Weak and are crucial concepts in functional analysis, offering alternative notions of convergence in normed spaces. They allow us to study sequences that don't converge in the usual norm topology but still exhibit useful convergence properties.

These convergence types are weaker than norm convergence but stronger than . They're especially useful in infinite-dimensional spaces, where norm convergence can be too restrictive for many important sequences or series.

Weak and Weak* Convergence

Weak vs weak* convergence

  • applies to sequences in a normed space XX converging to an element in XX
    • Sequence (xn)(x_n) converges weakly to xx if for every ff in the XX^*, the sequence of scalars f(xn)f(x_n) converges to f(x)f(x)
    • Notation: xnxx_n \rightharpoonup x
  • Weak* convergence applies to sequences in the dual space XX^* converging to an element in XX^*
    • Sequence (fn)(f_n) converges weak* to ff if for every xx in the original space XX, the sequence of scalars fn(x)f_n(x) converges to f(x)f(x)
    • Notation: fnff_n \stackrel{*}{\rightharpoonup} f
    • Only applicable in the dual space, not in the original normed space

Boundedness from weak convergence

  • Weakly convergent sequences in a normed space are always bounded
    • Consequence of the Uniform Boundedness Principle
  • If (xn)(x_n) converges weakly to xx, then there exists M>0M > 0 such that xnM\|x_n\| \leq M for all nn
    • If (xn)(x_n) were unbounded, there would exist a functional ff such that f(xn)|f(x_n)| is unbounded, contradicting weak convergence
  • Boundedness is a necessary condition for weak convergence, but not sufficient
    • Bounded sequences need not converge weakly (\ell^\infty with coordinate functionals)

Weak* and pointwise convergence

  • Weak* convergence of a sequence of functionals implies pointwise convergence
    • If (fn)(f_n) converges weak* to ff in XX^*, then for every fixed xx in XX, the sequence of scalars fn(x)f_n(x) converges to f(x)f(x)
  • Pointwise convergence does not imply weak* convergence
    • Pointwise convergence only considers convergence at each fixed point, while weak* convergence requires uniform convergence on bounded sets
  • Weak* convergence is stronger than pointwise convergence
    • Weak* convergence guarantees pointwise convergence, but not vice versa (1\ell^1 with coordinate functionals)

Examples of weak convergence types

  • Weak convergence without norm convergence:
    • Sequence (en)(e_n) in 2\ell^2, where en=(0,,0,1,0,)e_n = (0, \ldots, 0, 1, 0, \ldots) with 11 in the nn-th position
      • Converges weakly to 00 since for any f(2)f \in (\ell^2)^*, limnf(en)=0\lim_{n \to \infty} f(e_n) = 0
      • Does not converge in norm to 00 since en=1\|e_n\| = 1 for all nn
  • Weak* convergence without norm convergence:
    • Sequence (fn)(f_n) in 1\ell^1, where fn(x)=k=1nxkf_n(x) = \sum_{k=1}^n x_k for x=(xk)x = (x_k) \in \ell^\infty
      • Converges weak* to f(x)=k=1xkf(x) = \sum_{k=1}^\infty x_k since for any xx \in \ell^\infty, limnfn(x)=f(x)\lim_{n \to \infty} f_n(x) = f(x)
      • Does not converge in norm since fn=n\|f_n\| = n for all nn

Key Terms to Review (23)

