Affine invariance of the Löwner–John ellipsoid
ProvedConvexOptimization.lowner_john_affine_invariantThe Löwner–John ellipsoid commutes with nonsingular affine changes of coordinates.
Let , let be invertible, let , and write for the corresponding affine bijection. If is a Löwner–John pair for , then the image set also admits a Löwner–John pair , and its ellipsoid is the affine image of the original:
(Concretely one may take and , which is again symmetric-positive-definite up to the normalization built into the predicate.)
Affine invariance is what makes the normalization step of this mission legitimate: an arbitrary configuration can be mapped so that its Löwner–John ellipsoid becomes the unit ball, where the KKT identities take their clean isotropic form, and the conclusion transported back. It also shows the rounding factor is an affine invariant of the body, not an artefact of a particular position — determinant ratios, unlike volumes, are unchanged by .
Formalization Note Nonsingularity is stated as IsUnit T.det; the image is (fun v => T.mulVec v + t) '' S. The conclusion is existential in together with the set identity, even though uniqueness makes the pair determined. Source: Boyd & Vandenberghe §8.4.3, pp. 415–416.
import Mathlib import Definitions.Def_ConvexOptimization_ellipsoidBody import Definitions.Def_ConvexOptimization_IsLownerJohn open scoped RealInnerProductSpace ENNReal open MeasureTheory
theorem ConvexOptimization.lowner_john_affine_invariant {nn : ℕ}
(T : Matrix (Fin nn) (Fin nn) ℝ) (hT : IsUnit T.det) (t : Fin nn → ℝ)
(S : Set (Fin nn → ℝ)) (A : Matrix (Fin nn) (Fin nn) ℝ) (b : Fin nn → ℝ)
(h : IsLownerJohn A b S) :
∃ (A' : Matrix (Fin nn) (Fin nn) ℝ) (b' : Fin nn → ℝ),
IsLownerJohn A' b' ((fun v => T.mulVec v + t) '' S) ∧
ellipsoidBody A' b' = (fun v => T.mulVec v + t) '' ellipsoidBody A b := by
sorry
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What the Lean code literally says, in plain math · claude-fable-5
Theorem. For every , every real matrix whose determinant is a unit in (over the reals this means exactly , i.e. invertible), every translation vector , every set (an arbitrary set — not required to be finite, a point range, convex, bounded, or full-dimensional), and every pair satisfying the Löwner–John predicate for (unfolded: symmetric; positive definite, which in Mathlib includes symmetry; ; and for every symmetric positive-definite with any offset whose body contains ), there exist a matrix and a vector such that both conjuncts hold for the same witness pair: (i) satisfies the Löwner–John predicate for the image set , and (ii) as an equality of sets, — the new body is exactly the image of the old body under the same affine map . Only existence is claimed; no formula for is given and no uniqueness is asserted. Edge cases: for the unique matrix has determinant (a unit) and everything lives in a one-point space; for any for which the Löwner–John predicate is unsatisfiable, the hypothesis on cannot hold and the statement is vacuous for that .
Confirmed by the mission captain (proposal self-audit).