...

Commits (34)
 ... ... @@ -74,4 +74,19 @@ v 1.0.1, 27.08.14: * Improved implementation of the retrieval of optimal primal variables with CPLEX. With the previous versions there was an important overhead at the end of the solving process to get the optimal values, this is now working much faster. * Nicer documentation. \ No newline at end of file * Nicer documentation. v 1.0.2, 30.01.15: Major release with following new functionalities: * Support (read and write) for .cbf problem files (conic benchmark format _ ), which should be the standard for (mixed integer) conic optimization problems, cf. :func:write_to_file  and :func:import_cbf  . * Improved support for complex SDP (more efficient implementation of :func:to_real()  , corrected bug in the implementation of the scalar product for Hermitian matrices and the conjugate of a complex expression, support for equality constraints involving complex coefficients) * Support for inequalities involving the sum of k largest elements of an affine expression, or the k largest eigenvalues of a symmetric matrix expression, cf. the functions :func:sum_k_largest()  , :func:sum_k_smallest()  , :func:sum_k_largest_lambda() , :func:sum_k_smallest_lambda() , :func:lambda_max()  and :func:lambda_min()  . * Support for inequalities involving the :math:L_{p,q}- norm of an affine expresison, cf. :func:norm()  . * New vtype for antisymmetric matrix variables ( :attr:vtype  = antisym). * Constraints can be specified as membership in a :class:Set  . Sets can be created by the functions :func:ball()  , :func:simplex() , and :func:truncated_simplex()  . * New functions :func:maximize  and :func:maximize  to specify the objective function of a problem and solve it. And many thanks to Petter Wittek _ for the following improvements, who were motivated by the use of PICOS in the package ncpol2sdpa _ for optimization over noncommutative polynomials: * More efficient implementation of the writer to the sparse - SDPA file format (:func:write_to_file ) * Hadamard (elementwise) product of affine expression is implemented, as an overload of the ^ operator, cf. an example :ref:here  . * Partial transposition of an Affine Expression, cf. :func:partial_transpose()  or the :attr:Tx  attribute. \ No newline at end of file
 # file GENERATED by distutils, do NOT edit CHANGES.txt LICENSE.txt README.txt monomials.txt setup.py doc/api.rst doc/changes.rst doc/complex.rst doc/constraint.rst doc/download.rst doc/examples.rst doc/expression.rst doc/graphs.rst doc/index.rst doc/intro.rst doc/optdes.rst doc/problem.rst doc/summary.rst doc/tools.rst doc/tuto.rst doc/tuto_summary.rst doc/_static/picos_big_trans.gif doc/_static/css/theme.css doc/_templates/download.html doc/_templates/localtoc2.html picos/__init__.py picos/constraint.py picos/cplex_callbacks.py ... ...
MANIFEST.in 0 → 100755
 include *.txt recursive-include doc *.rst recursive-include doc/_templates *.html recursive-include doc/_static *
 ... ... @@ -3,11 +3,13 @@ lancer python ../../add_warning_notlast.py from directory /doc/full_html/x.x.x ( lancer script add_google_script directly from /doc Changer CHANGE file Creer dist file (python setup.py sdist) -> check that the doc is in the dist file ??? make html (to copy dist) rsync to /www: rsync -luzvr full_html/* opts1.zib.de:/www/Abt-Optimization/picos [OBSOLETE: move to /www, and change root-index with 3 and \$sed -i 's/href="/href="v013\//g' index.html] rsync to /www Register on pypi (python setup.py register, username guillaume.sagnol) sed -i 's/Picos 0\.1\.0/Picos 0\.1\.1/g' picos/*.py et change version num dans conf.py and setup.py and __init__.py make new directory in the full_html directory and update the symolic link "last" \ No newline at end of file make new directory in the full_html directory and update the symolic link "last" with the ".." trick \ No newline at end of file
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 newlines =['', '
', '

Warning

', '

You are consulting the doc of a former version of PICOS.', 'The latest version is HERE.

