Marginally Stable

GSoC Week 6: Just the little things

Fri 27 June 2014 — under , , ,

I was rather busy with my research this week, so no time for a long-winded post like some of my previous updates. There's not much interesting to say anyway. This week was mostly spent on little fixes to get my current pull request merged.

Topping the list of things that are better than they were last week is speed. The profiling I did last week showed that the current function sympy.physics.mechanics uses to solve a system of linear equations (_mat_inv_mul) is sloooooooooow. The underlying reason is because subs is slow - more on that later. I spent some time swapping out all forms of solving ($A x = B$) for LUsolve, the clear winner of last weeks benchmarks. This resulted in a 10x speedup of the formulation of equations for the bicycle model example.

This bicycle example has become the bane of my existence for the last couple weeks. It's a super slow test that I'd never actual gotten to run before. But with the speed improvements made, it actual finishes in a reasonable time. Except it still doesn't work. I'm able to run all the way up to

M, A, B = KM.linearize()

But when I go to sub in values for symbols in these matrices, things get hairy. There are two issues:

Issue 1: Get nan when not simplified

M.subs(val_dict) results in nan and oo upon after subs. But doesn't if it's simplified before the subs. An example of this behavior would be:

>>> M = sin(q1)/tan(q1)
>>> M.subs({q1: 0}
nan

Note that if this is simplified, this results in something completely different:

>>> M = sin(q1)/tan(q1)
>>> M = M.trigsimp()
>>> M
cos(q1)
>>> M.subs({q1: 0})
1

However, for the bicycle case M has over 19 thousand operations. This doesn't simplify quickly. Also, by default we don't simplify before subs in Linearizer (you can opt in to simplify, but it's done right before the return, so it won't affect the subbed result at all). Right now I'm looking through ways to make the resulting expressions smaller after the formulation, as this will result in speedups for all operations. This could be extremely helpful for issue 2...

Issue 2: subs is slow

because A has over 38 million operations!!! In this case subs doesn't even return. Ever. I left it running on my computer for 4 hours and came back and it was still whirring along, fans on high, eating up all my ram. No idea how to solve this. One possible solution is csympy, a fast core written in C++. Once this matures, subs, trigsimp, and other time consuming operations used heavily in sympy.physics.mechanics could rely on the equivalent, faster, C++ versions. I filed an issue with an example expression generated from the bicycle example (this one only had 147,841 operations, not nearly as bad). Hopefully Ondrej and the team can use this as a benchmark problem to help improve subs in csympy.

If you have thoughts on how to overcome these issues, please let me know. I'm kind of stumped right now.

The Good News

I didn't want to end this post on a bad note, so I'll close with the remainder of the things I did last week that actually worked:

  1. Improved documentation! Docstrings that are worth reading, and a start on the sphinx documentation.

  2. Added a deprecation warning for KanesMethod.linearize to warn people about the method change.

  3. Major interface changes. Now all operating points are specified as a single dictionary, or an iterable of dictionaries. This is to aid in consistency across different system implementations. Referring to a dictionary as u_op in LagrangesMethod doesn't really make any sense, as Lagrange's method only uses $q$, $\dot{q}$, and $\ddot{q}$. Also added a kwarg to make simplification of the results optional.

  4. Added a method to the LagrangesMethod class to calculate the value of the multipliers at different points. This is useful for multiple reasons. The multipliers have meaning, so knowing what the solution is symbolically is nice for calculating the constraint forces. Also, when linearizing with Lagrange's method, the multipliers have operating points as well, and these need to be calculated based on the operating point for the other states ($q$, $\dot{q}$, etc...). Now a user can go:

    op_point = dict_or_iterable_of_dicts
    lam_op = LM.solve_multipliers(op_point)
    op_point.append(lam_op)     # Or op_point.update if op_point is a dict, not a list of dicts
    M, A, B = LM.linearize(q_ind=q_ind, qd_ind=qd_ind, op_point=op_point)
    

Hopefully in the next week I can get my PR merged, so the Lagrange stuff can finally be submitted.

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