matrix from a triplet description. 

.. function:: cvxopt.spmatrix(x, I, J[, size[, tc]])

    ``I`` and ``J`` are sequences of integers (lists, tuples, 
    :mod:`array` arrays, ...) or integer matrices 
    (:class:`matrix <cvxopt.matrix>` objects with typecode :const:`'i'`),  
    containing the row and column indices of the nonzero entries.  
    The lengths of ``I`` and ``J`` must be  equal.  If they are matrices, 
    they are treated as lists of indices stored in column-major order, 
    i.e., as lists ``list(I)``, respectively, ``list(J)``. 
    
    ``size`` is a tuple of nonnegative integers with the row and column 
    dimensions of the matrix.
    The ``size`` argument is only needed when creating a matrix with 
    a zero last row or last column.  If ``size`` is not specified, it 
    is determined from ``I`` and ``J``:
    the default value for ``size[0]`` is ``max(I)+1`` if ``I`` is 
    nonempty and zero otherwise.  
    The default value for ``size[1]`` is ``max(J)+1`` if ``J`` is 
    nonempty and zero otherwise.
    
    ``tc`` is the typecode, :const:`'d'` or :const:`'z'`, for double and 
    complex matrices, respectively.   Integer sparse matrices are 
    not implemented.
    
    ``x`` can be a number, a sequence of numbers, or a dense matrix.  
    This argument specifies the numerical values of the nonzero entries.
    
    - If ``x`` is a number (Python integer, float, or complex), 
      a matrix is created with the sparsity pattern defined by ``I`` and 
      ``J``, and nonzero entries initialized to the value of ``x``.  
      The default value of ``tc`` is :const:`'d'` if ``x`` is integer or 
      float, and :const:`'z'` if ``x``  is complex.  
      
      The following code creates a 4 by 4 sparse identity matrix.
        
      >>> from cvxopt import spmatrix
      >>> A = spmatrix(1.0, range(4), range(4))
      >>> print(A) 
         [ 1.00e+00     0         0         0    ]
         [    0      1.00e+00     0         0    ]
         [    0         0      1.00e+00     0    ]
         [    0         0         0      1.00e+00]
        
        
    - If ``x`` is a sequence of numbers, a sparse matrix is created 
      with the entries of ``x`` copied to the entries indexed by ``I`` 
      and ``J``.  The list ``x`` must have the same length as ``I`` and 
      ``J``.
      The default value of ``tc`` is determined from the elements of ``x``:
      :const:`'d'` if ``x`` contains integers and floating-point numbers or
      if ``x`` is an empty list, and :const:`'z'` if ``x`` contains at 
      least one complex number.
      
      >>> A = spmatrix([2,-1,2,-2,1,4,3], [1,2,0,2,3,2,0], [0,0,1,1,2,3,4])
      >>> print(A)
        [    0      2.00e+00     0         0      3.00e+00]
        [ 2.00e+00     0         0         0         0    ]
        [-1.00e+00 -2.00e+00     0      4.00e+00     0    ]
        [    0         0      1.00e+00     0         0    ]
        
        
    - If ``x`` is a dense matrix, a sparse matrix is created with 
      all the entries of ``x`` copied, in column-major order, to the 
      entries indexed by ``I`` and ``J``.
      The matrix ``x`` must have the same length as ``I`` and ``J``.
      The default value of ``tc`` is :const:`'d'` if ``x`` is an 
      :const:`'i'` or :const:`'d'` matrix, and :const:`'z'` otherwise.  
      If ``I`` and ``J`` contain repeated entries, the corresponding 
      values of the coefficients are added.


The function :func:`sparse` constructs a sparse matrix 
from a block-matrix description.
   
.. function:: cvxopt.sparse(x[, tc])

    ``tc`` is the typecode, :const:`'d'` or :const:`'z'`, for double and 
    complex matrices, respectively.
  
    ``x`` can be a :class:`matrix`, :class:`spmatrix`, or a list of lists 
    of matrices (:class:`matrix` or :class:`spmatrix` objects) and 
    numbers (Python integer, float, or complex). 
    
    * If ``x`` is a :class:`matrix` or :class:`spmatrix` object, then a 
      sparse matrix of the same size and the same numerical value is 
      created.  Numerical zeros in ``x`` are treated as structural zeros 
      and removed from the triplet description of the new sparse matrix.
    
    * If ``x`` is a list of lists of matrices (:class:`matrix` or 
      :class:`spmatrix` objects) and numbers (Python integer, float, or 
      complex) then each element of ``x`` is interpreted as a 
      (block-)column matrix stored in colum-major order, and a
      block-matrix is constructed by juxtaposing the ``len(x)``
      block-columns (as in :func:`matrix <cvxopt.matrix>`).
      Numerical zeros are removed from the triplet description of the new 
      matrix.  
    