', '
' ] #newlines =['', #'
', #'

Warning

', #'

You are consulting the doc of a former version of PICOS.', #'The latest version is HERE.

', #'
' #] newlines = [ '
', '
', '

Warning

', '

You are consulting the doc of a former version of PICOS. The', ' latest version is HERE.

', '
'] import os files = os.popen('ls *.html').readlines() files = [f[:-1] for f in files if f[:6] not in ('search','py-mod','genind')] for f in files: print f fi=open(f,'r') fitmp=open(f+'tmp','w') line = fi.readline() while '' not in line: #while '' not in line: while '
' not in line: fitmp.write(line) line = fi.readline() ... ...
 ... ... @@ -8,9 +8,28 @@ Change History ============== * 30 Jan. 15: **Picos** :ref:1.0.2  **Released** |br| Major release with following new functionalities: * Support (read and write) for .cbf problem files (conic benchmark format _ ), which should be the standard for (mixed integer) conic optimization problems, cf. :func:write_to_file  and :func:import_cbf  . * Improved support for complex SDP (more efficient implementation of :func:to_real()  , corrected bug in the implementation of the scalar product for Hermitian matrices and the conjugate of a complex expression, support for equality constraints involving complex coefficients) * Support for inequalities involving the sum of k largest elements of an affine expression, or the k largest eigenvalues of a symmetric matrix expression, cf. the functions :func:sum_k_largest()  , :func:sum_k_smallest()  , :func:sum_k_largest_lambda() , :func:sum_k_smallest_lambda() , :func:lambda_max()  and :func:lambda_min()  . * Support for inequalities involving the :math:L_{p,q}- norm of an affine expresison, cf. :func:norm()  . * New vtype for antisymmetric matrix variables ( :attr:vtype  = antisym). * Constraints can be specified as membership in a :class:Set  . Sets can be created by the functions :func:ball()  , :func:simplex() , and :func:truncated_simplex()  . * New functions :func:maximize  and :func:maximize  to specify the objective function of a problem and solve it. And many thanks to Petter Wittek _ for the following improvements, who were motivated by the use of PICOS in the package ncpol2sdpa _ for optimization over noncommutative polynomials: * More efficient implementation of the writer to the sparse - SDPA file format (:func:write_to_file ) * Hadamard (elementwise) product of affine expression is implemented, as an overload of the ^ operator, cf. an example :ref:here  . * Partial transposition of an Affine Expression, cf. :func:partial_transpose()  or the :attr:Tx  attribute. * 27 Aug. 14: **Picos** :ref:1.0.1  **Released** |br| Release fixing the missing functionnalities of the previous *.dev* version: Release fixing the missing functionalities of the previous *.dev* version: * Improved support for complex SDP (access to dual information and correction of a few bugs, in particular sum of complex affine expression now work correctly) * Flow constraints in graphs, including multicommodity flows, cf. :ref:this section . * Additional coef argument in the function :func:picos.tracepow() , in order to represent constraints of the form :math:\operatorname{trace}(M X^p) \geq t. ... ...
