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Banach Contraction Mapping Principle Oksana Bihun March 2, 2010 - - PowerPoint PPT Presentation
Banach Contraction Mapping Principle Oksana Bihun March 2, 2010 - - PowerPoint PPT Presentation
Banach Contraction Mapping Principle Oksana Bihun March 2, 2010 Department of Mathematics and Computer Science Concordia College, Moorhead, MN Stefan Banach Metric Spaces Banach Contraction Mapping Principle Applications to Ordinary
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Stefan Banach
Born: March 30, 1892, in Ostrowsko, nearby Krak´
- w
(now Poland). Never met his mother. Father Stefan Greczek arranged for his son to be brought up by Franciszka Plowa. Received early education from a French intellectual Juliusz Mien. Graduated from Henryk Sienkiewicz Gymnasium
- No. 4 in 1910 without distinction.
Felt that nothing new can be discovered in mathematics and chose to study engineering at the Lviv Polytechnic (1910-1916).
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Lviv Lvov Lw´
- w Lemberg Leopolis
Lviv was founded in 1256 by King Danylo Halytskiy of the Ruthenian principality of Halych-Volhynia, and named in honor of his son, Lev (Lion). Throughout history, Lviv belonged to Halych-Volynia, Lithuania, Poland, Austrian-Hungarian Empire, and Soviet
- Union. Now it belongs to Ukraine.
As
- f 1910 Lviv and Krak´
- w belonged
to Austro-Hungarian Empire.
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Banach and Steinhaus
In his memoirs, Hugo Steinhaus wrote: “During one such walk I
- verheard the words“Lebesgue measure”. I approached the park
bench and introduced myself to the two young apprentices of mathematics... From then on we would meet on a regular basis, and ... we decided to establish a mathematical society.” Banach solved a problem Steinhaus posed, since then they wrote many papers together. In 1920 Banach became an assistant to Lomnicki at Lviv Polytechnic. Banach’s thesis “On Operations
- n Abstract Sets and their
Application to Integral Equations” is sometimes said to mark the birth of Functional Analysis. In 1922 Banach was awarded habilitation by Lviv University.
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The Scottish Caf´ e in Lviv
The caf´ e was a meeting place for many mathematicians including Banach, Steinhaus, Ulam, Mazur, Kac, Schauder, Kaczmarz, and
- thers. Problems were written in a book kept by the landlord and
- ften prizes were offered for their solution. A collection of these
problems appeared later as the Scottish Book.
- R. D. Mauldin,
The Scottish Book, Mathematics from the Scottish Caf´ e (1981). In 1936 Mazur posed an approximation problem and offered live goose to the
- ne who solves it. The problem was
solved only in 1972 by a Swedish mathematician Per Enflo. He was awarded a live goose in Wroclaw.
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Metric Spaces
A metric space (X, d) is a set X together with a function d : X × X → R that satisfies the properties
- 1. d(x, y) ≥ 0 for all x, y ∈ X;
- 2. d(x, y) = 0 iff x = y;
- 3. d(x, y) ≤ d(x, z) + d(z, y) for all x, y, z ∈ X (triangle
inequality);
- 4. d(x, y) = d(y, x) for all x, y ∈ X.
The function d is called a metric on X. The metric on X gives a way to measure distance on X.
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Examples of Metric Spaces
- 1. (R2, d) with d(X, Y ) =
- (y1 − x1)2 + (y2 − x2)2 for
X = (x1, x2)T and Y = (y1, y2)T.
- 2. (S2, d), where S2 is the 2-sphere in R3 and for every pair of
points x, y on S2, d(x, y) is defined as the length of the shortest arc of the great circle that passes through x and y.
- 3. (C[0, 1], d), where C[0, 1] is the set of all continuous
functions on [0, 1] and for every pair of continuous functions f , g : [0, 1] → R the distance between them is defined to be d(f , g) = max
x∈[0,1] |f (x) − g(x)|.
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Convergent sequences and Cauchy sequences
Let (X, d) be a metric space. An ε-neighborhood of x ∈ X is the set Bε(x) = {y ∈ X : d(x, y) < ε}. Let {x1, x2, . . .} = {xn}∞
n=1 be a sequence on X.