    >>> from cvxopt import matrix, spmatrix, sparse
    >>> A = matrix([[1., 2., 0.], [2., 1., 2.], [0., 2., 1.]])
    >>> print(A)
    [ 1.00e+00  2.00e+00  0.00e+00]
    [ 2.00e+00  1.00e+00  2.00e+00]
    [ 0.00e+00  2.00e+00  1.00e+00]
    >>> B = spmatrix([], [], [], (3,3))
    >>> print(B)
    [0 0 0]
    [0 0 0]
    [0 0 0]
    >>> C = spmatrix([3, 4, 5], [0, 1, 2], [0, 1, 2])
    >>> print(C)
    [ 3.00e+00     0         0    ]
    [    0      4.00e+00     0    ]
    [    0         0      5.00e+00]
    >>> D = sparse([[A, B], [B, C]])
    >>> print(D)
    [ 1.00e+00  2.00e+00     0         0         0         0    ]
    [ 2.00e+00  1.00e+00  2.00e+00     0         0         0    ]
    [    0      2.00e+00  1.00e+00     0         0         0    ]
    [    0         0         0      3.00e+00     0         0    ]
    [    0         0         0         0      4.00e+00     0    ]
    [    0         0         0         0         0      5.00e+00]
    
    A matrix with a single block-column can be represented by a single 
    list.
    
    >>> D = sparse([A, C])
    >>> print(D)
    [ 1.00e+00  2.00e+00     0    ]
    [ 2.00e+00  1.00e+00  2.00e+00]
    [    0      2.00e+00  1.00e+00]
    [ 3.00e+00     0         0    ]
    [    0      4.00e+00     0    ]
    [    0         0      5.00e+00]
    
    
The function :func:`spdiag` constructs a block-diagonal 
sparse matrix from a list of matrices.

.. function:: cvxopt.spdiag(x)

    ``x`` is a dense or sparse matrix with a single row or column, or a 
    list of square dense or sparse matrices or scalars.  
    If ``x`` is a matrix, a sparse diagonal matrix is returned with 
    the entries of ``x`` on its diagonal.
    If ``x`` is list, a sparse block-diagonal matrix is returned with
    the elements in the list as its diagonal blocks.
      
    >>> from cvxopt import matrix, spmatrix, spdiag
    >>> A = 3.0
    >>> B = matrix([[1,-2],[-2,1]])
    >>> C = spmatrix([1,1,1,1,1],[0,1,2,0,0,],[0,0,0,1,2])
    >>> D = spdiag([A, B, C])
    >>> print(D)
    [ 3.00e+00     0         0         0         0         0    ]
    [    0      1.00e+00 -2.00e+00     0         0         0    ]
    [    0     -2.00e+00  1.00e+00     0         0         0    ]
    [    0         0         0      1.00e+00  1.00e+00  1.00e+00]
    [    0         0         0      1.00e+00     0         0    ]
    [    0         0         0      1.00e+00     0         0    ]
    
    

.. _s-arithmetic:

Arithmetic Operations 
=====================

The following table lists the arithmetic operations defined for dense and 
sparse matrices.  In the table ``A`` and ``B`` are dense or sparse 
matrices of compatible dimensions, ``c``  is a scalar (a Python number or 
a dense 1 by 1 matrix), ``D`` is a dense matrix, and ``e`` is a Python 
number.

+------------------------------------+-------------------------+
| Unary plus/minus                   | ``+A, -A``              |
+------------------------------------+-------------------------+
| Addition                           | ``A + B, A + c, c + A`` |
+------------------------------------+-------------------------+
| Subtraction                        | ``A - B, A - c, c - A`` |
+------------------------------------+-------------------------+ 
| Matrix multiplication              | ``A * B``               |
+------------------------------------+-------------------------+
| Scalar multiplication and division | ``c * A, A * c, A / c`` |
+------------------------------------+-------------------------+    
| Remainder after division           | ``D % c``               |
+------------------------------------+-------------------------+
| Elementwise exponentiation         | ``D**e``                |
+------------------------------------+-------------------------+

The type of the result of these operations generally follows the Python
conventions.   
For example, if ``A`` and ``c`` are integer, then in Python 2 the division
``A/c`` is interpreted as integer division and results in a 
type :const:`'i'` matrix, while in Python 3 it is interpreted as standard 
divison and results in a type :const:`'d'` matrix.
An exception to the Python conventions is elementwise exponentiation: 
if ``D`` is an integer matrix and ``e`` is an integer number 
than ``D**e`` is a matrix of type :const:`'d'`.

Addition, subtraction, and matrix multiplication with two matrix operands 
result in a sparse matrix if both matrices are sparse, and in a dense 
matrix otherwise.  The result of a scalar multiplication or division is 
dense if ``A`` is dense, and sparse if ``A`` is sparse.  Postmultiplying 
a matrix with a number ``c`` means the same as premultiplying, i.e., 
scalar multiplication.  Dividing a matrix by ``c`` means dividing all its 
entries by ``c``.  

If ``c`` in the expressions ``A+c``, ``c+A``, ``A-c``, ``c-A`` is a number,
then it is interpreted as a dense matrix with 
the same dimensions as ``A``, type given by the type of ``c``, and all 
its entries equal to ``c``.  If ``c``  is a 1 by 1 dense matrix and ``A`` 
is not 1 by 1, then ``c`` is interpreted as a dense matrix with the same 
size of ``A`` and all entries equal to ``c[0]``.