 :tocdepth: 2 .. _complex: ******************************** Complex Semidefinite Programming ******************************** ************************************ **Complex Semidefinite Programming** ************************************ Since the version 1.0.1, it is possible to do complex semidefinite programming with Picos. ... ... @@ -47,8 +49,8 @@ anf H (Hermitian transposition, i.e. exp.H returns exp.conj.T ). Fidelity in Quantum Information Theory ====================================== *Fidelity in Quantum Information Theory* ======================================== The material of this section is inspired from a lecture of John Watrous :ref:[4] . ... ... @@ -78,7 +80,6 @@ This quantity can be expressed as the optimal value of the following complex-val \end{eqnarray*} \end{center} This Problem can be solved as follows in PICOS .. testcode:: ... ... @@ -154,8 +155,8 @@ This Problem can be solved as follows in PICOS fidelity computed by trace-norm: F(P,Q) = 37.4742 Phase Recovery in Signal Processing =================================== *Phase Recovery in Signal Processing* ===================================== The material from this section is inspired from :ref:[3] . ... ... @@ -252,8 +253,8 @@ This problem can be implemented as follows using Picos: .. _complex_refs: References ========== *References* ============ 1. "Approximation algorithms for MAX-3-CUT and other problems via complex semidefinite programming", M.X. Goemans and D. Williamson. In Proceedings of the thirty-third annual ... ...
 ... ... @@ -72,7 +72,7 @@ copyright = u'2012, Guillaume Sagnol' # The short X.Y version. version = '1.0' # The full version, including alpha/beta/rc tags. release = '1.0.1' release = '1.0.2' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. ... ...
 :tocdepth: 1 .. _constraint: ========== Constraint ========== ============== **Constraint** ============== .. autoclass:: picos.Constraint :members: ... ... @@ -14,6 +16,10 @@ Constraint .. autoclass:: picos.NormP_Constraint :members: :inherited-members: slack .. autoclass:: picos.NormPQ_Constraint :members: :inherited-members: slack .. autoclass:: picos.TracePow_Constraint :members: ... ... @@ -25,3 +31,7 @@ Constraint .. autoclass:: picos.DetRootN_Constraint :members: :inherited-members: slack .. autoclass:: picos.Sumklargest_Constraint :members: :inherited-members: slack \ No newline at end of file
 ... ... @@ -6,7 +6,7 @@ Download The latest version of PICOS can be downloaded here: picos-1.0.1 _ picos-1.0.2 _ Installation instructions are explained :ref:here . ... ... @@ -15,6 +15,8 @@ Installation instructions are explained **Older versions** picos-1.0.1 _ picos-1.0.1.dev _ picos-1.0.0 _ ... ...
 :tocdepth: 2 .. _examples: ******** ... ... @@ -7,7 +9,7 @@ Examples .. toctree:: :maxdepth: 3 optdes.rst graphs.rst complex.rst optdes.rst
 :tocdepth: 2 .. _expression: ========== Expression ========== ============== **Expression** ============== .. autoclass:: picos.Expression AffinExp ======== *AffinExp* ========== .. autoclass:: picos.AffinExp :members: Variable """""""" *Variable* ========== .. _variable: .. autoclass:: picos.Variable :members: Norm ==== *Norm* ====== .. autoclass:: picos.Norm :members: QuadExp ======= *QuadExp* ========= .. autoclass:: picos.QuadExp :members: LogSumExp ========= *LogSumExp* =========== .. autoclass:: picos.LogSumExp :members: GeoMeanExp ========== *GeoMeanExp* ============ .. autoclass:: picos.GeoMeanExp :members: NormP_Exp ========= *NormP_Exp* =========== .. autoclass:: picos.NormP_Exp :members: TracePow_Exp ============ *TracePow_Exp* ============== .. autoclass:: picos.TracePow_Exp :members: DetRootN_Exp ============ *DetRootN_Exp* ============== .. autoclass:: picos.DetRootN_Exp :members: \ No newline at end of file :members: *Set* ===== .. autoclass:: picos.Set :members: *Ball* ====== .. autoclass:: picos.Ball :members: *Truncated_Simplex* =================== .. autoclass:: picos.Truncated_Simplex :members:
