SlideShare a Scribd company logo
International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 5 Issue 1, November-December 2020 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD37914 | Volume – 5 | Issue – 1 | November-December 2020 Page 354
A Study of Closure of An Operator
Praveen Sharma
Student, Department of Mathematics, University of Delhi, New Delhi, India
ABSTRACT
In this paper first we generalizes the Hilbert-Adjoint of a linear operator and
showed that it is always closed for any linear operator with the condition that
the domain of the operator is dense.
We also proved that "Let J be a closed operator defined in H with dense
domain then D(J*) is dense and J** = J . "
We also proves Closed graph theorem for complex Hilbert spaces as a
corollary of our results.
Keywords: Separable, Closable, Closure, Operator extension.
How to cite this paper: Praveen Sharma
"A Study of Closure of An Operator"
Published in
International Journal
of Trend in Scientific
Research and
Development(ijtsrd),
ISSN: 2456-6470,
Volume-5 | Issue-1,
December 2020,
pp.347-353, URL:
www.ijtsrd.com/papers/ijtsrd37914.pdf
Copyright © 2020 by author(s) and
International JournalofTrendinScientific
Research and
DevelopmentJournal.
This is an Open
Access article distributedundertheterms
of the Creative Commons Attribution
License (CC BY 4.0)
(http://creativecommons.org/licenses/by/4.0)
INTRODUCTION
In this Paper we take H as the complexHilbertspaceandD(J)
denotes the domain of a linear operator J.
In section 1 we generalizes the Hilbert-Adjointforanylinear
operator with dense domain in H and prove
"If J is defined everywhere on H. Then its Hilbert-adjoint is
bounded."
In section 2 we first define closable operatorsandshowwith
the help of an example that every operator neednotclosable
and if it is closable then graph of its closure is equals to the
closure of its graph.
In Section 3 we proved our result
"Let J be a closed operator defined in H with dense domain
then D(J*) is dense and J** = J."
As a result of which Closed graph Theorem for Complex
Hilbert Spaces comes out as Corollary.
1 Hilbert-Adjoint
1.1 Definition: Let J : H → H be a bounded operator then its
Hilbert adjoint always exists [2] and is also a bounded linear
operator J∗ defined everywhere on H such that
(Jφ,ψ) = (φ,J∗ψ) ∀ φ,ψ ∈ H
or
(Jφ,ψ) = (φ,η) and J∗ψ = η (1)
1.2 Remark: We can use (1) to generalizes the Hilbert
Adjoint of any operator.
1.3 Lemma: Let J : D(J) → H be any operator then J∗ψ = η in
(1) is unique iff
D(J) = H. (i.e the domain of J is dense in H.)
Proof: Let J∗ψ = η in (1) is unique. Suppose D (J) H
⇒ ∃ 0 6= µ ∈ H such that
From (1) we have
(Jφ,ψ) = (φ,η) + 0
= (φ,η) + (φ,µ)
= (φ,η + µ) ∀ φ ∈ D(J)
Which contradicts the uniqueness of J : D(J) → H. Hence D(J)
= H.
Conversely
Let the domain of J is dense in H. Then
Hence unique.
1.4 Remark: Lemma 1.3 shows that Hilbert-Adjoint of any
linear operator J exists iff the domain of Jisdense.Nowusing
this Lemma we generalizes the definition of Hilbert-Adjoint
for any linear operator.
IJTSRD37914
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD37914 | Volume – 5 | Issue – 1 | November-December 2020 Page 355
1.5 Definition Let J : D(J) → H be any linear operator whose
domain is dense the its Hilbert-Adjoint exists and is a linear
operator J∗ : D(J∗) → H such that D(J∗) contains all ψ ∈ H such
that ∃ η with (Jφ,ψ) = (φ,η) ∀ φ ∈ D(T) and J∗ψ = η
Proposition 1.6: LetJ bean operatordefinedeverywhereon
H.Then J∗ is bounded.
Proof: Suppose J∗ is unbounded. Then ∃ a sequence(vn)each
of norm 1 and || vn|| → ∞ as n → ∞ For each n define a
functional Pn on H by Pn(φ) = (Jφ,vn) = (φ,J∗vn) By Schwarz
Lemma [2] we have
|Pn(φ)| = |(φ,J∗vn)|
≤ ||φ||||J∗vn|| ∀ φ ∈ H
∴ Pn is bounded for all n. For each φ ∈ H again by Schwarz
Lemma we have
|Pn(φ)| = |(Jφ,vn)|
≤ ||Jφ|| ∀ n
Then By Uniform Boundness Theorem [1] the sequence
||(Pn)|| is bounded.
⇒ ∃ K > 0 such that
||Pn|| ≤ K ∀ n
⇒ |(J∗vn,J∗vn)| = |Pn(J∗vn)|
≤ ||Pn||||J∗vn||
≤ K||J∗vn||
