TheoremBase

The origin is not an interior point of the convex set C − D, so the supporting hyperplane theorem gives a unit vector a with a·x ≤ a·y; the level α is the supremum of a over C.

Proof

Let M=C−D={x−y:x∈C, y∈D}M=C-D=\{x-y:x\in C,\ y\in D\} be the difference set; it is convex by Operations Preserving Convexity in Rn\mathbb{R}^n: Intersections, Translates, Scalar Multiples, Sums, Affine Images and Preimages, Closure and Interior (v). Since C∩D=∅C\cap D=\emptyset, the origin does not lie in MM, and since int⁡M⊆M\operatorname{int}M\subseteq M (Closure and Interior in a Metric Space: Containments, the Closure is Closed, the Interior is Open (i)), it does not lie in int⁡M\operatorname{int}M either.

By The Supporting Hyperplane Theorem in Rn\mathbb{R}^n (i), applied to MM and the origin, there is a∈Sa\in\mathbb{S} with a⋅w≤a⋅0=0a\cdot w\le a\cdot0=0 for every w∈Mw\in M. Taking w=x−yw=x-y and using bilinearity, we obtain

a⋅x≤a⋅yfor all x∈C and y∈D,a\cdot x\le a\cdot y\qquad\text{for all }x\in C\text{ and }y\in D,

and a≠0a\neq0 because ∥a∥=1\lVert a\rVert=1.

It remains to choose the level α\alpha. As DD is nonempty, fix y0∈Dy_{0}\in D. The set T={a⋅x:x∈C}T=\{a\cdot x:x\in C\} is nonempty, since CC is, and bounded above by a⋅y0a\cdot y_{0}, so it has a supremum α\alpha (The Real Numbers, with the Natural Numbers, Integers and Rationals Identified with Subsets of the Reals, and Completeness (vii)). As an upper bound of TT, α≥a⋅x\alpha\ge a\cdot x for every x∈Cx\in C. For y∈Dy\in D the number a⋅ya\cdot y is an upper bound of TT by the inequality just displayed, so α≤a⋅y\alpha\le a\cdot y, the supremum being the least upper bound (Bounds, Least and Greatest Elements, Suprema and Infima for a Partial Order (iv)). Hence

a⋅x≤α≤a⋅yfor all x∈C and y∈D,a\cdot x\le\alpha\le a\cdot y\qquad\text{for all }x\in C\text{ and }y\in D,

which is (i). Thus C⊆H≤(a,α)C\subseteq H^{\le}(a,\alpha) and D⊆H≥(a,α)D\subseteq H^{\ge}(a,\alpha) (Hyperplanes and Half-Spaces in Rn\mathbb{R}^n (ii)), and H(a,α)H(a,\alpha) separates CC and DD by Separating Hyperplanes in Rn\mathbb{R}^n: Separation, Strict Separation and Strong Separation (i).

Citations

Loading…

Dependencies

Uses0

Loading…

Comments

Log in to comment.

Loading…