If ``c`` is a 1 by 1 dense matrix, then, if possible, the products 
``c*A`` and ``A*c`` are interpreted as matrix-matrix products.
If the product cannot be interpreted as a matrix-matrix product
because the dimensions of ``A`` are incompatible, then the product is 
interpreted as the scalar multiplication with ``c[0]``.
The division ``A/c`` and remainder ``A%c`` with ``c`` a 
1 by 1 matrix are always interpreted as ``A/c[0]``, resp., ``A%c[0]``.

The following in-place operations are also defined, but only if they do 
not change the type (sparse or dense, integer, real, or complex) of the 
matrix ``A``.  These in-place operations do not return a new matrix but 
modify the existing object ``A``.

+---------------------------------------------+--------------------+
| In-place addition                           | ``A += B, A += c`` |
+---------------------------------------------+--------------------+
| In-place subtraction                        | ``A -= B, A -= c`` |
+---------------------------------------------+--------------------+
| In-place scalar multiplication and division | ``A *= c, A /= c`` |
+---------------------------------------------+--------------------+
| In-place remainder                          | ``A %= c``         |
+---------------------------------------------+--------------------+

For example, if ``A`` has typecode :const:`'i'`, then ``A += B`` is 
allowed if ``B`` has typecode :const:`'i'`.  It is not allowed if ``B`` 
has typecode :const:`'d'` or :const:`'z'` because the addition 
``A+B`` results in a :const:`'d'` or :const:`'z'` matrix and 
therefore cannot be assigned to ``A`` without changing its type.  
As another example, if ``A`` is a sparse matrix, then ``A += 1.0`` is 
not allowed because the operation ``A = A + 1.0`` results in a dense 
matrix, so it cannot be assigned to ``A`` without changing its type.

In-place matrix-matrix products are not allowed.  (Except when ``c`` is 
a 1 by 1 dense matrix, in which case ``A *= c`` is interpreted as the 
scalar product ``A *= c[0]``.)

In-place remainder is only defined for dense ``A``.

It is important to know when a matrix operation creates a new object.  
The following rules apply.

* A simple assignment (``A = B``) is given the standard Python 
  interpretation, i.e., it assigns to the variable ``A`` a reference (or 
  pointer) to the object referenced by ``B``.

  >>> B = matrix([[1.,2.], [3.,4.]])  
  >>> print(B)
  [ 1.00e+00  3.00e+00]
  [ 2.00e+00  4.00e+00]
  >>> A = B
  >>> A[0,0] = -1 
  >>> print(B)   # modifying A[0,0] also modified B[0,0]
  [-1.00e+00  3.00e+00]
  [ 2.00e+00  4.00e+00]
  
* The regular (i.e., not in-place) arithmetic operations always return 
  new objects.   
  
  >>> B = matrix([[1.,2.], [3.,4.]])  
  >>> A = +B
  >>> A[0,0] = -1 
  >>> print(B)   # modifying A[0,0] does not modify B[0,0]
  [ 1.00e+00  3.00e+00]
  [ 2.00e+00  4.00e+00]
  
* The in-place operations directly modify the coefficients of the 
  existing matrix object and do not create a new object. 
  
  >>> B = matrix([[1.,2.], [3.,4.]])  
  >>> A = B
  >>> A *= 2
  >>> print(B)   # in-place operation also changed B
  [ 2.00e+00  6.00e+00]
  [ 4.00e+00  8.00e+00]
  >>> A = 2*A
  >>> print(B)   # regular operation creates a new A, so does not change B
  [ 2.00e+00  6.00e+00]
  [ 4.00e+00  8.00e+00]
  
  
.. _s-indexing:

Indexing and Slicing
====================

Matrices can be indexed using one or two arguments.  In single-argument
indexing of a matrix ``A``, the index runs from ``-len(A)`` to 
``len(A)-1``, and is interpreted as an index in the one-dimensional 
array of coefficients of ``A`` in column-major order.   Negative indices 
have the standard Python interpretation: for negative ``k``, 
``A[k]`` is the same element as ``A[len(A) + k]``.

Four different types of one-argument indexing are implemented.

1. The index can be a single integer.  
   This returns a number, e.g., ``A[0]`` is the first element of ``A``.

2. The index can be an integer matrix.  This returns a column matrix: the 
   command ``A[matrix([0,1,2,3])]`` returns the 4 by 1 matrix 
   consisting of the first four elements of ``A``.  The size of the index 
   matrix is ignored: ``A[matrix([0,1,2,3], (2,2))]`` returns the same 
   4 by 1 matrix.

3. The index can be a list of integers.  This returns a column matrix, 
   e.g., ``A[[0,1,2,3]]`` is the 4 by 1 matrix consisting of elements 
   0, 1, 2, 3 of ``A``.   

4. The index can be a Python slice.  This returns a matrix with one column
   (possibly 0 by 1, or 1 by 1).  For example, ``A[::2]`` is the column
   matrix defined by taking every other element of ``A``, stored in 
   column-major order.  ``A[0:0]`` is a matrix with size (0,1).

Thus, single-argument indexing returns a scalar (if the index is an 
integer), or a matrix with one column.  This is consistent with the 
interpretation that single-argument indexing accesses the matrix in 
column-major order.

