$$ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Self-defined math definitions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Math symbol commands \newcommand{\intd}{\,{\rm d}} % Symbol 'd' used in integration, such as 'dx' \newcommand{\diff}{{\rm d}} % Symbol 'd' used in differentiation \newcommand{\Diff}{{\rm D}} % Symbol 'D' used in differentiation \newcommand{\pdiff}{\partial} % Partial derivative \newcommand{DD}[2]{\frac{\diff}{\diff #2}\left( #1 \right)} \newcommand{Dd}[2]{\frac{\diff #1}{\diff #2}} \newcommand{PD}[2]{\frac{\pdiff}{\pdiff #2}\left( #1 \right)} \newcommand{Pd}[2]{\frac{\pdiff #1}{\pdiff #2}} \newcommand{\rme}{{\rm e}} % Exponential e \newcommand{\rmi}{{\rm i}} % Imaginary unit i \newcommand{\rmj}{{\rm j}} % Imaginary unit j \newcommand{\vect}[1]{\boldsymbol{#1}} % Vector typeset in bold and italic \newcommand{\phs}[1]{\dot{#1}} % Scalar phasor \newcommand{\phsvect}[1]{\boldsymbol{\dot{#1}}} % Vector phasor \newcommand{\normvect}{\vect{n}} % Normal vector: n \newcommand{\dform}[1]{\overset{\rightharpoonup}{\boldsymbol{#1}}} % Vector for differential form \newcommand{\cochain}[1]{\overset{\rightharpoonup}{#1}} % Vector for cochain \newcommand{\bigabs}[1]{\bigg\lvert#1\bigg\rvert} % Absolute value (single big vertical bar) \newcommand{\Abs}[1]{\big\lvert#1\big\rvert} % Absolute value (single big vertical bar) \newcommand{\abs}[1]{\lvert#1\rvert} % Absolute value (single vertical bar) \newcommand{\bignorm}[1]{\bigg\lVert#1\bigg\rVert} % Norm (double big vertical bar) \newcommand{\Norm}[1]{\big\lVert#1\big\rVert} % Norm (double big vertical bar) \newcommand{\norm}[1]{\lVert#1\rVert} % Norm (double vertical bar) \newcommand{\ouset}[3]{\overset{#3}{\underset{#2}{#1}}} % over and under set % Super/subscript for column index of a matrix, which is used in tensor analysis. \newcommand{\cscript}[1]{\;\; #1} % Star symbol used as prefix in front of a paragraph with no indent \newcommand{\prefstar}{\noindent$\ast$ } % Big vertical line restricting the function. % Example: $u(x)\restrict_{\Omega_0}$ \newcommand{\restrict}{\big\vert} % Math operators which are typeset in Roman font \DeclareMathOperator{\sgn}{sgn} % Sign function \DeclareMathOperator{\erf}{erf} % Error function \DeclareMathOperator{\Bd}{Bd} % Boundary of a set, used in topology \DeclareMathOperator{\Int}{Int} % Interior of a set, used in topology \DeclareMathOperator{\rank}{rank} % Rank of a matrix \DeclareMathOperator{\divergence}{div} % Curl \DeclareMathOperator{\curl}{curl} % Curl \DeclareMathOperator{\grad}{grad} % Gradient \DeclareMathOperator{\tr}{tr} % Trace \DeclareMathOperator{\span}{span} % Span $$

止于至善

As regards numerical analysis and mathematical electromagnetism

Comparison of several types of convergence

In functional analysis, several types of convergence are defined, namely,

  • strong convergence for elements in normed linear space.
  • weak convergence for elements in normed linear space, which is defined via the assistance of the dual space.
  • weak-* convergence for linear functionals in the strong dual space of a normed linear space.
  • pointwise convergence for linear operators.

This post summarizes their definitions and shows the differences.

  1. Definition (Strong convergence) Let \(X\) be a normed linear space and \((x_l)_{l \in \mathbb{N}}\) be a sequence in \(X\). Then \((x_l)_{l \in \mathbb{N}}\) converges (strongly) to \(x \in X\) if

    \[
    \lim_{l \rightarrow \infty} \norm{x_l - x}_X = 0.
    \]

    It can be seen that the strong convergence is just the convergence with respect to the “distance between points”, or more generally, the so-called “norm” defined for a linear space, which is what we have been familiar with in fundamental calculus.

  2. Definition (Weak convergence) Let \(X\) be a Banach space and \(X’\) be its dual space. The sequence \((x_l)_{l \in \mathbb{N}}\) in \(X\) converges weakly to \(x \in X\) if

    \[
    \lim_{l \rightarrow \infty} \abs{f(x_l) - f(x)} = 0 \quad (\forall f \in X’).
    \]

    We can see that the convergence here is called weak, because it is not directly based on point distance in the original space \(X\), but the evaluation of an arbitrary functional in the dual space on the sequence.

    It is easy and natural to see that the strong convergence implies weak convergence because of the continuity of the linear functional \(f \in X’\):

    \[
    \abs{f(x_l) - f(x)} = \abs{f(x_l - x)} \leq \norm{f}_{X’} \norm{x_l - x}_X.
    \]

  3. Definition (Pointwise convergence) Let \(X\) and \(Y\) be normed spaces. The sequence of bounded linear operators \((T_l)_{l \in \mathbb{N}} \subset L(X, Y)\) converges to \(T \in L(X, Y)\) if

    \[
    \lim_{l \rightarrow \infty} \norm{T_l x - T x}_Y = 0 \quad (\forall x \in X).
    \]

    The pointwise convergence is used to describe the convergence of operators at each point in \(X\). A more strict convergence for operators is uniform convergence, which means the convergence speeds of \((T_l x)_{l \in \mathbb{N}}\) at different points \(x\) in \(X\) are comparable. It is also easy to see that the strong convergence of \((T_l)_{l \in \mathbb{N}}\) implies pointwise convergence.

  4. Definition (Weak-* convergence) Let \(X_s’\) be the strong dual space of the normed linear space \(X\). The linear functional sequence \((T_l)_{l \in \mathbb{N}}\) converges to \(T\) in \(X_s’\) if

    \[
    \lim_{l \rightarrow \infty} \abs{T_l x - T x} = 0 \quad (\forall x \in X).
    \]

    The weak-* convergence can be considered as a special case of pointwise convergence with the difference that the linear operators become linear functionals and the dual space \(X’\) of \(X\) is assigned with the strong topology.

posted @ 2017-10-19 23:10  皮波迪博士  阅读(320)  评论(0编辑  收藏  举报