The sequence {xn}∞
n=1 converges to x ∈ X, limn→∞ xn = x, if
every ε-neighborhood of x contains all but finitely many terms of {xn}∞
n=1.
The sequence {xn}∞
n=1 is a Cauchy sequence if for every ε > 0
there exists an index N such that the distance d(xn, xm) < ε as long as n, m > N. The metric space X is complete if every Cauchy sequence in X converges to an element of X.
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Banach Contraction Mapping Principle
Let (X, d) be a complete metric space. A map T : X → X is a contraction if there exists a nonnegative number ρ ≤ 1 such that d(T(x), T(y)) ≤ ρd(x, y). T is a strict contraction if ρ < 1. A point x ∈ X is called a fixed point of T if T(x) = x.
Theorem
Every strict contraction T on a complete metric space (X, d) has a unique fixed point x. Moreover, for every x0 ∈ X, x is the limit of the sequence {x0, T(x0), T(T(x0)), . . .}, which can be defined recursively: xn = T(xn−1) for all n = 1, 2, 3, . . . .
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Application to Ordinary Differential Equations
Consider the initial value problem ˙ x = f (x), x(0) = x0, (1) where f : R → R is a Lipschitz continuous function, i.e. there exists L > 0 such that |f (x) − f (y)| ≤ L|x − y| for all x, y ∈ R. We would like to prove the existence of a continuously differentiable function x : [0, δ] → R, where δ > 0, that solves the initial value problem (1). The latter problem is equivalent to the problem of finding a continuous function x : [0, δ] → R that solves the integral equation x(t) = x0 + t f (x(s)) ds.
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Existence of Solutions of ODEs
x(t) = x0 + t f (x(s)) ds. Let X = C[0, δ] with the same distance d as before. Define T : X → X by T(x)(t) = x0 + t
0 f (x(s)) ds for all
t ∈ [0, δ].
- Claim. T is a strict contraction provided δ > 0 is small enough.
|T(x)(t) − T(y)(t)| ≤ t |f (x(s)) − f (y(s))| ds ≤ L t |x(s) − y(s)| ds ≤ Lδ max
s∈[0,1] |x(s) − y(s)|.
Therefore, d(T(x), T(y)) ≤ Lδd(x, y) for all x, y ∈ X; T is a strict contraction provided δ < 1
L.
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Existence of Solutions of ODEs
Theorem
If f : R → R is Lipschitz continuous, then there exists δ > 0 and a unique continuously differentiable function x : [0, δ] → R that solves the initial value problem ˙ x = f (x), x(0) = x0, where x0 ∈ R.
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Applications to Numerical Solutions of Linear Systems
Consider the problem of solving a linear system Ax = b, where A is an n × n nonsingular matrix and b is an n-vector. Assume that the diagonal elements of A are equal to 1. Gauss elimination method produces large errors when used on a computer. Rewrite (I − A)x + b = x. Consider the (complete) metric space (Rn, d), where d(X, Y ) = max
1≤i≤n |xi − yi| for X = (x1, x2, . . . , xn)T and
Y = (y1, y2, . . . , yn)T.
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Applications to Numerical Solutions of Linear Systems
Define T(x) = (I − A)x + b for every x ∈ Rn. Because T(x) − T(y) = (I − A)(x − y). max
1≤i≤n |T(x)i − T(y)i|
≤ max
1≤i≤n n
- j=1
|(I − A)ij||xj − yj| = max
1≤i≤n n
- j=i,j=1
|Aij||xj − yj|. Thus, d(T(x), T(y)) ≤ max
1≤i≤n n
- j=i,j=1
|Aij| d(x, y).
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Applications to Numerical Solutions of Linear Systems
Theorem
Let A be a nonsingular n × n matrix and let b be an n-vector. If a nonsingular matrix A has diagonal dominance, that is,
n
- j=i,j=1
|Aij| ≤ |Aii| for all 1 ≤ i ≤ n, then for every x0 ∈ Rn the approximation scheme xn = (A − I)xn−1 + b, where n = 1, 2, . . ., converges to the solution of the linear system Ax = b.
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