 ... ... @@ -13,10 +13,10 @@ Change History Release fixing the missing functionnalities of the previous *.dev* version: * Improved support for complex SDP (access to dual information and correction of a few bugs, in particular sum of complex affine expression now work correctly) * Flow constraints in graphs, including multicommodity flows, cf. :ref:this section . * Improved implementation of :func:_retrieve_matrix() , which was taking a very long time to process large parameters. * Additional coef argument in the function :func:picos.tracepow() , in order to represent constraints of the form :math:\operatorname{trace}(M X^p) \geq t. * Improved implementation of :func:_retrieve_matrix() , which was taking a very long time to process large parameters. * Improved implementation of the retrieval of optimal primal variables with CPLEX. With the previous versions there was an important overhead at the end of the solving process to get the optimal values, this is now working much faster. * Improved readibility of the documentation. * Nicer documentation. * 18 May 14: **Picos** :ref:1.0.1.dev  **Released** |br| ... ...
 ... ... @@ -143,7 +143,7 @@ we solve below for s=16 and t=10: --------------------- optimization problem (LP): 61 variables, 139 affine constraints 61 variables, 140 affine constraints f : dict of 60 variables, (1, 1), continuous F : (1, 1), continuous ... ...
 ... ... @@ -31,8 +31,9 @@ on every function of PICOS. Major Release with following changes: * Support for Semidefinite Programming over the complex domain, see :ref:here . * Flow constraints in graphs, cf. :ref:this section . * Additional coef argument in the function :func:picos.tracepow() , in order to represent constraints of the form :math:\operatorname{trace}(M X^p) \geq t. * Improved implementation of several functionalities, in particular the slicing of affine expressions (__getitem__), the processing of large matrix parameters, and the access to primal optimal variables with CPLEX. * Improved readibility of the documentation. * Nicer documentation. * 18 May 14: **Picos** :ref:1.0.1.dev  **Released** |br| Preliminary release of the 1.0.1 (still a few bugs for complex SDPs). ... ...
 ... ... @@ -344,6 +344,8 @@ Author and contributors their comments, ideas, questions, ... (in no particular order): * Dan Stahlke _ * Marco Dalai _ * Matteo Seminaroti _ ... ...
 ... ... @@ -1263,4 +1263,6 @@ References 4. "On the semidefinite representations of real functions applied to symmetric matrices _", G. Sagnol, Submitted, ZIB Report 12-50, 2012. \ No newline at end of file *Linear Algebra and its Applications*, 439(10), p. *2829-2843*, 2013. \ No newline at end of file
 ... ... @@ -808,11 +808,20 @@ creates the constraint is used, the base :math:x is forced to be nonnegative (resp. the base :math:X is forced to be positive semidefinite) by picos. When the exponent is :math:0. >>> pic.tracepow(X, 0.6666, coef = A[0].T*A[0]) >= t # trace of pth power ineq : trace[ A[0].T*A[0] *(X)**2/3]>t# As for geometric means, inequalities involving real powers are stored in a temporary object of the class :class:TracePow_Constraint , which contains a field Ptmp , a Problem instance with all the SOC or SDP constraints used to represent the original inequality. Inequalities involving generalized p-norm ----------------------------------------- ... ... @@ -1218,4 +1227,8 @@ References M.S. Lobo, L. Vandenberghe, S. Boyd and H. Lebret, *Linear Algebra and its Applications*, 284, p. *193-228*, 1998. 2. "On the semidefinite representations of real functions applied to symmetric matrices _", G. Sagnol, *Linear Algebra and its Applications*, 439(10), p. *2829-2843*, 2013.
 ... ... @@ -35,6 +35,22 @@ ... ... @@ -129,8 +145,7 @@

