distributed with the package is also available on-line.
- Version 1.1.6 (April 22, 2013).
- Several bug fixes (int/int_t issues). Performance improvements
for certain spmatrix slicing operations. Upgrade to SuiteSparse
version 4.1.0. Improved Numpy compatibility via buffer protocol
(works in both Python 2.x and 3.x). Improved SunOS/Solaris
compatibility (“complex double” instead of “complex”).
- Version 1.1.5 (March 28, 2012).
- Fixed a Mac OS X BLAS compatibility issue.
- Version 1.1.4 (December 21, 2011).
- Merged the source for the Python 2.7 and Python 3 versions.
- Version 1.1.3 (September 15, 2010).
- Upgrade of the MOSEK interface to MOSEK version 6. A few bug fixes in
the matrix class.
- Version 1.1.2 (December 15, 2009).
- Several bug fixes. Improved initialization in the coneqp()
- Version 1.1.1 (March 1, 2009).
- Translated the user guide to Sphinx. Additional LAPACK routines for
LQ factorization and QR factorization with column pivoting.
- Version 1.1 (October 15, 2008).
- matrix(), spmatrix(), and the other functions in
cvxopt.base can now be directly imported from cvxopt
(“from cvxopt import matrix” replaces
“from cvxopt.base import matrix”, although the older code still
works). bool(A) of a dense or sparse matrix A is now defined to be
True if A is a nonzero matrix. (Hence “if A” in older
code should be replaced by “if len(A)”.) The optimization
routines now return the last iterates when returning with status
'unknown', and provide information about the accuracy of the
solution they return. An element-wise max and min of matrices. Schur
factorization routines from LAPACK.
- Version 1.0 (April 24, 2008).
- Addition of two-dimensional discrete transforms. Performance
improvements in the optimization routines. Interfaces to the MOSEK and
GLPK integer LP solvers (these features are documented in the source
- Version 0.9.3 (February 24, 2008).
- A new solver for quadratic programming with linear cone constraints.
Minor changes to the other solvers: the option of requesting several
steps of iterative refinement when solving Newton equations; the
fields W['dl'] and W['dli'] in the scaling dictionary described
in section 9.4 were renamed W['d'] and W['di'].
- Version 0.9.2 (December 27, 2007).
- The GNU Scientific Library is no longer required for installation.
The cvxopt.random module has been deleted, and the functions for
generating random matrices (random.uniform(),
random.normal(), random.getseed(), random.setseed())
have been moved to cvxopt.base. The upgrade also includes an
improved and more easily customized style of matrix formatting.
- Version 0.9.1 (November 23, 2007).
- A revision of the nonlinear optimization solver, with added support for
second-order cone and linear matrix inequality constraints. (A new
argument was added to the function solvers.cp(), but code that
uses the previous version should still work if the arguments A and
b are specified by keywords.) The functions in
cvxopt.random are now based on the random number generators of
the GNU Scientific Library. The MOSEK interface was upgraded to
version 5. A new function base.spdiag() for specifying sparse
block diagonal matrices.
- Version 0.9 (August 10, 2007).
- A new cone program solver, with support for second-order cone
- Version 0.8.2 (February 6, 2007).
- Performance improvements in the sparse matrix arithmetic. The LAPACK
solvers for banded and tridiagonal equations. Several bug fixes.
- Version 0.8.1 (October 31, 2006).
- Compatibility with Python 2.5. An extension of base.matrix()
to construct block matrices. A new function sparse() to create
sparse block matrices. The default value of
cholmod.options['supernodal'] was changed to 2.
- Version 0.8 (September 20, 2006).
- General sequences are allowed in matrix definitions and assignments.
The base.div(), base.mul(), and base.syrk()
functions. Elementwise exponentiation of dense matrices. The FFTW
interface. The optional arguments in BLAS and LAPACK have been
reordered so that the most important arguments come first. (This
affects previous code in which optional arguments were passed by
position instead of by keyword.) A revised nonlinear convex
optimization solver with a simpler calling sequence.
- Version 0.7.1 (August 1, 2006).
Complex sparse matrices. The sparse BLAS functions base.symv()
and base.gemm(). The DSDP5 interface.
The cvxopt.colamd and cvxopt.ccolamd interfaces were
removed. There are several important backward incompatible changes in
the definitions of base.matrix() and base.spmatrix():
- The x argument in base.matrix() is now required; it is no
longer possible to create matrices with uninitialized values.
- If the x argument in base.matrix() is of integer type,
an integer matrix is created. For example, “matrix(1)” now
creates an 'i' matrix; “matrix(1.0)” creates a
- Symmetric sparse matrices are no longer defined. The type
argument in base.spmatrix() has been removed.
- The x, I, J arguments in base.spmatrix() are all
- Version 0.7 (April 21, 2006).
- A semidefinite programming solver. LAPACK routines for QR
factorization. The base.gemv() function. The
base.smv() function was removed.
- Version 0.6.1 (February 27, 2006).
- Compatibility with the SciPy array interface. Portability to 64 bit
machines. LAPACK routines for matrix inversion. Generalized symmetric
eigenvalue problems and singular value decomposition. The
cvxopt.ldl module has been removed.
- Version 0.6 (December 27, 2005).
- Elementwise exp(), sin(), cos(), and
log() of dense matrices. Indexed assignments of sparse to dense
matrices. Pickling of dense and sparse matrices. Interfaces to the
matrix ordering libraries COLAMD and CCOLAMD. Several new functions in
cvxopt.cholmod. A new LP solver.
- Version 0.5 (October 20, 2005).
- The CHOLMOD interface. The nonlinear convex optimization solver in the
solvers module. Several bug fixes.
- Version 0.4 (May 18, 2005).
- Interfaces to the LP solvers in MOSEK and GLPK.
- Version 0.3 (March 29, 2005).
- Several minor additions and improvements.
- Version 0.2 (January 31, 2005).
- Sparse linear equation solvers from UMFPACK and LDL. A modeling tool
for convex piecewise-linear optimization problems.
- Version 0.1 (November 3, 2004).
- Dense and sparse matrix class. Some BLAS and LAPACK routines. A linear