Banach Space: A Banach space is a complete normed linear space where every Cauchy sequence converges within the space. This completeness property is vital in functional analysis as it ensures that limits of sequences remain within the space, allowing for robust analysis of functional properties and the behavior of operators.
Banach space: A Banach space is a complete normed vector space, meaning it is a vector space equipped with a norm such that every Cauchy sequence converges to a limit within the space. This property of completeness is crucial for ensuring the convergence of sequences, which allows for more robust analysis and applications in functional analysis.
Banach-Alaoglu Theorem: The Banach-Alaoglu Theorem states that in a normed space, the closed unit ball in the dual space is compact in the weak* topology. This theorem connects the concepts of dual spaces, weak topologies, and compactness, which are fundamental in understanding properties of linear functionals and their applications.
Bounded linear functional: A bounded linear functional is a specific type of linear functional that is continuous and maps elements from a normed vector space to the underlying field, typically the real or complex numbers. This concept is essential for understanding dual spaces, as it relates directly to the behavior of linear functionals in relation to the norms of the spaces they operate on.
Bounded sequence: A bounded sequence is a sequence of numbers where all its terms lie within a finite interval. This means there exists a real number M such that every term in the sequence is less than or equal to M and greater than or equal to -M. Understanding bounded sequences is crucial when discussing convergence properties, particularly in the context of weak and weak* convergence.
Continuous Linear Functional: A continuous linear functional is a linear map from a vector space into its field of scalars that is continuous with respect to the topology of the vector space. This concept is crucial in understanding how linear functionals operate within various spaces, particularly in the context of dual spaces, where every continuous linear functional corresponds to an element of the dual space, impacting many significant results in functional analysis.
Dual Space: The dual space of a vector space consists of all linear functionals defined on that space. It captures the idea of measuring or evaluating vectors in terms of how they interact with linear functionals, which are themselves linear maps that take vectors as input and return scalars.
Eberlein-Smulian Theorem: The Eberlein-Smulian Theorem states that a subset of a Banach space is weakly compact if and only if it is sequentially weakly compact, meaning that every sequence in the set has a subsequence that converges weakly to a limit within the set. This theorem provides a crucial connection between weak and weak* convergence in the context of functional analysis, particularly in studying compactness properties of subsets of Banach spaces.
Equivalence of norms: Equivalence of norms refers to the concept that two norms on a vector space are considered equivalent if they induce the same topology, meaning that they yield the same notions of convergence and boundedness. This idea is significant because it allows for the interchangeability of different norms when analyzing the behavior of sequences and functionals in functional spaces, particularly in discussions about weak and weak* convergence.
Hilbert Space: A Hilbert space is a complete inner product space that is a fundamental concept in functional analysis, combining the properties of normed spaces with the geometry of inner product spaces. It allows for the extension of many concepts from finite-dimensional spaces to infinite dimensions, facilitating the study of sequences and functions in a rigorous way.
Linear functional: A linear functional is a mapping from a vector space to its field of scalars that preserves the operations of vector addition and scalar multiplication. In other words, for a linear functional \( f \), it satisfies \( f(ax + by) = af(x) + bf(y) \) for all vectors \( x, y \) and scalars \( a, b \). This concept is crucial when discussing dual spaces and convergence properties in functional analysis.
Pointwise Convergence: Pointwise convergence refers to a type of convergence for a sequence of functions, where a sequence of functions converges to a limit function at each individual point in the domain. This concept is essential in understanding how functions behave as they approach a limiting function, which connects to the study of continuity, operator norms, dual spaces, orthonormal bases, eigenvalue problems, and different forms of convergence.
Portmanteau Theorem: The Portmanteau Theorem is a fundamental result in probability theory that provides various equivalent conditions for the convergence of a sequence of probability measures to a limit measure. This theorem connects different notions of convergence, such as weak convergence and convergence in distribution, helping to clarify the relationships among them.
Sequence of Dirac Measures: A sequence of Dirac measures is a collection of probability measures concentrated at single points in a space, typically denoted as $\\delta_{x_n}$ for a sequence of points $\\{x_n\ ext{ }|\text{ } n \in \\mathbb{N}\ ext{ }\}$. This concept plays a crucial role in understanding weak convergence, as it allows the examination of the convergence behavior of measures by focusing on how these point masses behave in a limiting process.
Sequentiality: Sequentiality refers to the property of a sequence of elements in a topological space, where convergence of sequences is used to define various types of convergence. In functional analysis, it is crucial for understanding weak and weak* convergence, as it allows for the examination of limits and continuity through sequences rather than general nets or filters. This property helps in establishing the relationships between different types of convergence and their implications in functional spaces.
Sequentially weakly convergent: Sequentially weakly convergent refers to a sequence in a topological vector space that converges to a limit in the weak topology. This means that for every continuous linear functional, the sequence of functionals evaluated at the points of the sequence converges to the functional evaluated at the limit point. This concept is crucial in understanding the behavior of sequences in relation to weak convergence and weak* convergence, highlighting how convergence can differ in various topological contexts.
Strong convergence: Strong convergence refers to a type of convergence in a normed space where a sequence converges to a limit if the norm of the difference between the sequence elements and the limit approaches zero. This concept is crucial as it connects with various properties of spaces, operators, and convergence types, playing a significant role in understanding the behavior of sequences and their limits in mathematical analysis.
Topological Duality: Topological duality refers to the relationship between a topological vector space and its dual space, where the dual space consists of all continuous linear functionals defined on the original space. This concept emphasizes how weak and weak* convergence in the context of functional analysis can illustrate the connection between points in a space and their corresponding behavior under linear functionals. Essentially, understanding topological duality allows for a deeper insight into the structure of spaces and how functionals interact with them.
Topological Vector Space: A topological vector space is a vector space equipped with a topology that makes the vector operations of addition and scalar multiplication continuous. This structure allows for the analysis of convergence and continuity in the context of linear algebra, providing a framework for studying functions and sequences. The interaction between the topology and the vector space enables concepts like convergence to be examined in a more nuanced way, particularly with respect to functional analysis and dual spaces.
Weak Convergence: Weak convergence refers to a type of convergence in a topological vector space where a sequence converges to a limit if it converges with respect to every continuous linear functional. This concept is crucial for understanding the behavior of sequences in various mathematical structures, particularly in the context of functional analysis and applications in areas like differential equations and optimization.
Weak* convergence: Weak* convergence refers to the convergence of a sequence of functionals in the dual space of a Banach space, where a sequence of functionals converges weakly* to a functional if it converges pointwise on every element of the original space. This concept connects closely with the notion of reflexive spaces, as weak* convergence takes center stage in discussions about the properties and characterizations of such spaces.
Weakly compact: Weakly compact refers to a set in a topological vector space that is compact with respect to weak topology, meaning every net or sequence in the set has a convergent subnet or subsequence that converges to a point within the set. This concept is closely related to weak convergence, where sequences converge not necessarily in norm but in terms of their action on continuous linear functionals. Understanding weak compactness helps in analyzing functional spaces and the behavior of sequences and nets within those spaces.
σ-convergence: σ-convergence is a mode of convergence in the context of functional analysis where a sequence of measures converges to a measure in the weak topology, particularly in terms of integration with respect to continuous bounded functions. This type of convergence plays an essential role when analyzing spaces of measures and is closely related to weak and weak* convergence concepts.
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