Physlib

QuantumInfo.ForMathlib.Minimax

8 declarations

theorem

A Lower Semicontinuous Function on a Non-empty Compact Set Attains its Minimum

#exists_isMinOn_lowerSemicontinuousOn

Let β\beta be a topological space and α\alpha be a topological space equipped with a linear order such that all lower rays (,a](-\infty, a] are closed (a closed-interval-of-comparison topology). Let sβs \subseteq \beta be a non-empty compact set. If a function f:βαf: \beta \to \alpha is lower semicontinuous on ss, then there exists some xsx \in s such that f(x)f(x) is the minimum value of ff on ss.

theorem

Linear maps are quasilinear on convex sets

#quasilinearOn

Let k\mathbf{k} be a semiring equipped with a partial order, and let EE and β\beta be modules over k\mathbf{k} where β\beta is also an ordered additive monoid satisfying the property that scalar multiplication by positive elements is monotonic (PosSMulMonoPosSMulMono). If f:Eβf: E \to \beta is a k\mathbf{k}-linear map and sEs \subseteq E is a convex set, then ff is quasilinear on ss.

theorem

Linear maps are quasiconvex on convex sets

#quasiconvexOn

Let k\mathbf{k} be a semiring with a partial order, and let EE and β\beta be modules over k\mathbf{k} that are also additive commutative monoids. Suppose β\beta is an ordered additive monoid where scalar multiplication by positive elements is monotonic. Let sEs \subseteq E be a convex set. For any k\mathbf{k}-linear map f:Eβf: E \to \beta, the restriction of ff to ss is quasiconvex.

theorem

Linear maps are quasiconcave on convex sets

#quasiconcaveOn

Let k\mathbf{k} be a ordered semiring, and let EE and β\beta be modules over k\mathbf{k} such that β\beta is an ordered additive monoid with covariant scalar multiplication. If f:Eβf: E \to \beta is a linear map and sEs \subseteq E is a convex set, then ff is quasiconcave on ss.

theorem

The evaluation map (x,y)f(x)(y)(x, y) \mapsto f(x)(y) for continuous linear maps fL(N,L(M,R))f \in L(N, L(M, \mathbb{R})) is continuous.

#continuous_stupid

Let MM and NN be real normed additive commutative groups that are also modules over R\mathbb{R}, with MM being finite-dimensional. Let f:NL(R;L(M;R))f: N \to L(\mathbb{R}; L(M; \mathbb{R})) be a continuous linear map (representing a family of continuous linear functionals). Then the evaluation map (x,y)f(x)(y)(x, y) \mapsto f(x)(y) from N×MN \times M to R\mathbb{R} is continuous.

theorem

Minimax theorem for continuous bilinear forms on compact convex sets

#minimax

Let MM be a finite-dimensional real normed topological vector space and NN be a real normed topological vector space. Let f:NL(M,R)f: N \to L(M, \mathbb{R}) be a continuous bilinear form (represented as a continuous linear map from NN to the dual space of MM). Given two sets SMS \subseteq M and TNT \subseteq N such that: 1. SS and TT are compact, 2. SS and TT are convex, 3. SS and TT are nonempty, then the following equality holds: infxTsupySf(x,y)=supySinfxTf(x,y)\inf_{x \in T} \sup_{y \in S} f(x, y) = \sup_{y \in S} \inf_{x \in T} f(x, y)

theorem

Von Neumann's Minimax Theorem for Bilinear Forms (infsup=supinf\inf \sup = \sup \inf)

#minimax

Let MM be a finite-dimensional real normed topological vector space. Let f:M×MRf: M \times M \to \mathbb{R} be a bilinear form on MM. Let SS and TT be subsets of MM such that: 1. SS and TT are compact, 2. SS and TT are convex, 3. SS and TT are nonempty, then the following equality holds: infxTsupySf(x,y)=supySinfxTf(x,y)\inf_{x \in T} \sup_{y \in S} f(x, y) = \sup_{y \in S} \inf_{x \in T} f(x, y)

theorem

Sion's minimax theorem for bilinear forms on compact convex sets (supinf=infsup\sup \inf = \inf \sup)

#minimax'

Let MM be a finite-dimensional real normed additive commutative group (a finite-dimensional real Banach space) with continuous addition and scalar multiplication. Let f:M×MRf: M \times M \to \mathbb{R} be a bilinear form on MM. Let SS and TT be subsets of MM such that both SS and TT are compact, convex, and nonempty. Then the supremum of the infimum equals the infimum of the supremum: supxSinfyTf(x,y)=infyTsupxSf(x,y)\sup_{x \in S} \inf_{y \in T} f(x, y) = \inf_{y \in T} \sup_{x \in S} f(x, y)