Warning

You are consulting the doc of a former version of PICOS. The latest version is HERE.

The PICOS Reference

... ...
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Warning

You are consulting the doc of a former version of PICOS. The latest version is HERE.

Change History

... ... @@ -140,10 +155,10 @@
• Improved support for complex SDP (access to dual information and correction of a few bugs, in particular sum of complex affine expression now work correctly)
• Flow constraints in graphs, including multicommodity flows, cf. this section.
• Improved implementation of _retrieve_matrix(), which was taking a very long time to process large parameters.
• Additional coef argument in the function picos.tracepow(), in order to represent constraints of the form .
• Improved implementation of _retrieve_matrix(), which was taking a very long time to process large parameters.
• Improved implementation of the retrieval of optimal primal variables with CPLEX. With the previous versions there was an important overhead at the end of the solving process to get the optimal values, this is now working much faster.
• Improved readibility of the documentation.
• Nicer documentation.
... ...
 ... ... @@ -36,6 +36,22 @@ ... ... @@ -132,8 +148,7 @@

Warning

You are consulting the doc of a former version of PICOS. The latest version is HERE.

Complex Semidefinite Programming

Since the version 1.0.1, it is possible to ... ...

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Warning

You are consulting the doc of a former version of PICOS. The latest version is HERE.

Constraint

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Warning

You are consulting the doc of a former version of PICOS. The latest version is HERE.

Installation instructions are explained here.

Older versions

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Warning

You are consulting the doc of a former version of PICOS. The latest version is HERE.

Examples

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Warning

You are consulting the doc of a former version of PICOS. The latest version is HERE.

Expression

... ...
 ... ... @@ -34,6 +34,22 @@ ... ... @@ -144,6 +160,7 @@ | I | K | L | M | N | O | P ... ... @@ -502,6 +519,16 @@

M

M (picos.TracePow_Exp attribute)

N

... ...
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Warning

You are consulting the doc of a former version of PICOS. The latest version is HERE.

Cut problems in graphs

The code below initializes the graph used in all the examples of this page. ... ... @@ -261,7 +276,7 @@ we solve below for s=16<

---------------------
optimization problem  (LP):
61 variables, 139 affine constraints
61 variables, 140 affine constraints

f   : dict of 60 variables, (1, 1), continuous
F   : (1, 1), continuous
...  ...
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Warning

You are consulting the doc of a former version of PICOS. The latest version is HERE.

PICOS: A Python Interface for Conic Optimization Solvers

... ... @@ -152,8 +167,9 @@ on every function of PICOS.

• Support for Semidefinite Programming over the complex domain, see here.
• Flow constraints in graphs, cf. this section.
• Additional coef argument in the function picos.tracepow(), in order to represent constraints of the form .
• Improved implementation of several functionalities, in particular the slicing of affine expressions (__getitem__), the processing of large matrix parameters, and the access to primal optimal variables with CPLEX.
• Improved readibility of the documentation.
• Nicer documentation.
... ...
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Warning

You are consulting the doc of a former version of PICOS. The latest version is HERE.

Introduction

PICOS is a user friendly interface ... ... @@ -439,6 +454,7 @@ their comments, ideas, questions, ... (in no particular order):

No preview for this file type
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Warning

You are consulting the doc of a former version of PICOS. The latest version is HERE.

Examples from Optimal Experimental Design

Optimal experimental design is a theory ... ... @@ -1241,7 +1256,8 @@ L. Vandenberghe, S. Boyd and S.P. Wu, SIAM journal on matrix analysis and ap 19(2), 499-533, 1998.

• On the semidefinite representations of real functions applied to symmetric matrices”, G. Sagnol, Submitted, ZIB Report 12-50, 2012.
• Linear Algebra and its Applications, 439(10), p. 2829-2843, 2013.
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Warning

You are consulting the doc of a former version of PICOS. The latest version is HERE.

Problem

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Warning

You are consulting the doc of a former version of PICOS. The latest version is HERE.

picos.tools

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Warning

You are consulting the doc of a former version of PICOS. The latest version is HERE.

Tutorial

First of all, let us import the PICOS module and cvxopt

... ... @@ -842,6 +857,14 @@ is used, the base , it is also possible to represent constraints of the form with SDPs, where , see [2].

