Describe the bug
The correctness of max_inscribed_ball is currently not verified by tests on polytopes with known analytical inscribed-ball solutions (e.g., axis-aligned boxes, simple simplices, low-dimensional cases). As a result, the algorithm may return numerically plausible but incorrect centers or radii without detection.
To Reproduce
Run max_inscribed_ball on simple polytopes with known inscribed ball properties (e.g., unit box, low-dimensional polytopes). There are currently no tests that validate the returned center and radius against expected analytical values.
Expected behavior
For polytopes with known inscribed ball solutions, max_inscribed_ball should return a center and radius that satisfy the constraints and match the expected analytical result within tolerance, and this behavior should be covered by regression tests.
Additional context
max_inscribed_ball is a foundational preprocessing routine used for feasibility detection, rounding, and sampling initialization. Its correctness currently relies on assumptions (e.g., normalized constraints, numerical scaling) that are not exercised or validated by tests. Adding targeted, non-invasive tests would help prevent silent correctness regressions and clarify intended behavior without changing the algorithm.
Describe the bug
The correctness of
max_inscribed_ballis currently not verified by tests on polytopes with known analytical inscribed-ball solutions (e.g., axis-aligned boxes, simple simplices, low-dimensional cases). As a result, the algorithm may return numerically plausible but incorrect centers or radii without detection.To Reproduce
Run
max_inscribed_ballon simple polytopes with known inscribed ball properties (e.g., unit box, low-dimensional polytopes). There are currently no tests that validate the returned center and radius against expected analytical values.Expected behavior
For polytopes with known inscribed ball solutions,
max_inscribed_ballshould return a center and radius that satisfy the constraints and match the expected analytical result within tolerance, and this behavior should be covered by regression tests.Additional context
max_inscribed_ballis a foundational preprocessing routine used for feasibility detection, rounding, and sampling initialization. Its correctness currently relies on assumptions (e.g., normalized constraints, numerical scaling) that are not exercised or validated by tests. Adding targeted, non-invasive tests would help prevent silent correctness regressions and clarify intended behavior without changing the algorithm.