Optimal Control Analytic Partial Derivatives Preparation
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Noble Hatten April 12, 2019 at 8:02 PM
All tasks completed; closing ticket.
Noble Hatten March 28, 2019 at 2:47 PM
Status 2019/03/28 [NH]
On Monday, Steve, Wendy, Josh, and I walked through what has been prototyped so far, and Josh and Wendy are starting to work on converting the Python to C++.
Wendy Shoan March 21, 2019 at 3:02 PM
Status 2019.03.21 [WCS]
Should I restart the conversion of the Python prototypes?
Noble Hatten March 21, 2019 at 2:57 PM
2019/03/21 [NH]
Created TransformUtil and moved transformations that were being done locally in GMATFunction into TransformUtil. TransformUtil functions take inputs like origin, axes, etc., so it does not need to know about a "phase" or "function."
Noble Hatten March 7, 2019 at 5:33 PMEdited
Status 209/03/07 [NH]
Working on a method of GMAT(OptimalControl)Function class that calculates unscaled Jacobian of function variables w.r.t. decision variables, given a phase point index.
So far, theoretically handles changes of orbit state representation and origin (as long as the origin is a celestial body).
Punting on axes for now. (Hooks for some of these derivatives already exist in GMAT, but not in my Python prototypes.)
Tested origin-change partials w.r.t. time and state against dirt-stupid finite differencing, and they seem to check out.
Scaling hooks already exist, but I haven't really tested them yet.
Next TO DO:
Mass partials.
Hook up to actual OC-relevant functions (currently just tested state-transformation Jacobians)
Keep working with Steve and Josh to nail down what it actually looks like to set a derivative in the code and implement that. (E.g., SETJAC())
Septuple check everything.
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Noble HattenNoble HattenReporter
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Noble HattenWendy ShoanComponents
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Scope: Prototype and test framework for analytic Jacobians in Python. Prepare astrodynamic utilities in C++ (e.g., state representation Jacobians).
[KP] Write textual specification for Jacobians of state transformations.
Written, but tough to tell if some of the time ones are correct without exhaustive testing.
[NH] Revise existing Python prototypes of state transformation Jacobians so that corner cases are handled the same way they are in the state transformations themselves. (E.g., transforming to/from Keplerian elements in for a circular and/or equatorial orbit.)
Not actually done, but putting off for now to focus on more pressing concerns. Finite-differencing to be used for corner cases in the interim.
[NH] Write Python prototypes of boundary functions and their Jacobians, including for state transformations.
State partials exist for transformations between Cartesian, sphericalAzFpa, and classical Keplerian state representations.
Completed a smooth ephemeris to be able to implement time Jacobians (see comment).
Have functions and Jacobians for a C3 function and simple linkage. These work if the phases and functions have different state representations and/or origins. However, they have only been checked against finite differences so far, not AD. That's fine for these simple functions, but, for more complicated functions, want AD.
[NH] Test boundary function Jacobian prototypes against automatic differentiation.
State partials for transformations between Cartesian, sphericalAzFpa, and classical Keplerian state representations have been tested against AD.
I think the last step to be able to do this is to get rid of dependencies on spiceypy. I think the only one currently is for time transformations, and I will replace that with an analytical approximation. (Thanks, Steve!)
Still an issue because of how the AD package requires that assignment statements be done. (No x[i] = y.)