>>> pic.tracepow(X, 0.6666, coef = A[0].T*A[0]) >= t
# trace of pth power ineq : trace[ A[0].T*A[0] *(X)**2/3]>t#

As for geometric means, inequalities involving real powers are stored in a temporary object of the class TracePow_Constraint, which contains a field Ptmp , a Problem instance with all the SOC or SDP constraints ... ... @@ -1204,6 +1227,10 @@ and On the semidefinite representations of real functions applied to symmetric matrices”, G. Sagnol, Linear Algebra and its Applications, 439(10), p. 2829-2843, 2013.

... ...
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 :tocdepth: 2 .. _graphs: ********************** Cut problems in graphs ********************** ************************** **Cut problems in graphs** ************************** The code below initializes the graph used in all the examples of this page. It should be run prior to any of the codes presented in this page. ... ... @@ -49,8 +51,8 @@ the output. In this example, we also use a kind of arbitrary sequence for the ed i=i+1 Max-flow, Min-cut (LP) ====================== *Max-flow, Min-cut (LP)* ======================== Max-flow '''''''' ... ... @@ -143,7 +145,7 @@ we solve below for s=16 and t=10: --------------------- optimization problem (LP): 61 variables, 139 affine constraints 61 variables, 140 affine constraints f : dict of 60 variables, (1, 1), continuous F : (1, 1), continuous ... ... @@ -416,8 +418,8 @@ Let us now draw the maximum flow: On this graph, the source in blue, the sink in green, and the edges defining the cut are marked in red. Multicut (MIP) ============== *Multicut (MIP)* ================ Multicut is a generalization of the mincut problem, in which several pairs of nodes must be disconnected. The goal is to find a cut of minimal ... ... @@ -588,7 +590,7 @@ Let us now draw the multicut: fig.gca().axes.get_yaxis().set_ticks([]) pylab.show() .. plot:: pyplots/multicut.py On this graph, the pairs of terminal nodes are denoted by ... ... @@ -596,8 +598,8 @@ dark and light colors of the same shade (e.g. dark vs. light green for the pairs (3,4),(3,9), and (3,18)), and the edges defining the cut are marked in red. Maxcut relaxation (SDP) ======================= *Maxcut relaxation (SDP)* ========================= The goal of the **maxcut** problem is to find a partition (S,T) of the nodes of an *undirected* graph G(V,E), ... ... @@ -633,13 +635,14 @@ onto a random hyperplan, we obtain a cut whose expected capacity is at least 0.878 times the optimum. Below is a simple implementation of their algorithm: .. testcode:: import cvxopt as cvx import cvxopt.lapack import numpy as np #make G undirected G=nx.Graph(G) #allocate weights to the edges for (i,j) in G.edges(): G[i][j]['weight']=c[i,j]+c[j,i] ... ... @@ -753,8 +756,8 @@ are blue or green depending on the partition they belong to. .. _graph_refs: References ========== *References* ============ 1. "Maximal Flow through a Network", LR Ford Jr and DR Fulkerson, *Canadian journal of mathematics*, 1956. ... ...
 ... ... @@ -27,18 +27,21 @@ on every function of PICOS. **News** * 27 Aug. 14: **Picos** :ref:1.0.1  **Released** |br| Major Release with following changes: * Support for Semidefinite Programming over the complex domain, see :ref:here . * Flow constraints in graphs, cf. :ref:this section . * Additional coef argument in the function :func:picos.tracepow() , in order to represent constraints of the form :math:\operatorname{trace}(M X^p) \geq t. * Improved implementation of several functionalities, in particular the slicing of affine expressions (__getitem__), the processing of large matrix parameters, and the access to primal optimal variables with CPLEX. * Nicer documentation. * 18 May 14: **Picos** :ref:1.0.1.dev  **Released** |br| Preliminary release of the 1.0.1 (still a few bugs for complex SDPs). * 30 Jan. 15: **Picos** :ref:1.0.2  **Released** Major release with following new functionalities: * Support (read and write) for .cbf problem files (conic benchmark format _ ), which should be the standard for (mixed integer) conic optimization problems, cf. :func:write_to_file  