Predicates on coordinate spaces in this section are treated classically when needed in piecewise definitions.
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Helper for Text 34.1.4: reverse-minimax inequality on a pair of open balls, derived from local concave-convexity on those balls.
This lemma is the true upstream minimax/duality input required by the current proof route.
Once it is available, helperForText_34_1_4_reverseMinimax_closedBallRadiusEnvelope follows
by deterministic rewriting to the closed-ball-radius envelope form.
Helper for Text 34.1.4: a one-dimensional coordinate kernel used to exhibit that Jensen-style concave-convexity on open balls does not force existence of a saddle point.
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Helper for Text 34.1.4: the coordinate kernel is concave-convex on every pair of open balls.
The first-variable Jensen inequality holds by equality for the linear map w ↦ w 0, while the
second-variable inequality holds because the kernel is constant in the second variable.
Helper for Text 34.1.4: the one-dimensional coordinate kernel is globally concave-convex.
Helper for Text 34.1.4: the first-variable closure of the coordinate kernel vanishes at the origin of the first variable.
Helper for Text 34.1.4: the mixed lower closure of the coordinate kernel also vanishes at the origin of the first variable.
Helper for Text 34.1.4: a one-dimensional coordinate kernel in the second variable, used to test the right closed-ball bridge.
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Helper for Text 34.1.4: the second-coordinate kernel is globally concave-convex.
Helper for Text 34.1.4: the first closure of the second-coordinate kernel is trivial because the kernel is constant in the first variable.
Helper for Text 34.1.4: the second closure of the second-coordinate kernel vanishes at the origin of the second variable.
Helper for Text 34.1.4: the mixed lower closure of the second-coordinate kernel also vanishes at the origin of the second variable.
Helper for Text 34.1.4: the coordinate kernel admits no saddle point on open balls.
This gives a concrete counterexample to the naive implication
IsConcaveConvexOn (openBall u ε) (openBall xStar δ) K → ∃ saddle point,
so the corresponding step in the proof pipeline must be replaced by a genuine minimax/duality
theorem with additional hypotheses (compactness/attainment, semicontinuity, etc.).
Helper for Text 34.1.4: counterexample showing that the open-ball saddle-point existence step
cannot be proved from IsConcaveConvexOn alone.
Helper for Text 34.1.4: the abandoned open-ball route is already false in dimension 1.
Concretely, there is no uniform theorem saying that local concave-convexity on open balls forces existence of a saddle point on those balls.
Helper for Text 34.1.4: the coordinate kernel satisfies the reverse-minimax inequality on open balls even though it has no saddle point there.
This emphasizes that failure of saddle-point attainment does not by itself refute reverse minimax; what is missing for the general theorem is a genuine minimax/duality input, not the simple saddle-point existence step.
Helper for Text 34.1.4: graph-function closedness upgrades a Rockafellar convex bifunction to a closed convex bifunction.
Dimension-wide realization qualification for the converse direction of Corollary 33.3.1. The forward closure theorem does not construct a bifunction witness from an arbitrary closure pair, so the two canonical realization clauses are recorded explicitly.
- upper (K : SaddleFunction m n) (h : IsConcaveConvex K) : HasNoBotValuesBifunction K → HasNoTopOrBotValuesBifunction (lowerClosureConcaveConvex K h) → ∃! F : (Fin m → ℝ) → (Fin n → ℝ) → EReal, ClosedConvexBifunction F ∧ HasNoBotValuesBifunction F ∧ (∀ (u : Fin m → ℝ) (xStar : Fin n → ℝ), lowerClosureConcaveConvex K h u xStar = convexBifunctionPairing F u xStar) ∧ ∀ (u : Fin m → ℝ) (xStar : Fin n → ℝ), partialClosure₁ (lowerClosureConcaveConvex K h) u xStar = convexBifunctionCanonicalAdjointPairing F xStar u
- lower (K : SaddleFunction m n) (h : IsConcaveConvex K) : HasNoBotValuesBifunction K → HasNoTopOrBotValuesBifunction (partialClosure₂ (upperClosureConcaveConvex K h)) → ∃! F : (Fin m → ℝ) → (Fin n → ℝ) → EReal, ClosedConvexBifunction F ∧ HasNoBotValuesBifunction F ∧ (∀ (u : Fin m → ℝ) (xStar : Fin n → ℝ), partialClosure₂ (upperClosureConcaveConvex K h) u xStar = convexBifunctionPairing F u xStar) ∧ ∀ (u : Fin m → ℝ) (xStar : Fin n → ℝ), upperClosureConcaveConvex K h u xStar = convexBifunctionCanonicalAdjointPairing F xStar u
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Helper for Text 34.1.4: the qualified converse of Corollary 33.3.1 gives a closed convex witness for the
canonical pair (underline(K), cl₁ underline(K)) once the primal side is assumed to avoid both
⊥ and ⊤.
Helper for Text 34.1.4: the corrected Corollary 33.3.1 likewise gives a closed convex
witness for the canonical pair (cl₂ overline(K), overline(K)) once the lower partner is
assumed to avoid both ⊥ and ⊤.
Helper for Text 34.1.4: a closed convex bifunction already has graph function fixed by the raw convex-closure operator.
Helper for Text 34.1.4: the constant ⊥ function is concave in the Jensen sense on ℝ^n.
Route correction for Text 34.1.4: the zero-dimensional constant-⊥ kernel is
concave-convex, and its mixed lower closure still equals ⊥ at the unique point.
Route correction for Text 34.1.4: the statement
HasNoBotValuesBifunction (lowerClosureConcaveConvex K h) is false in general. The
zero-dimensional constant-⊥ kernel already refutes it.
Helper for Text 34.1.4: although underline(K) need not avoid ⊥ in general, any actual
closed-convex witness for underline(K) forces the represented mixed lower closure to have no
⊥ values.
Helper for Text 34.1.4: the usable no-⊥ statement for underline(K) is the witness-local
one. The naive global version is false by the constant-⊥ counterexample above, so the
remaining development only uses this corrected interface.
Helper for Text 34.1.4: if a convex bifunction represents underline(K), then none of its
parameter sections can be identically ⊤; otherwise the pairing would force underline(K) to
take the value ⊥.
Helper for Text 34.1.4: any convex bifunction representing underline(K) already has full
strict parameter domain, because underline(K) never takes the value ⊥.
Helper for Text 34.1.4: any convex bifunction representing underline(K) has full
Section 33 parameter domain, not just the strict < ⊤ variant.