Transcript slides pptx - Tennessee State University

Fundamentals from Real
Analysis
Ali Sekmen, Ph.D.2
Professor and Department Chair
Department of Computer Science
College of Engineering
Tennessee State University
1st Annual Workshop on Data Sciences
Outline
Spaces
Normed Vector Space
Banach Space
Inner Product Space
Hilbert Space
Metric Space
Topological Space
Subspaces and their Properties
Subspace Angles and Distances
Big Picture
Vector Spaces
Normed Vector Spaces
Inner-Product Vector Spaces
Hilbert Spaces
Banach Spaces
Big Picture
Vector Spaces
Topological Spaces
Normed Space
Inner Product
Space
Metric Spaces
What is a Vector Space?
A vector space is a set of objects that may
be added together or multiplied by
numbers (called scalars)
Scalars are typically real numbers
But can be complex numbers, rational numbers, or
generally any field
Vector addition and scalar multiplication must
satisfy certain requirements (called axioms)
What is a Vector Space?
A vector space may have additional
structures such as a norm or inner product
This is typical for infinite dimensional function
spaces whose vectors are functions
Many practical problems require ability to
decide whether a sequence of vectors
converges to a given vector
In order to allow proximity and continuity
considerations, most vector spaces are endowed
with a suitable topology
A topology is a structure that allows to define “being
close to each other”
Such topological vector spaces have richer theory
– Banach space topology is given by a norm
– Hilbert space topology is given by an inner product
What is a Vector Space?
Associativity
Commutativity
Identity Element
Inverse Element
Compatibility
Distributivity
Identity Element
Vector Spaces - Applications
The Fourier transform is widely used in
many areas of engineering and science
We can analyze a signal in the time
domain or in the frequency domain
We can show that
is a measure for the amount of the frequency s
What does this have to do with vector spaces?
Vector Spaces - Example
When we define the Fourier transform, we
need to also define when the transform is
well-defined
The Fourier transform is defined on a vector
space
For the sake of simplicity, we are not considering equivalence classes of functions that are the same almost everywhere
Vector Spaces - Example
What kind of functions have a Fourier
series
Periodic functions
Let us say
periodic functions
We can have Fourier series of functions that
belongs to the vector space
If the function does not belong to this space, then the Fourier
coefficients may not be well-defined
Basis
Every vector space has a basis
Every vector in a vector space can be
written in a unique way as a finite linear
combination of the elements in this basis
# of elements in a basis is dimension of
vector space
Basis
Vector Spaces - Example
All of us know a very well-known vector
space:
For general vector spaces, we need a
concept that corresponds to length in
We use “norm” instead of “length”
Norm
This concept is defined by mimicking what we know about
Euclidean Vector Norm
p-Norms
Surface of the diamond
of includes all the vectors
whose 1-norm is c
Surface of the sphere of
radius c includes all the
vectors whose 2-norm is c
Question
Are there any vector spaces for which we
cannot define any norms?
Every finite dimensional real or complex
topological vector space has a norm
There are infinite dimensional topological
vector spaces that do not have a norm that
induces the topology
Subspaces
Before we introduce some interesting
vector spaces, we will now introduce
“subspaces”
Subspaces
A line through
origin in
is a 1dimensional
subspace of
A plane through
origin in
is a 2dimensional
subspace of
Subspaces
Consider trigonometric polynomials, i.e., a
finite linear combination of exponential
functions
We can show that trigonometric
polynomials form a subspace of
Subspaces
Closed Subset
A Vector Space
A Vector Space
A Vector Space
A Set of Polynomials
Set of Polynomials
Set of Polynomials
Set of Polynomials
Banach Space
An important group of normed
vector spaces in which a
Cauchy sequence of vectors
converges to an element of
the space
Banach spaces play an
important role in functional analysis
In many areas of analysis, the spaces are
often Banach spaces
Convergence
Convergence
Convergence
Cauchy Sequences
Convergence and Cauchy
Banach Space
Banach Space
Inner Product for
Inner product is a very important
tool for analysis in
.
It is a measure of angle between
vectors
General Inner Product
Example Inner Product Spaces
Cauchy-Schwarz Inequality
Important
In any inner product vector space,
regardless of the inner product we can
always define a norm
But opposite is not true. We may not
always define an inner product from a
given norm
Important
We may define an inner product from a
given norm if the parallelogram law holds
for the norm
In this case, the induced inner product
from the norm is defined as
Hilbert Space
A Hilbert space is a vector space equipped
with an inner product such that when we
consider the space with the corresponding
induced norm, then that space is a Banach
space
Hilbert Space
Big Picture
Vector Spaces
Normed Vector Spaces
Inner-Product Vector Spaces
Hilbert Spaces
Banach Spaces
Metric Space
Topological Space
Inner Product Spaces
Angles
Normed Vector Spaces
Length
Not as strong as angles
Metric Spaces
Distance
Not as strong as length
Topological Spaces
What do we do if we do not have a notion of
distance between elements?
Nearness
Not as strong as distance
Via neighborhoods
Big Picture
Vector Spaces
Topological Spaces
Normed Space
Inner Product
Space
Metric Spaces
Subspaces (Revisited)
Sum of Subspaces
Union of Subspaces
More on Subspaces
More on Subspaces
Orthogonal Subspaces
Orthogonal Projection
Independent versus Disjoint
Two lines are Independent
Three lines are disjoint
Independent versus Disjoint
Minimal Angle
It is a good
measure for
complementary
subspaces
It is not a good measure
for non-complementary
subspaces
Gap between Subspaces
Maximum Angle
It is useful for subspaces
of equal dimension
Principle Angles
Manifold
A manifold is a mathematical space that
(on a sufficiently small scale) resembles to
the Euclidean space of a specific
dimension
2-dimensional
1-dimensional
Manifold
Manifolds are like curves and surfaces,
except that they might be of higher
dimension
Every manifold has a dimension
The number of independent parameters to
specify a point
n dimensional manifold is an object
modeled locally on
It takes exactly n numbers to specify a point
Some References
Functions, Spaces, and Expansions by
Ole Christensen
Topology by James Munkres
Topological Space
Topological Space
Metric Topology
Subspace Topology
Supremum
Supremum
We do not only talk about sets but we also
talk about functions
Consider a function
Range of the function
Supremum
Sometimes supremum is attained, i.e., we
have a maximum value of the function
Supremum
Supremum is attained
Supremum is not attained
A Vector Space
Linear Mapping
When we deal with finite dimensional
spaces, we simply use the ideas from
linear algebra
However, when we deal with infinite
dimensional spaces, we need to be careful
Linear Mapping
Linear Mapping
Linear Spaces
Affine Space