and :func:import_cbf  . * Improved support for complex SDP (more efficient implementation of :func:to_real()  , corrected bug in the implementation of the scalar product for Hermitian matrices and the conjugate of a complex expression, support for equality constraints involving complex coefficients) * Support for inequalities involving the sum of k largest elements of an affine expression, or the k largest eigenvalues of a symmetric matrix expression, cf. the functions :func:sum_k_largest()  , :func:sum_k_smallest()  , :func:sum_k_largest_lambda() , :func:sum_k_smallest_lambda() , :func:lambda_max()  and :func:lambda_min()  . * Support for inequalities involving the :math:L_{p,q}- norm of an affine expresison, cf. :func:norm()  . * New vtype for antisymmetric matrix variables ( :attr:vtype  = antisym). * Constraints can be specified as membership in a :class:Set  . Sets can be created by the functions :func:ball()  , :func:simplex() , and :func:truncated_simplex()  . * New functions :func:maximize  and :func:maximize  to specify the objective function of a problem and solve it. And many thanks to Petter Wittek _ for the following improvements, who were motivated by the use of PICOS in the package ncpol2sdpa _ for optimization over noncommutative polynomials: * More efficient implementation of the writer to the sparse - SDPA file format (:func:write_to_file ) * Hadamard (elementwise) product of affine expression is implemented, as an overload of the ^ operator, cf. an example :ref:here  . * Partial transposition of an Affine Expression, cf. :func:partial_transpose()  or the :attr:Tx  attribute. * 19 Jul. 13: **Picos** :ref:1.0.0  **Released** |br| with Semidefinite Programming Interface for MOSEK 7.0 !!! ... ... @@ -53,7 +56,7 @@ on every function of PICOS. :maxdepth: 2 intro tuto tuto_summary examples api download ... ...
 ... ... @@ -6,8 +6,8 @@ Introduction PICOS is a user friendly interface to several conic and integer programming solvers, very much like YALMIP _ under MATLAB _. very much like YALMIP _ or CVX _ under MATLAB _. The main motivation for PICOS is to have the possibility to enter an optimization problem as a *high level model*, ... ... @@ -59,7 +59,7 @@ interfaced solvers can be found :ref:here . * Mixed Integer Programming (**MIP**) * Convex Quadratically constrained Quadratic Programming (**convex QCQP**) * Second Order Cone Programming (**SOCP**) * Semidefinite Programming (**SDP**) * Semidefinite Programming (**SDP**), also with (complex-valued) Hermitian matrices. * General Quadratically constrained Quadratic Programming (**QCQP**) * Mixed Integer Quadratic Programming (**MIQP**) ... ... @@ -71,8 +71,9 @@ below, explaining their main differences with PICOS: This is a python interface that can be used to solve any convex optimization problem. However, CVXPY interfaces only the open source solver cvxopt _ for disciplined convex programming (**DCP**) problem that can be formed following the rules of disciplined convex programming (**DCP**). * Numberjack _: ... ... @@ -336,6 +337,8 @@ Author and contributors * Contributors: People who actively contributed to the code of Picos (in no particular order) * Petter Wittek _ * Paul Fournel * Bertrand Omont ... ...
 :tocdepth: 2 .. _optdes: ***************************************** Examples from Optimal Experimental Design ***************************************** ********************************************* **Examples from Optimal Experimental Design** ********************************************* Optimal experimental design is a theory at the interface of statistics and optimization, ... ... @@ -65,8 +67,8 @@ It should be run prior to any of the codes presented in this page. c = cvx.matrix([1,2,3,4,5]) c-optimality, multi-response: SOCP ================================== *c-optimality, multi-response: SOCP* ==================================== We compute the c-optimal design (c=[1,2,3,4,5]) for the observation matrices A[i].T from the variable A defined above. ... ... @@ -233,8 +235,8 @@ Generated output: [...] c-optimality, single-response: LP