X-Git-Url: http://gitweb.michael.orlitzky.com/?a=blobdiff_plain;f=TODO;h=76c435875bdae09d370839976c70c749c8cd9025;hb=b176f008ba7fee7a41fb1f3645dbf9c526e86cbe;hp=a75a1aa011f36a3d0ac1ee9aa18d70073dcb895e;hpb=e48dec4e5ae09d18c4e76d8ae7191ec0f42550bf;p=dunshire.git

diff --git a/TODO b/TODO
index a75a1aa..76c4358 100644
--- a/TODO
+++ b/TODO
@@ -1,20 +1,24 @@
-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.
+5. Make it work on a cartesian product of cones in the wrong order
+   (apply a perm utation before/after).
 
-6. Add real docstrings everywhere.
+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. Try to eliminate the code in matrices.py.
+7. Make sure we have the dimensions of the PSD cone correct.
 
-8. Make it work on a cartesian product of cones in the correct order.
+8. Come up with a fast heuristic (like making nu huge and taking e1 as
+   our point) that finds a primal feasible point.
 
-9. Make it work on a cartesian product of cones in the wrong order
-   (apply a perm utation before/after).
+9. We only need to include the API docs for dunshire.games in the
+   "user manual;" everything else can go in an appendix.
+
+10. The ice cream cone tests sometimes fail with "unknown" solution.