For MacOS 10.6 and Mathematica 8 the following should work

- Install 64-bit Python 2.7 distribution from official site
- Get latest cvxopt sources and make
- Get Pythonika sources from google site
- copy mathlink.framework (in /Applications/Mathematica.app/SystemFiles/Links/MathLink/DeveloperKit/CompilerAdditions/) to /Library/Frameworks directory
- Fill correct paths in Pythonika Makefile, also add "-lstdc++ -framework CoreFoundation" linker flags
- In Pythonika Makefile, rename libML.a into libMLi3.a

If your compilation doesn't work, post a follow-up comment here

Then you can use my package Bulatov`sdp1 to get direct access to one common form of SDP -- maximize a linear function of a matrix subject to positive-definiteness and element value constraints.

As an example application, consider MAXCUT -- find partition of the vertices such that number of edges connecting vertices from different partitions is maximized.

Solution above was obtained by solving a binary integer quadratic program, as follows

g = GridGraph[{4, 3}];

A = AdjacencyMatrix@g;

n = Length@VertexList@g;

u = ConstantArray[1, n];

vars = x /@ Range@n;

cons = And @@ (-1 <= x[#] <= 1 & /@ Range@n);

solution=vars /. Last[Maximize[{-vars.A.vars, cons}, vars, Integers]]

SDP relaxation gives a guaranteed approximation to binary quadratic program above --

Needs["Bulatov`sdp1`"];

Install["/Users/Yaroslav/mathematica/Pythonika"];

gram = solveDualSDP[-A, IdentityMatrix[n], u];

solution = gramRound[gram];

This gives the same solution for the example above, but is much faster. solveDualSDP here finds psd matrix X that maximizes -A.X with diagonal entries of X constrained to be 1. For more details on this formulation, see Chapter 6 of Williamson,Shmoys, Design of Approximation Algorithms. For details of how to convert SDPs to standard vectorized form as used by CVXOPT, see this post

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