need GP class as data model about gpkit

[bqpd]

RE: 'matrix of exponents associated with each constant in each constraint'

I think the GP class should have a 'constants' attribute that it applies to its purely symbolic representation of the cost and constraints before solving, and an 'update' function. This would look something like:

GP = gpkit.GP(cost, slf+other_constraints, constants)
sol1 = GP.solve()['solution']
# change the constraints...
GP.update(constraints = new_constraints)
sol2 = GP.solve()['solution']  # also stored at GP.solution
# change the constants...
GP.update(constants = new_constants)
sol3 = GP.solve()['solution']
# change the cost function and merge with another GP
GP.update(new_cost, new_GP.constraints, new_GP.constants)
# the GP class will have automatically generated and efficiently kept up-to-date:
#    - a sparse A matrix (called F by CVXopt)
#    - a c vector (called g by CVXopt)
#    - a map vector for MOSEK
#    - a 'k' vector for CVXopt, because I can't see of a clean way to do without it
sol4 = GP.solve()['solution']

[whoburg]

yes, like everything we're outlining here.

[bqpd]

I implemented it just as above except that GP.solve() does not return a dictionary, but the primal solution. Everything in the original request has been covered...