⇒ ||J∗vn|| ≤ K ∀ n
⇒ the sequence (||J∗vn||) is boundedwhichisacontradiction.
Hence J∗ is bounded.
2. Closed Linear Extension:
2.1 Definition(Closed operator): An Operator J : D(J)
→ H is said to be closed if its graph υ(J) = {< φ,Jφ >: φ ∈ D(J)}
is closed in H × H, where the inner product on H × H is given
by
(< φ,ψ >,< η,µ >) = (φ,η) + (ψ,µ)
Notation J˜ is said to be an extension of J if
D(J) ⊂ D(J˜) and
or
J˜|D(J) = J
υ(J) ⊂ υ(J˜)
denoted by J ⊂ J˜
2.2 Definition (Closable): A linear operator J is said to
be closable if J has an extension which is closed.
If J is closable then J has a minimal closed extension called
closure denoted by J.[1] 2.3 Proposition: if J be any closable
operator then
υ(J) = υ(J)
Proof: Let J be any closable operator ⇒ J exists.
Let L be any closed extension of J
⇒ υ(J) ⊂ υ(L) (∵ Lis closed)
Define an operator P with domain
D(P) = {µ :< µ,η >∈ υ(J)}
and
Pµ = η
Claim: P is well defined
Let
Pµ = η1 and Pµ = η2
Hence well defined. Also
As L is arbitrary. Therefore P is a minimal closed extension
of J
⇒ P = J. Hence the result.
2.4 Remark A natural question ariseshere thatifwe wantto
take the closure of any linear operator J then we can takethe
closure of υ(J) in H×H then just find a operator
whose graph is equals to υ(J).But this is not always possible
we constructed an Example 2.5 below.
2.5 Example: Let H be any separable Hilbert Space with
countable orthonormal basis {ηk}. Let e be an element of H
which is not a finite linear combination of {ηk}. Let
e
Define an operator J : D(J) → H, where
D(J) = set of all the finite linear combination of {ηk}andeand
N
J(ae + Xciηk) = ae
i=1
Clearly J is linear
Claim:< e,e >,< e,0 > ∈ υ(J)
As
e ∈ D(J) and Je = e
Also, for each N define
N
ψN = Xcknηkn ∈ D(J) and JψN = 0 ∀ N
k=1
By (2))
Suppose ∃ an operator L whose graph is υ(J)
⇒ Le = e and Le = 0. Which contradicts the uniqueness.
Hence there is no operator whose graph is υ(J)
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD37914 | Volume – 5 | Issue – 1 | November-December 2020 Page 356
3. Main Results
Theorem 3.1 Let X : H × H → H × H be an operator given by
X < η,µ >=< µ,−η >
. Then for any linear operator J defined in H with dense
domain we have
υ(J∗) = [X(υ(J))]⊥
Moreover J∗ is closed
Proof: Clearly, X is linear.
Let < η,µ >∈ H × H Then ∃ < −µ,η >∈ H × H such that
X < −µ,η >=< η,µ >
∴ X is surjective Also
||X < η,µ > ||2 = || < µ,−η > ||2
= ||µ||2 + ||η||2
= || < η,µ > ||2
∴ X is a surjective isometry hence unitary
Claim: υ(J∗) = [X(υ(J))]⊥
Let
< η,µ >∈ [X(υ(J))]⊥ ⇔ (< η,µ >,X < φ,Jφ >) = 0 ∀ φ ∈ D(J)
⇔ (< η,µ >,< Jφ,−φ >) = 0 ∀ φ ∈ D(J)
⇔ < η,Jφ >=< µ,φ > ∀ φ ∈ D(J)
⇔ < Jφ,η >=< φ,µ > ∀ φ ∈ D(J)
⇔ η ∈ D(J∗),J∗η = µ
⇔ < η,µ >∈ υ(J∗)
As the graph is closed. Hence J∗ is closed
Theorem 3.2 Let J be a closed operator defined in H with
dense domain then D(J∗) is dense and J∗∗ = J
Proof: As
= Identity)
= [X(Xυ(J)⊥)]⊥ (∵ X is unitary)
= [Xυ(J∗)]⊥ (∵ By Theorem 3.1) (3)
As J is closed
Suppose D(J∗) is not dense. Then ∃ 0 6= η such that
As η 6= 0 then there is no linear operator with graph υ(J).
which is a contradiction to J is closed.
∴ D(J∗) is dense ⇒ J∗∗ exists. Then from Theorem 3.1 and (3)
we have
υ(J∗∗) = [Xυ(J∗)]⊥ = υ(J)
⇒ J = J∗∗
Corollary 3.3:(Closed graph Theorem) AclosedoperatorJ
defined everywhere on H then J is bounded.
Proof: As J is a linear operator defined everywhere on H.
Then J∗ exists and By Proposition 1.6 it is also bounded.
Also By Theorem 3.1 we have J∗ is closed ⇒ D(J∗) is closed
[2]. By Theorem 3.2 we have
and = J
⇒ J∗ is defined everywhere on H.
Then again By Proposition 1.6 we have J∗∗ is bounded
Hence J is bounded
REFERENCE
[1] Reed, Michael, and Barry Simon. I: Functional
analysis. Vol. 1. Academic press, 1980.
[2] Kreyszig, Erwin.Introductoryfunctionalanalysiswith
applications. Vol. 1. New York: wiley, 1978.
[3] Rudin, Walter. Principles of mathematical analysis.
Vol. 3. New York: McGraw-hill, 1964.
[4] Jacobson Nathan, Lie algebras. Interscience
Publishers,New York (1979).

