Minor change in roadmap
## TODO (Code)
- [ ] Parameter Selection: `rho`, `sigma` and `alpha` from examples (data driven). N.B. It seems that the optimal `rho` decreases with the problem dimensions
-- [ ] Add functions to update matrices in Python and Matalab
+- [ ] Implement functions to update `sigma`, `eps_inf` and `eps_unb` parameters
+- [ ] Add functions to update matrices in Python and Matlab
- [ ] Change embedded flag with VECTOR_PARAMETERS and MATRIX_PARAMETERS
+- [ ] Add unittests Python for code generation
+- [ ] Implement code generation in Matlab
- [ ] Fix relative criterion for termination condition
- [ ] Implement cheaper dual residual computation: (only one matrix-vector computation)
-- [ ] Implement functions to update `sigma`, `eps_inf` and `eps_unb` parameters
+
- [ ] Stress tests Maros Meszaros
- [ ] Link to CVXPY
## TODO (Paper)
-- [x] Write infeasibility/unbounedness conditions
-- [x] Write compact algorithm (basic ADMM steps)
- [ ] Talk about paper: [Inertial Proximal ADMM for Linearly Constrained Separable Convex Optimization](http://epubs.siam.org/doi/pdf/10.1137/15100463X) (similar to our robustification idea)
- [ ] Proove convergence to vectors satisfying Farkas lemma
- [ ] Write examples in the paper
