Add prox operators for simple constraints #13
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ya_glm/opt/constraint/convex.py
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| def __init__(self, A, b): | ||
| self.A = A | ||
| self.b = b | ||
| self.pinvA = np.linalg.pinv(A) |
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Perhaps we should avoid computing this in init (sometimes we initialize this object just to check if it is proximable). Maybe we should cache it the first time we call prox? I've also though about having a .setup() method that precomputes required data for these functions.
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we'll do it as a @property
ya_glm/opt/constraint/convex.py
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| @@ -58,8 +75,9 @@ def is_proximable(self): | |||
| # See https://gist.github.com/mblondel/6f3b7aaad90606b98f71 | |||
| # for more algorithms. | |||
| def project_simplex(v, z=1): | |||
| if np.sum(v) <= z: | |||
| return v | |||
| # z is what the entries need to add up to, e.g. z=1 for probability simplex | |||
ya_glm/opt/constraint/convex.py
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| if np.sum(v) <= z: | ||
| return v | ||
| # z is what the entries need to add up to, e.g. z=1 for probability simplex | ||
| if np.sum(v) <= z: # don't we want the simplex to mean sum == z not sum <= z? |
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Agreed, but perhaps up to machine precision.
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So we can just delete this sum <= z check?
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Yes on second thought anytime we are calling project it's probably safe to assume we were not originally on the simplex.
ya_glm/opt/constraint/convex.py
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| return v | ||
| # z is what the entries need to add up to, e.g. z=1 for probability simplex | ||
| if np.sum(v) <= z: # don't we want the simplex to mean sum == z not sum <= z? | ||
| return v # also this doesn't work when v has, say, all negative entries |
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Ya actually we should probably check if it's positive too.
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Forgot what this was?
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Think we're ready to go here? |
Isotonic, LinearEquality, L2Ball constraints