More Related Content

What's hot

On generalized dislocated quasi metrics
On generalized dislocated quasi metricsOn generalized dislocated quasi metrics
On generalized dislocated quasi metrics
Alexander Decker
 
11.on generalized dislocated quasi metrics
11.on generalized dislocated quasi metrics11.on generalized dislocated quasi metrics
11.on generalized dislocated quasi metrics
Alexander Decker
 
Fuzzy portfolio optimization_Yuxiang Ou
Fuzzy portfolio optimization_Yuxiang OuFuzzy portfolio optimization_Yuxiang Ou
Fuzzy portfolio optimization_Yuxiang Ou
Yuxiang Ou
 

What's hot (16)

Chapter0 reviewofalgebra-151003150137-lva1-app6891
Chapter0 reviewofalgebra-151003150137-lva1-app6891Chapter0 reviewofalgebra-151003150137-lva1-app6891
Chapter0 reviewofalgebra-151003150137-lva1-app6891
 
Stability criterion of periodic oscillations in a (15)
Stability criterion of periodic oscillations in a (15)Stability criterion of periodic oscillations in a (15)
Stability criterion of periodic oscillations in a (15)
 
On semi symmetric projective connection
On semi   symmetric projective connectionOn semi   symmetric projective connection
On semi symmetric projective connection
 
Numerical solutions for linear fredholm integro differential
 Numerical solutions for linear fredholm integro differential  Numerical solutions for linear fredholm integro differential
Numerical solutions for linear fredholm integro differential
 
IRJET- On Semigroup and its Connections with Lattices
IRJET- On Semigroup and its Connections with LatticesIRJET- On Semigroup and its Connections with Lattices
IRJET- On Semigroup and its Connections with Lattices
 
On generalized dislocated quasi metrics
On generalized dislocated quasi metricsOn generalized dislocated quasi metrics
On generalized dislocated quasi metrics
 
11.on generalized dislocated quasi metrics
11.on generalized dislocated quasi metrics11.on generalized dislocated quasi metrics
11.on generalized dislocated quasi metrics
 
On best one sided approximation by multivariate lagrange
      On best one sided approximation by multivariate lagrange      On best one sided approximation by multivariate lagrange
On best one sided approximation by multivariate lagrange
 
Global Domination Set in Intuitionistic Fuzzy Graph
Global Domination Set in Intuitionistic Fuzzy GraphGlobal Domination Set in Intuitionistic Fuzzy Graph
Global Domination Set in Intuitionistic Fuzzy Graph
 
Dual Gravitons in AdS4/CFT3 and the Holographic Cotton Tensor
Dual Gravitons in AdS4/CFT3 and the Holographic Cotton TensorDual Gravitons in AdS4/CFT3 and the Holographic Cotton Tensor
Dual Gravitons in AdS4/CFT3 and the Holographic Cotton Tensor
 
Instantons and Chern-Simons Terms in AdS4/CFT3
Instantons and Chern-Simons Terms in AdS4/CFT3Instantons and Chern-Simons Terms in AdS4/CFT3
Instantons and Chern-Simons Terms in AdS4/CFT3
 
Application of Bayesian and Sparse Network Models for Assessing Linkage Diseq...
Application of Bayesian and Sparse Network Models for Assessing Linkage Diseq...Application of Bayesian and Sparse Network Models for Assessing Linkage Diseq...
Application of Bayesian and Sparse Network Models for Assessing Linkage Diseq...
 
Fuzzy portfolio optimization_Yuxiang Ou
Fuzzy portfolio optimization_Yuxiang OuFuzzy portfolio optimization_Yuxiang Ou
Fuzzy portfolio optimization_Yuxiang Ou
 
Modified Method for Fixed Charge Transportation Problem
Modified Method for Fixed Charge Transportation ProblemModified Method for Fixed Charge Transportation Problem
Modified Method for Fixed Charge Transportation Problem
 
IRJET- W-R0 Space in Minimal G-Closed Sets of Type1
IRJET- W-R0 Space in Minimal G-Closed Sets of Type1IRJET- W-R0 Space in Minimal G-Closed Sets of Type1
IRJET- W-R0 Space in Minimal G-Closed Sets of Type1
 
QMC: Operator Splitting Workshop, Structured Composite Problems in Direct Hil...
QMC: Operator Splitting Workshop, Structured Composite Problems in Direct Hil...QMC: Operator Splitting Workshop, Structured Composite Problems in Direct Hil...
QMC: Operator Splitting Workshop, Structured Composite Problems in Direct Hil...
 

Similar to A Study of Closure of An Operator

Notes up to_ch7_sec3
Notes up to_ch7_sec3Notes up to_ch7_sec3
Notes up to_ch7_sec3
neenos
 
GradStudentSeminarSept30
GradStudentSeminarSept30GradStudentSeminarSept30
GradStudentSeminarSept30
Ryan White
 
l1-Embeddings and Algorithmic Applications
l1-Embeddings and Algorithmic Applicationsl1-Embeddings and Algorithmic Applications
l1-Embeddings and Algorithmic Applications
Grigory Yaroslavtsev
 
Formulas for Surface Weighted Numbers on Graph
Formulas for Surface Weighted Numbers on GraphFormulas for Surface Weighted Numbers on Graph
Formulas for Surface Weighted Numbers on Graph
ijtsrd
 

Similar to A Study of Closure of An Operator (20)

Asymptotic Analysis
Asymptotic  AnalysisAsymptotic  Analysis
Asymptotic Analysis
 
IRJET- On Distributive Meet-Semilattices
IRJET- On Distributive Meet-SemilatticesIRJET- On Distributive Meet-Semilattices
IRJET- On Distributive Meet-Semilattices
 
Stochastic calculus
Stochastic calculus Stochastic calculus
Stochastic calculus
 
Fixed point result in menger space with ea property
Fixed point result in menger space with ea propertyFixed point result in menger space with ea property
Fixed point result in menger space with ea property
 
Notes up to_ch7_sec3
Notes up to_ch7_sec3Notes up to_ch7_sec3
Notes up to_ch7_sec3
 
Notes up to_ch7_sec3
Notes up to_ch7_sec3Notes up to_ch7_sec3
Notes up to_ch7_sec3
 
