Existence of saddle-values and saddle-points #
Saddle/Conjugate.lean proves the D* halves of the criteria for a saddle-value; this module
supplies the C* halves, turns the saddle-point criterion into a statement about K alone, and
then specialises everything to a finite continuous concave-convex function on a product of closed
convex sets — where the result is the classical minimax theorem.
The device is the involution saddleSwap K (y, u) = -K (u, y). Under it the class Ω (F) becomes
the class Ω (F♯) for the negated flipped pairings -Bx.flip, -Bu.flip, where F♯ is the
bifunction (F♯ y) v = -(F* y)(v), and the two conjugates are exchanged. So every C* statement
is its D* companion read at the swapped data, and no new duality is needed. The pairings must be
negated and not merely flipped: saddleSwap negates values, and only the negated pairings restore
the sign of the linear terms in the two conjugates.
The file closes with the identification of ∂K with the subdifferential of the graph function of
F, partially inverted along a linear homeomorphism.
Main results #
swapAdjointBifun,adjointBifun_swapAdjointBifun,saddleSwap_mem_bifunSaddleClass— the swap dictionary:saddleSwapcarriesΩ (F)ontoΩ (F♯), and the adjoint ofF♯is-F;upperConjSaddle_saddleSwap,lowerConjSaddle_saddleSwapexchange the two conjugates.proper_graphFn_of_properSaddleFn— a proper member ofΩ (F)forcesProper (graphFn F).zero_mem_interior_dom₁_lowerConjSaddle_iff—0 ∈ int C*in terms of directions of recession;hasSaddleValue_of_no_common_direction_of_recession_neg— no common direction of recession in the first variable gives a saddle-value;hasSaddleValue_of_isBounded_dom₁— likewise whenCis bounded.exists_isSaddlePoint_of_no_common_direction_of_recession— a saddle-point exists when neither variable has a common direction of recession (Theorem 37.6 in [^1]);exists_isSaddlePoint_of_isBounded_domSaddle— likewise for a bounded effective domain.saddleStructure_lowerSimpleExt,maximin_lowerSimpleExt,exists_bifunSaddleClass_lowerSimpleExt— the transfer to a finite continuous concave-convex function on a closedC × D, through its lower simple extension.biSup_biInf_eq_biInf_biSup_of_isBounded_right—sup inf = inf supwith one factor bounded;exists_saddlePoint_of_isBounded— the minimax theorem (Corollary 37.6.2 in [^1]).isBifunSubgradientPair_iff_mem_subgradient_graphFn,setOf_mem_saddleSubgradient_eq_preimage—∂Kis∂fpartially inverted, pointwise and as an equality of graphs;isClosed_setOf_mem_saddleSubgradient— the graph of∂Kis closed.
Implementation notes #
The sup inf = inf sup statements carry EReal-valued extrema. The customary display is
inf_D sup_C K = sup_C inf_D K for a finite K, but with only one of C, D bounded the two
iterated extrema can be ±∞. The minimax theorem, where both are bounded, is stated with real
inequalities.
References #
[^1]: R. T. Rockafellar, Convex Analysis, Princeton University Press, 1970, §24, §34, §36, §37.
saddleSwap and the conjugate saddle-functions #
The adjoint commutes with flipBifun once both pairings are negated and exchanged. This is
the identity behind the whole swap dictionary: adjointBifun is an infimum over U × X of
F u x + ⟨u, v⟩ - ⟨x, y⟩, and negating and exchanging the pairings restores that summand with the
two bound variables exchanged.
The bracket of y ↦ -(G y ·) at the negated flipped pairing is minus the concave bracket
of G. This is one of the two halves of the swap dictionary for equivalence classes.
The mirror half: the concave bracket of u ↦ -(H u ·) at the negated flipped pairing is
minus the bracket of H.
The class conjugate to Ω (F) seen through saddleSwap #
The convex bifunction attached to the swapped saddle-function: (F♯ y) v = -(F* y)(v).
It is not F_*^*, which goes from V to Y and belongs to the conjugate class on V × X;
F♯ goes from Y to V, and the two differ by flipBifun. Its two brackets at the negated
flipped pairings are the negatives of the two brackets of F, exchanged — which is what
saddleSwap does to the class Ω (F).