Note that an index list or an index matrix are equivalent, but they are 
both useful, especially when we perform operations on index sets.  For 
example, if ``I`` and ``J`` are lists then ``I+J`` is the 
concatenated list, and ``2*I`` is ``I`` repeated twice.  If they 
are matrices, these operations are interpreted as arithmetic operations.
For large index sets, indexing with integer matrices is also faster 
than indexing with lists. 

The following example illustrates one-argument indexing.

>>> from cvxopt import matrix, spmatrix
>>> A = matrix(range(16), (4,4), 'd')
>>> print(A)
[ 0.00e+00  4.00e+00  8.00e+00  1.20e+01]
[ 1.00e+00  5.00e+00  9.00e+00  1.30e+01]
[ 2.00e+00  6.00e+00  1.00e+01  1.40e+01]
[ 3.00e+00  7.00e+00  1.10e+01  1.50e+01]
>>> A[4]
4.0
>>> I = matrix([0, 5, 10, 15])
>>> print(A[I])      # the diagonal
[ 0.00e+00]
[ 5.00e+00]
[ 1.00e+01]
[ 1.50e+01]
>>> I = [0,2];  J = [1,3]
>>> print(A[2*I+J])  # duplicate I and append J
[ 0.00e+00]
[ 2.00e+00]
[ 0.00e+00]
[ 2.00e+00]
[ 1.00e+00]
[ 3.00e+00]
>>> I = matrix([0, 2]);  J =  matrix([1, 3])
>>> print(A[2*I+J])  # multiply I by 2 and add J
[ 1.00e+00]
[ 7.00e+00]
>>> print(A[4::4])   # get every fourth element skipping the first four  
[ 4.00e+00]
[ 8.00e+00]
[ 1.20e+01]

In two-argument indexing the arguments can be any combinations of the
four types listed above.  The first argument indexes the rows of 
the matrix and the second argument indexes the columns.  If both 
indices are scalars, then a scalar is returned.  In all other cases, 
a matrix is returned.  We continue the example.

>>> print(A[:,1])
[ 4.00e+00]
[ 5.00e+00]
[ 6.00e+00]
[ 7.00e+00]
>>> J = matrix([0, 2])
>>> print(A[J,J])
[ 0.00e+00  8.00e+00]
[ 2.00e+00  1.00e+01]
>>> print(A[:2, -2:])
[ 8.00e+00  1.20e+01]
[ 9.00e+00  1.30e+01]
>>> A = spmatrix([0,2,-1,2,-2,1], [0,1,2,0,2,1], [0,0,0,1,1,2]) 
>>> print(A[:, [0,1]])
[ 0.00e+00  2.00e+00]
[ 2.00e+00     0    ]
[-1.00e+00 -2.00e+00]
>>> B = spmatrix([0,2*1j,0,-2], [1,2,1,2], [0,0,1,1,])
>>> print(B[-2:,-2:])
[ 0.00e+00-j0.00e+00  0.00e+00-j0.00e+00]
[ 0.00e+00+j2.00e+00 -2.00e+00-j0.00e+00]

Expressions of the form ``A[I]`` or ``A[I,J]`` can also appear on 
the left-hand side of an assignment.   The right-hand side must be a 
scalar 
(i.e., a number or a 1 by 1 dense matrix), a sequence of numbers, or a 
dense or sparse matrix.  If the right-hand side is a scalar, it is 
interpreted as a dense matrix with identical entries and the dimensions of
the left-hand side.  If the right-hand side is a sequence of numbers 
(list, tuple, :mod:`array` array, range object, ...) its values are 
interpreted 
as the coefficients of a dense matrix in column-major order.  If the 
right-hand side is a matrix (:class:`matrix` or 
:class:`spmatrix`), it must 
have the same size as the left-hand side.  Sparse matrices are 
converted to dense in the assignment to a dense matrix.

Indexed assignments are only allowed if they do not change the type of 
the matrix.  For example, if ``A`` is a matrix with type :const:`'d'`, 
then ``A[I] = B`` is only permitted if ``B`` is an integer, a float, 
or a matrix of type :const:`'i'` or :const:`'d'`.  If ``A`` is an integer 
matrix, then ``A[I] = B`` is only permitted if ``B`` is an integer or 
an integer matrix.

The following examples illustrate indexed assignment.

