-1. Add doctests for simple examples like the ones in Dr. Gowda's paper
- and the identity operator.
+1. Add unit testing for crazier things like random invertible matrices.
-2. Add unit testing for crazier things like random invertible matrices.
+2. Copy the intro from my thesis into README.rst, and add a section
+ explaining the CVXOPT formulation.
-3. Test that the primal/dual optimal values always agree (this implies
- that we always get a solution).
+3. Try to eliminate the code in matrices.py.
-4. Run the tests with make test.
+4. Make it work on a cartesian product of cones in the correct order.
-5. Use pylint or whatever to perform static analysis.
-
-6. Add real docstrings everywhere.
-
-7. Try to eliminate the code in matrices.py.
-
-8. Make it work on a cartesian product of cones in the correct order.
-
-9. Make it work on a cartesian product of cones in the wrong order
+5. Make it work on a cartesian product of cones in the wrong order
(apply a perm utation before/after).
-10. Add (strict) cone containment tests to sanity check e1,e2.
+6. Rename all of my variables so that they don't conflict with CVXOPT.
+ Maybe x -> xi and y -> gamma in my paper, if that works out.
+
+7. Make sure we have the dimensions of the PSD cone correct.
-11. Rename all of my variables so that they don't conflict with CVXOPT.
- Maybe x -> xi and y -> gamma in my paper, if that works out.
+8. Come up with a fast heuristic (like making nu huge and taking e1 as
+ our point) that finds a primal feasible point.
-12. Make sure we have the dimensions of the PSD cone correct.
+9. We only need to include the API docs for dunshire.games in the
+ "user manual;" everything else can go in an appendix.
-13. Use a positive tolerance when comparing floating point numbers.
+10. Should our one exception also spit out the game parameters?
projects / dunshire.git / blobdiff