Begin overhauling docs to numpy format.

[dunshire.git] / TODO

diff --git a/TODO b/TODO
index a75a1aa011f36a3d0ac1ee9aa18d70073dcb895e..ab87751f3168ceecdf739128a222dd766828c377 100644 (file)
--- a/TODO
+++ b/TODO
@@ -1,20 +1,23 @@
-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. Add real docstrings everywhere.
 
-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. Use a positive tolerance when comparing floating point numbers.
 
-9. Make it work on a cartesian product of cones in the wrong order
-   (apply a perm utation before/after).
+9. Come up with a fast heuristic (like making nu huge and taking e1 as
+   our point) that finds a primal feasible point.
+
+10. Ensure that we only compute eigenvalues of *symmetric* matrices
+    (so that the eigenvalues are real).