>>> A = matrix(range(16), (4,4))
>>> A[::2,::2] = matrix([[-1, -2], [-3, -4]])
>>> print(A)
[ -1   4  -3  12]
[  1   5   9  13]
[ -2   6  -4  14]
[  3   7  11  15]
>>> A[::5] += 1
>>> print(A)
[  0   4  -3  12]
[  1   6   9  13]
[ -2   6  -3  14]
[  3   7  11  16]
>>> A[0,:] = -1, 1, -1, 1
>>> print(A)
[ -1   1  -1   1]
[  1   6   9  13]
[ -2   6  -3  14]
[  3   7  11  16]
>>> A[2:,2:] = range(4)
>>> print(A)
[ -1   1  -1   1]
[  1   6   9  13]
[ -2   6   0   2]
[  3   7   1   3]
>>> A = spmatrix([0,2,-1,2,-2,1], [0,1,2,0,2,1], [0,0,0,1,1,2]) 
>>> print(A)
[ 0.00e+00  2.00e+00     0    ]
[ 2.00e+00     0      1.00e+00]
[-1.00e+00 -2.00e+00     0    ]
>>> C = spmatrix([10,-20,30], [0,2,1], [0,0,1])
>>> print(C)
[ 1.00e+01     0    ]
[    0      3.00e+01]
[-2.00e+01     0    ]
>>> A[:,0] = C[:,0]
>>> print(A)
[ 1.00e+01  2.00e+00     0    ]
[    0         0      1.00e+00]
[-2.00e+01 -2.00e+00     0    ]
>>> D = matrix(range(6), (3,2))
>>> A[:,0] = D[:,0]
>>> print(A)
[ 0.00e+00  2.00e+00     0    ]
[ 1.00e+00     0      1.00e+00]
[ 2.00e+00 -2.00e+00     0    ]
>>> A[:,0] = 1
>>> print(A)
[ 1.00e+00  2.00e+00     0    ]
[ 1.00e+00     0      1.00e+00]
[ 1.00e+00 -2.00e+00     0    ]
>>> A[:,0] = 0
>>> print(A)
[ 0.00e+00  2.00e+00     0    ]
[ 0.00e+00     0      1.00e+00]
[ 0.00e+00 -2.00e+00     0    ]



Attributes and Methods 
======================

Dense and sparse matrices have the following attributes.

.. attribute:: size

    A tuple with the dimensions of the matrix. The size of the matrix can 
    be changed by altering this attribute, as long as the number of 
    elements in the matrix remains unchanged.  

.. attribute:: typecode

    A character, either :const:`'i'`, :const:`'d'`, or :const:`'z'`, for 
    integer, real, and complex matrices, respectively.  A read-only 
    attribute.

.. method:: trans()

    Returns the transpose of the matrix as a new matrix.  One can also use
    ``A.T`` instead of ``A.trans()``.

.. method:: ctrans()

    Returns the conjugate transpose of the matrix as a new matrix.  One 
    can also use ``A.H`` instead of ``A.ctrans()``.

.. method:: real()

    For complex matrices, returns the real part as a real matrix.  For 
    integer and real matrices, returns a copy of the matrix.

.. method:: imag

    For complex matrices, returns the imaginary part as a real matrix.
    For integer and real matrices, returns an integer or real zero matrix.

In addition, sparse matrices have the following attributes.

.. attribute:: V

    A single-column dense matrix containing the numerical values of the 
    nonzero entries in column-major order.  Making an assignment to the 
    attribute is an efficient way of changing the values of the sparse 
    matrix, without changing the sparsity pattern.
    
    When the attribute :attr:`V` is read, a *copy* of :attr:`V` is 
    returned, as a new dense matrix.  This implies, for example, that an 
    indexed assignment ``A.V[I] = B`` does not work, or at least 
    cannot be used to modify ``A``.  Instead the attribute :attr:`V` 
    will be read and returned as a new matrix; then the elements of this 
    new matrix are modified.

.. attribute:: I

    A single-column integer dense matrix with the row indices of the 
    entries in :attr:`V`.  A read-only attribute.

.. attribute:: J

    A single-column integer dense matrix with the column indices of the 
    entries in :attr:`V`.  A read-only attribute.

.. attribute:: CCS

    A triplet (column pointers, row indices, values) with the 
    compressed-column-storage representation of the matrix.  A read-only 
    attribute.  This attribute can be used to export sparse matrices to 
    other packages such as MOSEK.

The next example below illustrates assignments to :attr:`V`.

>>> from cvxopt import spmatrix, matrix
>>> A = spmatrix(range(5), [0,1,1,2,2], [0,0,1,1,2])
>>> print(A)
[ 0.00e+00     0         0    ]
[ 1.00e+00  2.00e+00     0    ]
[    0      3.00e+00  4.00e+00]
>>> B = spmatrix(A.V, A.J, A.I, (4,4))  # transpose and add a zero row and column
>>> print(B)
[ 0.00e+00  1.00e+00     0         0    ]
[    0      2.00e+00  3.00e+00     0    ]
[    0         0      4.00e+00     0    ]
[    0         0         0         0    ]
>>> B.V = matrix([1., 7., 8., 6., 4.])   # assign new values to nonzero entries
>>> print(B)
[ 1.00e+00  7.00e+00     0         0    ]
[    0      8.00e+00  6.00e+00     0    ]
[    0         0      4.00e+00     0    ]
[    0         0         0         0    ]


The following attributes and methods are defined for dense matrices.

.. method:: tofile(f)

    Writes the elements of the matrix in column-major order to a binary 
    file ``f``. 

.. method:: fromfile(f)

    Reads the contents of a binary file ``f`` into the matrix object.

The last two methods are illustrated in the following examples.

