Stabilize linear-update covariance symmetrization - #5256
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FlorianPfaff
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August 7, 2026 17:54
FlorianPfaff
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August 7, 2026 17:54
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Bug
linear_update_planning._symmetrized(...)averaged covariance-like matrices asFor large but finite values, the intermediate addition can overflow even though the mathematically correct symmetric average is finite. A concrete one-dimensional covariance equal to
0.75 * np.finfo(float).maxtherefore raisesFloatingPointErrorunder strict NumPy overflow handling before the planner can perform an otherwise valid update.This helper is used both while validating component covariance matrices and when symmetrizing nominal/effective innovation covariance matrices.
Fix
Scale each operand before adding it:
The expression is algebraically equivalent for ordinary finite inputs but avoids doubling a large entry before the factor of one half is applied.
Regression coverage
Add a public-path regression through
plan_linear_measurement_update(...)with a covariance of0.75 * np.finfo(float).maxundernp.errstate(over="raise", invalid="raise"). The test verifies that the plan remains accepted and that the nominal innovation covariance stays finite and exactly retains the large finite value.Validation
FloatingPointError: overflow encountered in add;main(f196ec41d1d8681e279936db9f1a0a3abfa276e7);