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SIRT objective #1790

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@MargaretDuff MargaretDuff commented Apr 23, 2024

Changes

  • Changed the SIRT objective_function to match the mathematics - it is now a weighted least squares calculation i.e. $0.5*(Ax-b)^M(Ax-b)$ were $M=\frac{1}{A*\mathbb{1}}$.
  • Updated the documentation to explain this
  • Changed some of our SIRT tests so they do not all use the identity

We might need to consider the efficiency of the objective calculation

Testing you performed

Please add any demo scripts to https://github.com/TomographicImaging/CIL-Demos/tree/main/misc
New unit tests added

Related issues/links

Closes #1787

Checklist

  • I have performed a self-review of my code
  • I have added docstrings in line with the guidance in the developer guide
  • I have updated the relevant documentation
  • I have implemented unit tests that cover any new or modified functionality
  • CHANGELOG.md has been updated with any functionality change
  • Request review from all relevant developers
  • Change pull request label to 'Waiting for review'

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Please read and adhere to the developer guide and local patterns and conventions.

  • The content of this Pull Request (the Contribution) is intentionally submitted for inclusion in CIL (the Work) under the terms and conditions of the Apache-2.0 License
  • I confirm that the contribution does not violate any intellectual property rights of third parties

--->

@MargaretDuff MargaretDuff self-assigned this Apr 23, 2024
@MargaretDuff MargaretDuff added bug Something isn't working Waiting for review labels Apr 23, 2024
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@epapoutsellis epapoutsellis left a comment

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lgtm

@MargaretDuff MargaretDuff changed the title Bug fix for the SIRT objective SIRT objective Jul 5, 2024
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@jakobsj jakobsj left a comment

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Just a couple of minor things.

@@ -46,6 +46,8 @@ class SIRT(Algorithm):
:math:`\mathrm{prox}_{C}` is the projection over a set :math:`C`,
and :math:`\omega` is the relaxation parameter.

Note that SIRT is equivalent to gradient descent on a weighted least squares objective (weighted by :math:`M`) preconditioned by the sensitivity matrix (:math:`D`).
Thus the objective calculation for SIRT is the weighted least squares objective :math:`\frac{1}{2}\|A x - b\|^{2}_{M}`.
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Please add a line to define the formula for the M-norm.

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In the docstring.

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Done

y = self.operator.direct(self.x)
y.subtract(self.data, out = y)
wy=self.M.multiply(y)
self.objective.append(0.5* y.dot(wy))
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Do we want the 0.5? Could be omitted for (slight) speed-up and objective function rewritten to omit this. Should think about consistency with defaults in related operations including LeastSquares and CGLS though.

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There isn't a 0.5 in CGLS (it uses \min || A x - b ||^2_2) and the default for least squares is 1.0. So in some ways, SIRT would be odd to include the 0.5. Mathematically, it is optimising the same thing. Shall we remove it?

Comment on lines +212 to +215
y = self.operator.direct(self.x)
y.subtract(self.data, out = y)
wy=self.M.multiply(y)
self.objective.append(0.5* y.dot(wy))
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@gfardell gfardell Jul 10, 2024

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This is adding an additional 2 copies of the data in to memory so we need to try to improve it.

wy is the same as the residuals r in update and this is still in memory. So instead I think we can square r (in place) and then weight it with M (again in place). As r is not reused by the algorithm it's ok using the memory for this. The first step of the algorithm recomputes the new residuals in to r.

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I tried this but got numerical errors (effectively from dividing and multiplying by M). I will try to debug this again.

@gfardell gfardell added this to the v24.2.0 milestone Jul 15, 2024
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Incorrect objective calculation for SIRT
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