Talk5
Talk5Talk5
Talk5
 
Characterizations of Prime and Minimal Prime Ideals of Hyperlattices
Characterizations of Prime and Minimal Prime Ideals of HyperlatticesCharacterizations of Prime and Minimal Prime Ideals of Hyperlattices
Characterizations of Prime and Minimal Prime Ideals of Hyperlattices
 
INTUITIONISTIC S-FUZZY SOFT NORMAL SUBGROUPS
INTUITIONISTIC S-FUZZY SOFT NORMAL SUBGROUPSINTUITIONISTIC S-FUZZY SOFT NORMAL SUBGROUPS
INTUITIONISTIC S-FUZZY SOFT NORMAL SUBGROUPS
 
Green’s Function Solution of Non-homogenous Singular Sturm-Liouville Problem
Green’s Function Solution of Non-homogenous Singular Sturm-Liouville ProblemGreen’s Function Solution of Non-homogenous Singular Sturm-Liouville Problem
Green’s Function Solution of Non-homogenous Singular Sturm-Liouville Problem
 
GradStudentSeminarSept30
GradStudentSeminarSept30GradStudentSeminarSept30
GradStudentSeminarSept30
 
My PhD Presentation 9-Apr-2008
My PhD Presentation 9-Apr-2008My PhD Presentation 9-Apr-2008
My PhD Presentation 9-Apr-2008
 
VECTOR CALCULUS
VECTOR CALCULUS VECTOR CALCULUS
VECTOR CALCULUS
 
IRJET-Entire Domination in Jump Graphs
IRJET-Entire Domination in Jump GraphsIRJET-Entire Domination in Jump Graphs
IRJET-Entire Domination in Jump Graphs
 
l1-Embeddings and Algorithmic Applications
l1-Embeddings and Algorithmic Applicationsl1-Embeddings and Algorithmic Applications
l1-Embeddings and Algorithmic Applications
 
A Generalization of the Chow-Liu Algorithm and its Applications to Artificial...
A Generalization of the Chow-Liu Algorithm and its Applications to Artificial...A Generalization of the Chow-Liu Algorithm and its Applications to Artificial...
A Generalization of the Chow-Liu Algorithm and its Applications to Artificial...
 
Formulas for Surface Weighted Numbers on Graph
Formulas for Surface Weighted Numbers on GraphFormulas for Surface Weighted Numbers on Graph
Formulas for Surface Weighted Numbers on Graph
 
A STUDY ON L-FUZZY NORMAL SUBl -GROUP
A STUDY ON L-FUZZY NORMAL SUBl -GROUPA STUDY ON L-FUZZY NORMAL SUBl -GROUP
A STUDY ON L-FUZZY NORMAL SUBl -GROUP
 
Learning to Reconstruct at Stanford
Learning to Reconstruct at StanfordLearning to Reconstruct at Stanford
Learning to Reconstruct at Stanford
 
μ-RANGE, λ-RANGE OF OPERATORS ON A HILBERT SPACE
μ-RANGE, λ-RANGE OF OPERATORS ON A HILBERT SPACEμ-RANGE, λ-RANGE OF OPERATORS ON A HILBERT SPACE
μ-RANGE, λ-RANGE OF OPERATORS ON A HILBERT SPACE
 

More from ijtsrd

‘Six Sigma Technique’ A Journey Through its Implementation
‘Six Sigma Technique’ A Journey Through its Implementation‘Six Sigma Technique’ A Journey Through its Implementation
‘Six Sigma Technique’ A Journey Through its Implementation
ijtsrd
 
Dynamics of Communal Politics in 21st Century India Challenges and Prospects
Dynamics of Communal Politics in 21st Century India Challenges and ProspectsDynamics of Communal Politics in 21st Century India Challenges and Prospects
Dynamics of Communal Politics in 21st Century India Challenges and Prospects
ijtsrd
 
Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in...
Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in...Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in...
Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in...
ijtsrd
 
The Impact of Digital Media on the Decentralization of Power and the Erosion ...
The Impact of Digital Media on the Decentralization of Power and the Erosion ...The Impact of Digital Media on the Decentralization of Power and the Erosion ...
The Impact of Digital Media on the Decentralization of Power and the Erosion ...
ijtsrd
 
Problems and Challenges of Agro Entreprenurship A Study
Problems and Challenges of Agro Entreprenurship A StudyProblems and Challenges of Agro Entreprenurship A Study
Problems and Challenges of Agro Entreprenurship A Study
ijtsrd
 
Comparative Analysis of Total Corporate Disclosure of Selected IT Companies o...
Comparative Analysis of Total Corporate Disclosure of Selected IT Companies o...Comparative Analysis of Total Corporate Disclosure of Selected IT Companies o...
Comparative Analysis of Total Corporate Disclosure of Selected IT Companies o...
ijtsrd
 
A Study on the Effective Teaching Learning Process in English Curriculum at t...
A Study on the Effective Teaching Learning Process in English Curriculum at t...A Study on the Effective Teaching Learning Process in English Curriculum at t...
A Study on the Effective Teaching Learning Process in English Curriculum at t...
ijtsrd
 
The Role of Mentoring and Its Influence on the Effectiveness of the Teaching ...
The Role of Mentoring and Its Influence on the Effectiveness of the Teaching ...The Role of Mentoring and Its Influence on the Effectiveness of the Teaching ...
The Role of Mentoring and Its Influence on the Effectiveness of the Teaching ...
ijtsrd
 