>>> from cvxopt import matrix, spmatrix
>>> A = matrix([[1.,2.,3.], [4.,5.,6.]])  
>>> print(A)
[ 1.00e+00  4.00e+00]
[ 2.00e+00  5.00e+00]
[ 3.00e+00  6.00e+00]
>>> f = open('mat.bin','wb')
>>> A.tofile(f)
>>> f.close()
>>> B = matrix(0.0, (2,3))
>>> f = open('mat.bin','rb')
>>> B.fromfile(f)
>>> f.close()
>>> print(B)
[ 1.00e+00  3.00e+00  5.00e+00]
[ 2.00e+00  4.00e+00  6.00e+00]
>>> A = spmatrix(range(5), [0,1,1,2,2], [0,0,1,1,2])
>>> f = open('test.bin','wb')
>>> A.V.tofile(f)  
>>> A.I.tofile(f) 
>>> A.J.tofile(f)
>>> f.close()
>>> f = open('test.bin','rb')
>>> V = matrix(0.0, (5,1));  V.fromfile(f)  
>>> I = matrix(0, (5,1));  I.fromfile(f)  
>>> J = matrix(0, (5,1));  J.fromfile(f)  
>>> B = spmatrix(V, I, J)
>>> print(B)
[ 0.00e+00     0         0    ]
[ 1.00e+00  2.00e+00     0    ]
[    0      3.00e+00  4.00e+00]

Note that the :func:`dump` and :func:`load` functions in the :mod:`pickle` 
module offer a convenient alternative for writing matrices to files and 
reading matrices from files.


Built-In Functions
==================

Many Python built-in functions and operations can be used with matrix 
arguments.  We list some useful examples.

.. function:: len(x)

    If ``x`` is a dense matrix, returns the product of the number of rows 
    and the number of columns.  If ``x`` is a sparse matrix, returns the 
    number of nonzero entries.

.. function:: bool([x])

    Returns :const:`False` if ``x`` is a zero matrix and :const:`True` 
    otherwise.

.. function:: max(x)

    If ``x`` is a dense matrix, returns the maximum element of ``x``.
    If ``x`` is a sparse, returns the maximum nonzero element of ``x``.

.. function:: min(x)

    If ``x`` is a dense matrix, returns the minimum element of ``x``.
    If ``x`` is a sparse matrix, returns the minimum nonzero element of 
    ``x``.

.. function:: abs(x)

    Returns a matrix with the absolute values of the elements of ``x``.

.. function:: sum(x[, start = 0.0])

    Returns the sum of ``start`` and the elements of ``x``.


Dense and sparse matrices can be used as  arguments to the :func:`list`, 
:func:`tuple`, :func:`zip`, :func:`map`, and :func:`filter` functions 
described in the Python Library Reference.  
However, one should note that when used with sparse matrix arguments, 
these functions only consider the nonzero entries.
For example, ``list(A)`` and ``tuple(A)`` construct a list, 
respectively a tuple, from the elements of ``A`` if ``A`` is dense, and 
of the nonzero elements of ``A`` if ``A`` is sparse.

``list(zip(A, B, ...))`` returns a list of tuples, with the i-th tuple 
containing the i-th elements (or nonzero elements) of ``A``, ``B``, ....   

>>> from cvxopt import matrix
>>> A = matrix([[-11., -5., -20.], [-6., 0., 7.]])
>>> B = matrix(range(6), (3,2))
>>> list(A)
[-11.0, -5.0, -20.0, -6.0, 0.0, 7.0]
>>> tuple(B)
(0, 1, 2, 3, 4, 5)
>>> list(zip(A, B))
[(-11.0, 0), (-5.0, 1), (-20.0, 2), (-6.0, 3), (0.0, 4), (7.0, 5)]

``list(map(f, A))``, where ``f`` is a function and ``A`` is a 
dense matrix, returns a list constructed by applying ``f`` to each 
element of ``A``.  If
``A`` is sparse, the function ``f`` is applied to each nonzero element of 
``A``.  Multiple arguments can be provided, for example, as in 
``map(f, A, B)``, if ``f`` is a function with two arguments.
In the following example, we return an integer 0-1 matrix with the
result of an elementwise comparison.

>>> A = matrix([ [0.5, -0.1, 2.0], [1.5, 0.2, -0.1], [0.3, 1.0, 0.0]]) 
>>> print(A)
[ 5.00e-01  1.50e+00  3.00e-01]
[-1.00e-01  2.00e-01  1.00e+00]
[ 2.00e+00 -1.00e-01  0.00e+00]
>>> print(matrix(list(map(lambda x: 0 <= x <= 1, A), A.size)))
[ 1  0  1]
[ 0  1  1]
[ 0  0  1]

``list(filter(f, A))``, where ``f`` is a function and ``A`` is a matrix, 
returns a list containing the elements of ``A`` (or nonzero elements of 
``A`` is ``A`` is sparse) for which ``f`` is true.

>>> A = matrix([[5, -4, 10, -7], [-1, -5, -6, 2], [6, 1, 5, 2],  [-1, 2, -3, -7]])
>>> print(A)
[  5  -1   6  -1]
[ -4  -5   1   2]
[ 10  -6   5  -3]
[ -7   2   2  -7]
>>> list(filter(lambda x: x%2, A))         # list of odd elements in A
[5, -7, -1, -5, 1, 5, -1, -3, -7]
>>> list(filter(lambda x: -2 < x < 3, A))  # list of elements between -2 and 3
[-1, 2, 1, 2, -1, 2]

It is also possible to iterate over matrix elements, as illustrated in
the following example.