Equations
Instances For
F♯ is a convex bifunction: it is F_*^* with its two arguments exchanged.
F♯ is a closed bifunction, because an adjoint bifunction is closed.
The adjoint of F♯ is -F, which is the biadjoint identity (F_*^*)^* = F_* read
through adjointBifun_neg_flipBifun.
The swap dictionary for equivalence classes: saddleSwap carries Ω (F) onto Ω (F♯)
at the negated flipped pairings. Every statement about the first variable is therefore the
corresponding statement about the second variable, read here.
The two conjugates of saddleSwap K #
The upper conjugate of saddleSwap K is the swap of the lower conjugate of K.
Negating both pairings turns a supremum-of-infima into an infimum-of-suprema.
The lower conjugate of saddleSwap K is the swap of the upper conjugate of K.
Properness of the bifunction behind a proper class #
A proper member forces a proper bifunction. The two propernesses are usually not
separated, but the results here ask for Proper (graphFn F) while the structural theorems deliver
ProperSaddleFn K, so the bridge is crossed explicitly.
F u x = ⊥ would make the bracket ⟨Fu, y⟩ identically +∞, contradicting dom₂ K ≠ ∅; and
F ≡ +∞ would make the upper bracket -∞, contradicting dom₁ K ≠ ∅.
The C* half of the saddle-value criteria #
Negating and flipping a pairing exchanges its two separation properties.
The origin is an interior point of C* if and only if the convex functions -K (·, v), for
v ∈ ri D, have no common direction of recession. This is the D* half read at saddleSwap K,
whose class is Ω (F♯) for the negated flipped pairings.
If the convex functions -K (·, v) for v ∈ ri D have no common direction of recession,
then the saddle-value of K exists.
If the first effective domain of K is bounded, the saddle-value of K exists.
Existence of a saddle-point #
If neither variable has a common direction of recession, K has a saddle-point. The two
recession conditions translate into 0 ∈ int C* and 0 ∈ int D*, and ∂K* (0, 0) is then a
nonempty set of saddle-points.
If both halves of the effective domain of K are bounded, K has a saddle-point.
The saddle-value is then finite — it is a value of K at a saddle-point, and a proper
saddle-function is finite on its effective domain.
A finite continuous saddle-function on C × D #
The lower simple extension is a closed proper concave-convex function, so it has the full structural description.
The sup inf of the lower simple extension over the whole space is the sup inf of K over
C × D.
The inf sup of the lower simple extension over the whole space is the inf sup of K over
C × D.
The unique closed convex bifunction whose lower bracket is the lower simple extension, packaged
with the membership K₁ ∈ Ω (F) that the results above consume.
The half where D is bounded: a finite continuous concave-convex function on a product of
nonempty closed convex sets, one of them bounded, has sup inf = inf sup over C × D. The lower
simple extension is a closed proper concave-convex function with effective domain C × D, so it
has a saddle-value, and its extrema over the whole space are the restricted ones.
The half where C is bounded.
The minimax theorem: a finite continuous concave-convex function on a product of nonempty
compact convex sets has a saddle-point relative to that product. The bounded-domain criterion gives
the lower simple extension a saddle-point, that saddle-point lies in C × D, and there the
extension agrees with K.
∂K as the subdifferential of the graph function, partially inverted #
Membership in the subdifferential of the class Ω (F) is membership in the subdifferential
of the graph function f of F, with the pair (u*, v) partially inverted to (-u*, v). No
hypothesis on F is needed: it is the conjugate criterion for a subgradient together with the
unconditional identity (F* v)(u*) = -f*(-u*, v).
The graph of ∂K is the graph of ∂f partially inverted, f the graph function of F,
as an equality of sets. The inversion (u, y, v, x) ↦ ((u, x), (-v, y)) is a linear homeomorphism,
which is what makes closedness and maximal monotonicity transfer from ∂f.
The graph of ∂K is closed: it is the preimage of the graph of ∂f under a linear
homeomorphism, and the graph of the subdifferential of a closed proper convex function is closed.
The homeomorphism clause is saddleSubgradientHomeomorph.