Design Simulation and Hardware Construction of an Arduino Microcontroller Bas...
Design Simulation and Hardware Construction of an Arduino Microcontroller Bas...Design Simulation and Hardware Construction of an Arduino Microcontroller Bas...
Design Simulation and Hardware Construction of an Arduino Microcontroller Bas...
ijtsrd
 
Sustainable Energy by Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadiku
Sustainable Energy by Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. SadikuSustainable Energy by Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadiku
Sustainable Energy by Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadiku
ijtsrd
 
Concepts for Sudan Survey Act Implementations Executive Regulations and Stand...
Concepts for Sudan Survey Act Implementations Executive Regulations and Stand...Concepts for Sudan Survey Act Implementations Executive Regulations and Stand...
Concepts for Sudan Survey Act Implementations Executive Regulations and Stand...
ijtsrd
 
Towards the Implementation of the Sudan Interpolated Geoid Model Khartoum Sta...
Towards the Implementation of the Sudan Interpolated Geoid Model Khartoum Sta...Towards the Implementation of the Sudan Interpolated Geoid Model Khartoum Sta...
Towards the Implementation of the Sudan Interpolated Geoid Model Khartoum Sta...
ijtsrd
 
Activating Geospatial Information for Sudans Sustainable Investment Map
Activating Geospatial Information for Sudans Sustainable Investment MapActivating Geospatial Information for Sudans Sustainable Investment Map
Activating Geospatial Information for Sudans Sustainable Investment Map
ijtsrd
 
Educational Unity Embracing Diversity for a Stronger Society
Educational Unity Embracing Diversity for a Stronger SocietyEducational Unity Embracing Diversity for a Stronger Society
Educational Unity Embracing Diversity for a Stronger Society
ijtsrd
 
DeepMask Transforming Face Mask Identification for Better Pandemic Control in...
DeepMask Transforming Face Mask Identification for Better Pandemic Control in...DeepMask Transforming Face Mask Identification for Better Pandemic Control in...
DeepMask Transforming Face Mask Identification for Better Pandemic Control in...
ijtsrd
 

More from ijtsrd (20)

‘Six Sigma Technique’ A Journey Through its Implementation
‘Six Sigma Technique’ A Journey Through its Implementation‘Six Sigma Technique’ A Journey Through its Implementation
‘Six Sigma Technique’ A Journey Through its Implementation
 
Edge Computing in Space Enhancing Data Processing and Communication for Space...
Edge Computing in Space Enhancing Data Processing and Communication for Space...Edge Computing in Space Enhancing Data Processing and Communication for Space...
Edge Computing in Space Enhancing Data Processing and Communication for Space...
 
Dynamics of Communal Politics in 21st Century India Challenges and Prospects
Dynamics of Communal Politics in 21st Century India Challenges and ProspectsDynamics of Communal Politics in 21st Century India Challenges and Prospects
Dynamics of Communal Politics in 21st Century India Challenges and Prospects
 
Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in...
Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in...Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in...
Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in...
 
The Impact of Digital Media on the Decentralization of Power and the Erosion ...
The Impact of Digital Media on the Decentralization of Power and the Erosion ...The Impact of Digital Media on the Decentralization of Power and the Erosion ...
The Impact of Digital Media on the Decentralization of Power and the Erosion ...
 
Online Voices, Offline Impact Ambedkars Ideals and Socio Political Inclusion ...
Online Voices, Offline Impact Ambedkars Ideals and Socio Political Inclusion ...Online Voices, Offline Impact Ambedkars Ideals and Socio Political Inclusion ...
Online Voices, Offline Impact Ambedkars Ideals and Socio Political Inclusion ...
 
Problems and Challenges of Agro Entreprenurship A Study
Problems and Challenges of Agro Entreprenurship A StudyProblems and Challenges of Agro Entreprenurship A Study
Problems and Challenges of Agro Entreprenurship A Study
 
Comparative Analysis of Total Corporate Disclosure of Selected IT Companies o...
Comparative Analysis of Total Corporate Disclosure of Selected IT Companies o...Comparative Analysis of Total Corporate Disclosure of Selected IT Companies o...
Comparative Analysis of Total Corporate Disclosure of Selected IT Companies o...
 
The Impact of Educational Background and Professional Training on Human Right...
The Impact of Educational Background and Professional Training on Human Right...The Impact of Educational Background and Professional Training on Human Right...
The Impact of Educational Background and Professional Training on Human Right...
 
A Study on the Effective Teaching Learning Process in English Curriculum at t...
A Study on the Effective Teaching Learning Process in English Curriculum at t...A Study on the Effective Teaching Learning Process in English Curriculum at t...
A Study on the Effective Teaching Learning Process in English Curriculum at t...
 
The Role of Mentoring and Its Influence on the Effectiveness of the Teaching ...
The Role of Mentoring and Its Influence on the Effectiveness of the Teaching ...The Role of Mentoring and Its Influence on the Effectiveness of the Teaching ...
The Role of Mentoring and Its Influence on the Effectiveness of the Teaching ...
 
Design Simulation and Hardware Construction of an Arduino Microcontroller Bas...
Design Simulation and Hardware Construction of an Arduino Microcontroller Bas...Design Simulation and Hardware Construction of an Arduino Microcontroller Bas...
Design Simulation and Hardware Construction of an Arduino Microcontroller Bas...
 
Sustainable Energy by Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadiku
Sustainable Energy by Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. SadikuSustainable Energy by Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadiku
Sustainable Energy by Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadiku
 
Concepts for Sudan Survey Act Implementations Executive Regulations and Stand...
Concepts for Sudan Survey Act Implementations Executive Regulations and Stand...Concepts for Sudan Survey Act Implementations Executive Regulations and Stand...
Concepts for Sudan Survey Act Implementations Executive Regulations and Stand...
 