>>> A = matrix([[5, -3], [9, 11]])
>>> for x in A: print(max(x,0))
...
5
0
9
11
>>> [max(x,0) for x in A]
[5, 0, 9, 11]

The expression ``x in A`` returns :const:`True` if an element 
of ``A`` (or a nonzero element of ``A`` if ``A`` is sparse)
is equal to ``x`` and :const:`False` otherwise.


Other Matrix Functions
======================

The following functions can be imported from CVXOPT.

.. function:: cvxopt.sqrt(x)

    The elementwise square root of a dense matrix ``x``.  The result is 
    returned as a real matrix if ``x`` is an integer or real matrix and 
    as a complex matrix if ``x`` is a complex matrix.  Raises an exception
    when ``x`` is an integer or real matrix with negative elements.

    As an example we take the elementwise square root of the sparse matrix

    .. math:: 

        A = \left[ \begin{array}{rrrrr}
            0 & 2 & 0 & 0 & 3 \\
            2 & 0 & 0 & 0 & 0 \\
            1 & 2 & 0 & 4 & 0 \\
            0 & 0 & 1 & 0 & 0 \end{array} \right]

    >>> from cvxopt import spmatrix, sqrt
    >>> A = spmatrix([2,1,2,2,1,3,4], [1,2,0,2,3,0,2], [0,0,1,1,2,3,3]) 
    >>> B = spmatrix(sqrt(A.V), A.I, A.J)
    >>> print(B)
    [    0      1.41e+00     0      1.73e+00]
    [ 1.41e+00     0         0         0    ]
    [ 1.00e+00  1.41e+00     0      2.00e+00]
    [    0         0      1.00e+00     0    ]


.. function:: cvxopt.sin(x)

    The sine function applied elementwise to a dense matrix ``x``.  
    The result is returned as a real matrix if ``x`` is an integer
    or real matrix and as a complex matrix otherwise.  

.. function:: cvxopt.cos(x)

    The cosine function applied elementwise to a dense matrix ``x``.  
    The result is returned as a real matrix if ``x`` is an integer
    or real matrix and as a complex matrix otherwise.  

.. function:: cvxopt.exp(x)

    The exponential function applied elementwise to a dense matrix ``x``.  
    The result is returned as a real matrix if ``x`` is an integer 
    or real matrix and as a complex matrix otherwise.  

.. function:: cvxopt.log(x)

    The natural logarithm applied elementwise to a dense matrix ``x``.  
    The result is returned as a real matrix if ``x`` is an integer
    or real matrix and as a complex matrix otherwise.  
    Raises an exception when ``x`` is an integer or real matrix with 
    nonpositive elements, or a complex matrix with zero elements.

.. function:: cvxopt.mul(x0, [, x1 [, x2 ...]])

    If the arguments are dense or sparse matrices of the same size, returns
    the elementwise product of its arguments.  The result is a sparse
    matrix if one or more of its arguments is sparse, and a dense matrix 
    otherwise.

    If the arguments include scalars, a scalar product with the scalar is 
    made.  (A 1 by 1 dense matrix is treated as a scalar if the dimensions
    of the other arguments are not all 1 by 1.)

    :func:`mul` can also be called with an iterable 
    (list, tuple, range object, or generator) as its single argument, 
    if the iterable generates a list of dense or sparse matrices or 
    scalars.

    >>> from cvxopt import matrix, spmatrix, mul
    >>> A = matrix([[1.0, 2.0], [3.0, 4.0]])
    >>> B = spmatrix([2.0, 3.0], [0, 1], [0, 1])
    >>> print(mul(A, B, -1.0))
    [-2.00e+00     0    ]
    [    0     -1.20e+01]
    >>> print(mul( matrix([k, k+1]) for k in [1,2,3] ))
    [  6]
    [ 24]

.. function:: cvxopt.div(x, y)

    Returns the elementwise division of ``x`` by ``y``.  ``x`` is a dense 
    or sparse matrix, or a scalar (Python number of 1 by 1 dense matrix).
    ``y`` is a dense matrix or a scalar.

.. function:: cvxopt.max(x0[, x1[, x2 ...]])

    When called with a single matrix argument, returns the maximum of the 
    elements of the matrix (including the zero entries, if the matrix is 
    sparse).  

    When called with multiple arguments, the arguments must be matrices of 
    the same size, or scalars, and the elementwise maximum is returned.   
    A 1 by 1 dense matrix is treated as a scalar if the other arguments 
    are not all 1 by 1.  If one of the arguments is scalar, and the other 
    arguments are not all 1 by 1, then the scalar argument is interpreted 
    as a dense matrix with all its entries equal to the scalar.
    
    The result is a sparse matrix if all its arguments are sparse matrices.
    The result is a number if all its arguments are numbers.  The result 
    is a dense matrix if at least one of the arguments is a dense matrix.
    
    :func:`max <cvxopt.max>` can also be called with an iterable 
    (list, tuple, range object, or generator) as its single argument, 
    if the iterable generates a list of dense or sparse matrices or 
    scalars.  
    >>> from cvxopt import matrix, spmatrix, max
    >>> A = spmatrix([2, -3], [0, 1], [0, 1])
    >>> print(max(A, -A, 1))
    [ 2.00e+00  1.00e+00]
    [ 1.00e+00  3.00e+00]
    
    It is important to note the difference between this 
    :func:`max <cvxopt.max>`
    and the built-in :func:`max`, explained in the previous section.
    
