Convex sets and functions play a crucial role in Banach spaces. They're the building blocks for many important theorems and applications in functional analysis. Understanding these concepts is key to grasping more advanced topics.
The is a powerful tool for extending linear functionals and separating convex sets. It has far-reaching consequences, including the existence of non-trivial and the characterization of reflexive Banach spaces.
Convex Sets and Functions in Banach Spaces
Convex sets in Banach spaces
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contains all points on the line segment connecting any two points within the set
Examples of convex sets in Banach spaces include closed balls B[x0,r]={x∈X:∥x−x0∥≤r}, hyperplanes {x∈X:f(x)=α} where f is a continuous linear functional and α∈R, and halfspaces {x∈X:f(x)≤α} or {x∈X:f(x)≥α}
Convex functions in Banach spaces
satisfies the inequality f((1−t)x+ty)≤(1−t)f(x)+tf(y) for any x,y∈X and t∈[0,1], meaning the line segment connecting any two points on the graph lies above or on the graph
Examples of convex functions in Banach spaces include norms f(x)=∥x∥, continuous linear functionals f(x)=⟨x∗,x⟩ where x∗∈X∗ (dual space), and affine functions f(x)=⟨x∗,x⟩+α where x∗∈X∗ and α∈R
Hahn-Banach Theorem and Its Consequences
Hahn-Banach theorem applications
Hahn-Banach theorem (analytic form) extends any continuous linear functional defined on a subspace to the whole space without increasing its norm
Hahn-Banach theorem (geometric form) separates any closed and a point outside it by a hyperplane
Consequences of the Hahn-Banach theorem:
Normed spaces have enough continuous linear functionals to separate points
The dual space X∗ of a X is non-trivial (X∗={0})
The bidual space X∗∗ is isometrically isomorphic to X for reflexive Banach spaces
Subdifferentials in Banach Spaces
Subdifferentials in Banach spaces
of a convex function f at x∈X is the set ∂f(x)={x∗∈X∗:f(y)≥f(x)+⟨x∗,y−x⟩,∀y∈X}, generalizing the concept of the gradient for non-differentiable convex functions
Properties of subdifferentials:
If f is differentiable at x, then ∂f(x)={∇f(x)}
If f is continuous at x, then ∂f(x) is a non-empty, convex, and weak* compact subset of X∗
The is a monotone operator, satisfying ⟨x∗−y∗,x−y⟩≥0 for any x,y∈X, x∗∈∂f(x), and y∗∈∂f(y)
Applications of subdifferentials include optimality conditions (x is a minimizer of f if and only if 0∈∂f(x)) and characterizing proximal operators proxf(x)=argminy∈X{f(y)+21∥y−x∥2}
Convex Conjugate Functions
Properties of convex conjugates
Convex conjugate (or ) of a convex function f is defined as f∗(x∗)=supx∈X{⟨x∗,x⟩−f(x)} and is always convex, even if the original function is not
: f(x)+f∗(x∗)≥⟨x∗,x⟩ for all x∈X and x∗∈X∗
Double conjugate: (f∗)∗=f if and only if f is convex, lower semicontinuous, and proper
Subdifferential characterization: x∗∈∂f(x) if and only if f(x)+f∗(x∗)=⟨x∗,x⟩
Examples of convex conjugates
Indicator function δC(x)={0,+∞,x∈Cx∈/C where C is a closed convex set has conjugate equal to the support function δC∗(x∗)=supx∈C⟨x∗,x⟩
Norm f(x)=∥x∥ has conjugate equal to the indicator function of the unit ball in the dual space, f∗(x∗)=δB[0,1](x∗)
Quadratic function f(x)=21∥x∥2 has conjugate that is also a quadratic function, f∗(x∗)=21∥x∗∥2