Towards the Implementation of the Sudan Interpolated Geoid Model Khartoum Sta...
Towards the Implementation of the Sudan Interpolated Geoid Model Khartoum Sta...Towards the Implementation of the Sudan Interpolated Geoid Model Khartoum Sta...
Towards the Implementation of the Sudan Interpolated Geoid Model Khartoum Sta...
 
Activating Geospatial Information for Sudans Sustainable Investment Map
Activating Geospatial Information for Sudans Sustainable Investment MapActivating Geospatial Information for Sudans Sustainable Investment Map
Activating Geospatial Information for Sudans Sustainable Investment Map
 
Educational Unity Embracing Diversity for a Stronger Society
Educational Unity Embracing Diversity for a Stronger SocietyEducational Unity Embracing Diversity for a Stronger Society
Educational Unity Embracing Diversity for a Stronger Society
 
Integration of Indian Indigenous Knowledge System in Management Prospects and...
Integration of Indian Indigenous Knowledge System in Management Prospects and...Integration of Indian Indigenous Knowledge System in Management Prospects and...
Integration of Indian Indigenous Knowledge System in Management Prospects and...
 
DeepMask Transforming Face Mask Identification for Better Pandemic Control in...
DeepMask Transforming Face Mask Identification for Better Pandemic Control in...DeepMask Transforming Face Mask Identification for Better Pandemic Control in...
DeepMask Transforming Face Mask Identification for Better Pandemic Control in...
 
Streamlining Data Collection eCRF Design and Machine Learning
Streamlining Data Collection eCRF Design and Machine LearningStreamlining Data Collection eCRF Design and Machine Learning
Streamlining Data Collection eCRF Design and Machine Learning
 

Recently uploaded

IATP How-to Foreign Travel May 2024.pdff
IATP How-to Foreign Travel May 2024.pdffIATP How-to Foreign Travel May 2024.pdff
IATP How-to Foreign Travel May 2024.pdff
17thcssbs2
 
The basics of sentences session 4pptx.pptx
The basics of sentences session 4pptx.pptxThe basics of sentences session 4pptx.pptx
The basics of sentences session 4pptx.pptx
heathfieldcps1
 

Recently uploaded (20)

Operations Management - Book1.p - Dr. Abdulfatah A. Salem
Operations Management - Book1.p  - Dr. Abdulfatah A. SalemOperations Management - Book1.p  - Dr. Abdulfatah A. Salem
Operations Management - Book1.p - Dr. Abdulfatah A. Salem
 
IATP How-to Foreign Travel May 2024.pdff
IATP How-to Foreign Travel May 2024.pdffIATP How-to Foreign Travel May 2024.pdff
IATP How-to Foreign Travel May 2024.pdff
 
How to Create Map Views in the Odoo 17 ERP
How to Create Map Views in the Odoo 17 ERPHow to Create Map Views in the Odoo 17 ERP
How to Create Map Views in the Odoo 17 ERP
 
Phrasal Verbs.XXXXXXXXXXXXXXXXXXXXXXXXXX
Phrasal Verbs.XXXXXXXXXXXXXXXXXXXXXXXXXXPhrasal Verbs.XXXXXXXXXXXXXXXXXXXXXXXXXX
Phrasal Verbs.XXXXXXXXXXXXXXXXXXXXXXXXXX
 
The Benefits and Challenges of Open Educational Resources
The Benefits and Challenges of Open Educational ResourcesThe Benefits and Challenges of Open Educational Resources
The Benefits and Challenges of Open Educational Resources
 
Pragya Champions Chalice 2024 Prelims & Finals Q/A set, General Quiz
Pragya Champions Chalice 2024 Prelims & Finals Q/A set, General QuizPragya Champions Chalice 2024 Prelims & Finals Q/A set, General Quiz
Pragya Champions Chalice 2024 Prelims & Finals Q/A set, General Quiz
 
Basic_QTL_Marker-assisted_Selection_Sourabh.ppt
Basic_QTL_Marker-assisted_Selection_Sourabh.pptBasic_QTL_Marker-assisted_Selection_Sourabh.ppt
Basic_QTL_Marker-assisted_Selection_Sourabh.ppt
 
size separation d pharm 1st year pharmaceutics
size separation d pharm 1st year pharmaceuticssize separation d pharm 1st year pharmaceutics
size separation d pharm 1st year pharmaceutics
 
How to Break the cycle of negative Thoughts
How to Break the cycle of negative ThoughtsHow to Break the cycle of negative Thoughts
How to Break the cycle of negative Thoughts
 
50 ĐỀ LUYỆN THI IOE LỚP 9 - NĂM HỌC 2022-2023 (CÓ LINK HÌNH, FILE AUDIO VÀ ĐÁ...
50 ĐỀ LUYỆN THI IOE LỚP 9 - NĂM HỌC 2022-2023 (CÓ LINK HÌNH, FILE AUDIO VÀ ĐÁ...50 ĐỀ LUYỆN THI IOE LỚP 9 - NĂM HỌC 2022-2023 (CÓ LINK HÌNH, FILE AUDIO VÀ ĐÁ...
50 ĐỀ LUYỆN THI IOE LỚP 9 - NĂM HỌC 2022-2023 (CÓ LINK HÌNH, FILE AUDIO VÀ ĐÁ...
 