    >>> from cvxopt import spmatrix
    >>> A = spmatrix([-1.0, -2.0], [0,1], [0,1])
    >>> max(A)          # built-in max of a sparse matrix takes maximum over nonzero elements
    -1.0
    >>> max(A, -1.5)
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    NotImplementedError: matrix comparison not implemented
    >>> from cvxopt import max
    >>> max(A)          # cvxopt.max takes maximum over all the  elements
    0.0
    >>> print(max(A, -1.5))
    [-1.00e+00  0.00e+00]
    [ 0.00e+00 -1.50e+00]
    

.. function:: cvxopt.min(x0[, x1[, x2 ...]])

    When called with a single matrix argument, returns the minimum of the 
    elements of the matrix (including the zero entries, if the matrix is 
    sparse).  

    When called with multiple arguments, the arguments must be matrices of 
    the same size, or scalars, and the elementwise maximum is returned.   
    A 1 by 1 dense matrix is treated as a scalar if the other arguments 
    are not all 1 by 1.  If one of the arguments is scalar, and the other 
    arguments are not all 1 by 1, then the scalar argument is interpreted 
    as a dense matrix with all its entries equal to the scalar.
    
    :func:`min <cvxopt.min>` can also be called with an iterable 
    (list, tuple, range object, or generator) as its single argument, 
    if the iterable generates a list of dense or sparse matrices or 
    scalars.
    
    
.. _s-random:

Randomly Generated Matrices 
===========================

The CVXOPT package provides two functions 
:func:`normal <cvxopt.normal>` and 
:func:`uniform <cvxopt.uniform>` for generating randomly distributed 
matrices.  
The default installation relies on the pseudo-random number generators in 
the Python standard library :mod:`random`.  Alternatively, the random 
number generators in the 
`GNU Scientific Library (GSL) <http://www.gnu.org/software/gsl>`_ 
can be used, if this option is selected during the installation of CVXOPT.
The random matrix functions based on GSL are faster than the default 
functions based on the :mod:`random` module.

.. function:: cvxopt.normal(nrows[, ncols = 1[, mean = 0.0[, std = 1.0]]])

    Returns a type :const:`'d'` dense matrix of size ``nrows``  by 
    ``ncols`` with elements chosen from a normal distribution 
    with mean ``mean`` and standard deviation ``std``.

.. function:: cvxopt.uniform(nrows[, ncols = 1[, a = 0.0[, b = 1.0]]])

    Returns a type :const:`'d'` dense matrix of size ``nrows`` by 
    ``ncols`` matrix with elements uniformly distributed between ``a`` and 
    ``b``.

.. function:: cvxopt.setseed([value])

    Sets the state of the random number generator.  ``value`` must be an 
    integer.  If ``value`` is absent or equal to zero, the value is taken 
    from the system clock.  If the Python random number generators are 
    used, this is equivalent to :samp:`random.seed(value)`.

.. function:: cvxopt.getseed()

    Returns the current state of the random number generator.  This 
    function is only available if the GSL random number generators are 
    installed.   (The state of the random number generators in the Python 
    :mod:`random` module can be managed via the functions 
    :func:`random.getstate` and :func:`random.setstate`.)


.. _s-array-interface:

The NumPy Array Interface
=========================

This section only applies to the Python 2 version of CVXOPT.

The CVXOPT :class:`matrix` object is compatible with the NumPy Array 
Interface, which allows Python objects that represent multidimensional 
arrays to exchange data using information stored in the attribute 
:attr:`__array_struct__`.  

As already mentioned in the section :ref:`s-dense-matrices`, a 
two-dimensional array 
object (for example, a NumPy matrix or two-dimensional array) can be 
converted to a CVXOPT :class:`matrix` object by using 
the :func:`matrix <cvxopt.matrix>` constructor.  
Conversely, CVXOPT matrices can be used as array-like 
objects in NumPy.  The following example illustrates the compatibility of 
CVXOPT matrices and NumPy arrays. 

>>> from cvxopt import matrix
>>> a = matrix(range(6), (2,3), 'd')
>>> print(a)
[ 0.00e+00  2.00e+00  4.00e+00]
[ 1.00e+00  3.00e+00  5.00e+00]
>>> from numpy import array
>>> b = array(a)
>>> b
array([[ 0.  2.  4.]
       [ 1.  3.  5.]])
>>> a*b
array([[  0.   4.  16.]
       [  1.   9.  25.]])
>>> from numpy import mat
>>> c = mat(a)
>>> c
matrix([[ 0.  2.  4.]
        [ 1.  3.  5.]])
>>> a.T * c 
matrix([[  1.,   3.,   5.],
        [  3.,  13.,  23.],
        [  5.,  23.,  41.]])

In the first product, ``a * b`` is interpreted as NumPy array 
multiplication, i.e., componentwise multiplication.
The second product ``a.T * c`` is interpreted as NumPy matrix 
multiplication, i.e., standard matrix multiplication.
