There is no one size for all students who are studying in one class. keep in mind that every student learning techniques are different from other while other are different. There some efficient methods for every students learn and improve their learning skills.
Remember that every technique you can learn from there you use any where. And important thing that what work for other student may not work for you.
If your a metric student then your learning mental level are different if your a student of intermediate class then are different. Rather if your in university classes then your mentality level different. Every thing you learn is totally depends on your mind and your practice skill.
GET ORGANISED
TALK TO YOUR TEACHER & ASK QUESTIONS
DON’T SKIP CLASS!
TAKE NOTES
SPACE OUT YOUR STUDYING
CREATE A STUDY PLAN – & STICK TO IT
DON’T JUST RE-READ BUT STUDY
SET UP A QUIET STUDY SPACE
TEST YOURSELF
FIND A STUDY BUDDY OR JOIN A STUDY GROUP
KEEP YOU MOTIVATED
HELP YOU SAY ACCOUNTABLE
QUIZ AND TEXT ON YOUR COURSE METRIALS
READ OVER YOUR WORK TO OFFER ADVICE
SHARE RESOURCES, SUCH AS TEXTBOOKS, LOWERING EXPENSES.
Why Study Important?
Study is not important for only you career building, its also play an important rule for your character building and improve your personal skills. A better study to improve your confidence, self estimate etc. Also reduce stress and anxiety about your career.
These days, mostly student by using social media like tiktok, facebook, Instagram are making too much money. All things are possible because of education. On the other hand social media destroying student moral values. By using social media students are well confident and self dependent. But mostly social media platform directly effecting on student learning and education.
Functional Analysis plays an important role in both applied science as well as in mathematics itself. As a result, it is frequently recommended to introduce the student to the area early in the study. It became a distinct area in the 20th century when it was discovered that many mathematical operations, from arithmetic to calculus procedures, share a number of highly similar characteristics.
Here we mainly cover the definition of Functional Analysis and Examples. Also, we discuss different branches of Functional Analysis like Normed spaces, Banach Space, Inner product space, Euclidean space, Uniform boundness theorem, Spectral theorem, Hahn–Banach theorem, Open mapping theorem, and Closed Graph Theorem.
Definitions:
Definitions; 1) “The branch of mathematics that deals with the theory of vector space and linear algebra” 2) “Functional analysis is a strategy for methodically examining links between problematic behavior and external events” 3) “Functional analysis is a technique for explaining how a complex system works”.
Originally derived from classical analysis, functional analysis is an abstract area of mathematics. Applications involving ordinary and partial differential equations, numerical analysis, calculus of variations, approximation theory, integral equations, and others provided the impetus for the development of the field.
Metric Space
Definition: A metric space is a pair of (X, d) where X is the set and ” d ” is metric on set X ( or distance function on X ), that is function defined on X x X such that for all x, y, z ∈ X.
Metric Space Axioms
d is a real-valued, finite and non-negative
d(x, y) = 0, if and only if x = y
d(x, y) = d(y, x)
d(x, y) ≤ d(x, z) + d(z, y)
Examples:
Real line R. This is the set of real numbers, taken with the usual metric defined by
d(x, y) = | x – y |
Euclidean Plane R2. The metric space R2 called the Euclidean plane is obtained if we take the set of ordered pairs of real numbers, written x = ( ξ1, ξ2 ) and y = ( η1, η2 ), etc.
Normed Space
Definition: A normed space X is a vector space with a norm defined on it.
A normed on a ( real or complex ) vector space X is a real-valued function on X whose value at an x ∈ X is denoted by
|| x ||
Normed Space Axioms
|| x || ≥ 0
|| x || = 0 ⇔ x = 0
|| αx || = | α | || x ||
|| x + y || ≤ || x || || y || ( Triangular inequality )
Here x and y are arbitrary vectors in X and α is any scalar.
A norm on X defines a metric d on X which is given by
d (x, y ) = || x – y ||
and is called the metric induced by the norm. The normed just defined is denoted by ( X, ||.|| ) or simply by X.
Examples:
n-dimensional Euclidean space is a normed space.
Banach Space
Definition: A Banach space is a completely normed space ( X, ||.|| )
If the norm-induced metric ” d ” is a complete metric, then the normed space (X, ||.||) is a Banach space. If (X, ||.||) is a Banach space, the nome ||.|| of a normed space (X, ||.||) is referred to as the complete norm.
Examples
Space l∞ is a Banach space with the norm given by
||x|| = sup j | ξj |
Space C[a, b] is a Banach space with the norm given by
||x|| = max t ∈ j | x(t) |,
where j = [a, b].
Inner Product Space
Definition: An inner product space ( or pre-Hilbert space ) is a vector space X with an inner product defined on X.
Inner Product Space Axioms:
〈 x + y, z 〉 = 〈 x, z 〉 + 〈 y, z 〉.
〈 αx, y 〉 = α 〈 x, y 〉.
〈 x, x 〉 ≥ 0
〈 x, x 〉 = 0 ⇔ x = 0
An inner product on X defines a norm on X given by
|| x || = √〈x, x 〉
Hilbert Space
Definition: A Hilbert space is a complete inner product space.
The addition of a certain distance function provides the final structure that completes it.
d(x, y) = || x – y || = √〈x -y, x – y〉
Point To Be Noted:
Inner product spaces are normed spaces, and Hilbert spaces are Banach spaces
Examples:
Euclidean Space Rn. The space Rn is a Hilbert space with the inner product defined by
〈x, y 〉 = ξ1 η1 + ξ2 η2 + … + ξn ηn
Where x = ( ξj ) = ( ξ1, … ξn ) and y = ( ηj ) = ( η1, …. ηn )
Unitary Space Cn. The space Cn is a Hilbert space with the inner product given by
〈x, y 〉 = ξ1 η1– +… + ξn ηn–
Uniform Boundness Theorem
Statement: Let ” X ” be Banach space and ” Y ” be a norm linearly space and ” M ” be a subset of B (X, Y) such that
〈Tx: T ∈ M 〉 is a bounded subset of ” Y ” ∀ x ∈ X then ” M ” is a bounded subset of B(X, Y). i.e if ” M ” is pointwise bounded then it is uniformly bounded.
Spectral Theorem
Statement: Let ” T ” be an operator on a finite-dimensional complete Hilbert space ” H “. Let λ1, λ2, … λm be distinct eigenvalues of ” T “. Let P1, P2, … Pm be corresponding projections on subspaces M1, M2, … Mm respectively. The following statements are equivalent
The Mi‘s are piecewise orthogonal and spans ” H “.
Statement: Let X be a real vector space and p a sublinear functional on X. Furthermore, let f be a linear function that is defined on a subspace Z of X and satisfies
f(x) ≤ p(x) ∀ x ∈ Z → ( A )
Then f has a linear extension of f– from Z to X satisfying
f– (x) ≤ p(x) ∀ x ∈ Z
that is, f– is a linear functional on X, satisfies ( A ) on X f– (x) = f(x) for every x ∈ X.
Open Mapping Theorem
Statement: Let X and Y be Banach spaces and let
T: X → Y
be a continuous ( bounded ) linear transformation from X to Y. Then T is open mapping.
i. e For all open subsets A of X, T(A) is an open subset of Y.
Closed Graph Theorem
Statement: Let X and Y be Banach spaces and
T: D( T ) → Y a closed linear operator, Where D ( T ) ⊂ X. Then if D ( T ) is closed in X, the operator T is bounded.
〈 x, x 〉 ≥ 0