GIÁO ÁN DẠY THÊM (KẾ HOẠCH BÀI BUỔI 2) - TIẾNG ANH 8 GLOBAL SUCCESS (2 CỘT) N...
GIÁO ÁN DẠY THÊM (KẾ HOẠCH BÀI BUỔI 2) - TIẾNG ANH 8 GLOBAL SUCCESS (2 CỘT) N...GIÁO ÁN DẠY THÊM (KẾ HOẠCH BÀI BUỔI 2) - TIẾNG ANH 8 GLOBAL SUCCESS (2 CỘT) N...
GIÁO ÁN DẠY THÊM (KẾ HOẠCH BÀI BUỔI 2) - TIẾNG ANH 8 GLOBAL SUCCESS (2 CỘT) N...
 
Telling Your Story_ Simple Steps to Build Your Nonprofit's Brand Webinar.pdf
Telling Your Story_ Simple Steps to Build Your Nonprofit's Brand Webinar.pdfTelling Your Story_ Simple Steps to Build Your Nonprofit's Brand Webinar.pdf
Telling Your Story_ Simple Steps to Build Your Nonprofit's Brand Webinar.pdf
 
INU_CAPSTONEDESIGN_비밀번호486_업로드용 발표자료.pdf
INU_CAPSTONEDESIGN_비밀번호486_업로드용 발표자료.pdfINU_CAPSTONEDESIGN_비밀번호486_업로드용 발표자료.pdf
INU_CAPSTONEDESIGN_비밀번호486_업로드용 발표자료.pdf
 
The impact of social media on mental health and well-being has been a topic o...
The impact of social media on mental health and well-being has been a topic o...The impact of social media on mental health and well-being has been a topic o...
The impact of social media on mental health and well-being has been a topic o...
 
The basics of sentences session 4pptx.pptx
The basics of sentences session 4pptx.pptxThe basics of sentences session 4pptx.pptx
The basics of sentences session 4pptx.pptx
 
Benefits and Challenges of Using Open Educational Resources
Benefits and Challenges of Using Open Educational ResourcesBenefits and Challenges of Using Open Educational Resources
Benefits and Challenges of Using Open Educational Resources
 
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptx
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxStudents, digital devices and success - Andreas Schleicher - 27 May 2024..pptx
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptx
 
Incoming and Outgoing Shipments in 2 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 2 STEPS Using Odoo 17Incoming and Outgoing Shipments in 2 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 2 STEPS Using Odoo 17
 
How to Manage Notification Preferences in the Odoo 17
How to Manage Notification Preferences in the Odoo 17How to Manage Notification Preferences in the Odoo 17
How to Manage Notification Preferences in the Odoo 17
 
NCERT Solutions Power Sharing Class 10 Notes pdf
NCERT Solutions Power Sharing Class 10 Notes pdfNCERT Solutions Power Sharing Class 10 Notes pdf
NCERT Solutions Power Sharing Class 10 Notes pdf
 

A Study of Closure of An Operator

  • 1. International Journal of Trend in Scientific Research and Development (IJTSRD) Volume 5 Issue 1, November-December 2020 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470 @ IJTSRD | Unique Paper ID – IJTSRD37914 | Volume – 5 | Issue – 1 | November-December 2020 Page 354 A Study of Closure of An Operator Praveen Sharma Student, Department of Mathematics, University of Delhi, New Delhi, India ABSTRACT In this paper first we generalizes the Hilbert-Adjoint of a linear operator and showed that it is always closed for any linear operator with the condition that the domain of the operator is dense. We also proved that "Let J be a closed operator defined in H with dense domain then D(J*) is dense and J** = J . " We also proves Closed graph theorem for complex Hilbert spaces as a corollary of our results. Keywords: Separable, Closable, Closure, Operator extension. How to cite this paper: Praveen Sharma "A Study of Closure of An Operator" Published in International Journal of Trend in Scientific Research and Development(ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1, December 2020, pp.347-353, URL: www.ijtsrd.com/papers/ijtsrd37914.pdf Copyright © 2020 by author(s) and International JournalofTrendinScientific Research and DevelopmentJournal. This is an Open Access article distributedundertheterms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0) INTRODUCTION In this Paper we take H as the complexHilbertspaceandD(J) denotes the domain of a linear operator J. In section 1 we generalizes the Hilbert-Adjointforanylinear operator with dense domain in H and prove "If J is defined everywhere on H. Then its Hilbert-adjoint is bounded." In section 2 we first define closable operatorsandshowwith the help of an example that every operator neednotclosable and if it is closable then graph of its closure is equals to the closure of its graph. In Section 3 we proved our result "Let J be a closed operator defined in H with dense domain then D(J*) is dense and J** = J." As a result of which Closed graph Theorem for Complex Hilbert Spaces comes out as Corollary. 1 Hilbert-Adjoint 1.1 Definition: Let J : H → H be a bounded operator then its Hilbert adjoint always exists [2] and is also a bounded linear operator J∗ defined everywhere on H such that (Jφ,ψ) = (φ,J∗ψ) ∀ φ,ψ ∈ H or (Jφ,ψ) = (φ,η) and J∗ψ = η (1) 1.2 Remark: We can use (1) to generalizes the Hilbert Adjoint of any operator. 1.3 Lemma: Let J : D(J) → H be any operator then J∗ψ = η in (1) is unique iff D(J) = H. (i.e the domain of J is dense in H.) Proof: Let J∗ψ = η in (1) is unique. Suppose D (J) H ⇒ ∃ 0 6= µ ∈ H such that From (1) we have (Jφ,ψ) = (φ,η) + 0 = (φ,η) + (φ,µ) = (φ,η + µ) ∀ φ ∈ D(J) Which contradicts the uniqueness of J : D(J) → H. Hence D(J) = H. Conversely Let the domain of J is dense in H. Then Hence unique. 1.4 Remark: Lemma 1.3 shows that Hilbert-Adjoint of any linear operator J exists iff the domain of Jisdense.Nowusing this Lemma we generalizes the definition of Hilbert-Adjoint for any linear operator. IJTSRD37914
  • 2. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD37914 | Volume – 5 | Issue – 1 | November-December 2020 Page 355 1.5 Definition Let J : D(J) → H be any linear operator whose domain is dense the its Hilbert-Adjoint exists and is a linear operator J∗ : D(J∗) → H such that D(J∗) contains all ψ ∈ H such that ∃ η with (Jφ,ψ) = (φ,η) ∀ φ ∈ D(T) and J∗ψ = η Proposition 1.6: LetJ bean operatordefinedeverywhereon H.Then J∗ is bounded. Proof: Suppose J∗ is unbounded. Then ∃ a sequence(vn)each of norm 1 and || vn|| → ∞ as n → ∞ For each n define a functional Pn on H by Pn(φ) = (Jφ,vn) = (φ,J∗vn) By Schwarz Lemma [2] we have |Pn(φ)| = |(φ,J∗vn)| ≤ ||φ||||J∗vn|| ∀ φ ∈ H ∴ Pn is bounded for all n. For each φ ∈ H again by Schwarz Lemma we have |Pn(φ)| = |(Jφ,vn)| ≤ ||Jφ|| ∀ n Then By Uniform Boundness Theorem [1] the sequence ||(Pn)|| is bounded. ⇒ ∃ K > 0 such that ||Pn|| ≤ K ∀ n ⇒ |(J∗vn,J∗vn)| = |Pn(J∗vn)| ≤ ||Pn||||J∗vn|| ≤ K||J∗vn|| ⇒ ||J∗vn|| ≤ K ∀ n ⇒ the sequence (||J∗vn||) is boundedwhichisacontradiction. Hence J∗ is bounded. 2. Closed Linear Extension: 2.1 Definition(Closed operator): An Operator J : D(J) → H is said to be closed if its graph υ(J) = {< φ,Jφ >: φ ∈ D(J)} is closed in H × H, where the inner product on H × H is given by (< φ,ψ >,< η,µ >) = (φ,η) + (ψ,µ) Notation J˜ is said to be an extension of J if D(J) ⊂ D(J˜) and or J˜|D(J) = J υ(J) ⊂ υ(J˜) denoted by J ⊂ J˜ 2.2 Definition (Closable): A linear operator J is said to be closable if J has an extension which is closed. If J is closable then J has a minimal closed extension called closure denoted by J.[1] 2.3 Proposition: if J be any closable operator then υ(J) = υ(J) Proof: Let J be any closable operator ⇒ J exists. Let L be any closed extension of J ⇒ υ(J) ⊂ υ(L) (∵ Lis closed) Define an operator P with domain D(P) = {µ :< µ,η >∈ υ(J)} and Pµ = η Claim: P is well defined Let Pµ = η1 and Pµ = η2 Hence well defined. Also As L is arbitrary. Therefore P is a minimal closed extension of J ⇒ P = J. Hence the result. 2.4 Remark A natural question ariseshere thatifwe wantto take the closure of any linear operator J then we can takethe closure of υ(J) in H×H then just find a operator whose graph is equals to υ(J).But this is not always possible we constructed an Example 2.5 below. 2.5 Example: Let H be any separable Hilbert Space with countable orthonormal basis {ηk}. Let e be an element of H which is not a finite linear combination of {ηk}. Let e Define an operator J : D(J) → H, where D(J) = set of all the finite linear combination of {ηk}andeand N J(ae + Xciηk) = ae i=1 Clearly J is linear Claim:< e,e >,< e,0 > ∈ υ(J) As e ∈ D(J) and Je = e Also, for each N define N ψN = Xcknηkn ∈ D(J) and JψN = 0 ∀ N k=1 By (2)) Suppose ∃ an operator L whose graph is υ(J) ⇒ Le = e and Le = 0. Which contradicts the uniqueness. Hence there is no operator whose graph is υ(J)
  • 3. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD37914 | Volume – 5 | Issue – 1 | November-December 2020 Page 356 3. Main Results Theorem 3.1 Let X : H × H → H × H be an operator given by X < η,µ >=< µ,−η > . Then for any linear operator J defined in H with dense domain we have υ(J∗) = [X(υ(J))]⊥ Moreover J∗ is closed Proof: Clearly, X is linear. Let < η,µ >∈ H × H Then ∃ < −µ,η >∈ H × H such that X < −µ,η >=< η,µ > ∴ X is surjective Also ||X < η,µ > ||2 = || < µ,−η > ||2 = ||µ||2 + ||η||2 = || < η,µ > ||2 ∴ X is a surjective isometry hence unitary Claim: υ(J∗) = [X(υ(J))]⊥ Let < η,µ >∈ [X(υ(J))]⊥ ⇔ (< η,µ >,X < φ,Jφ >) = 0 ∀ φ ∈ D(J) ⇔ (< η,µ >,< Jφ,−φ >) = 0 ∀ φ ∈ D(J) ⇔ < η,Jφ >=< µ,φ > ∀ φ ∈ D(J) ⇔ < Jφ,η >=< φ,µ > ∀ φ ∈ D(J) ⇔ η ∈ D(J∗),J∗η = µ ⇔ < η,µ >∈ υ(J∗) As the graph is closed. Hence J∗ is closed Theorem 3.2 Let J be a closed operator defined in H with dense domain then D(J∗) is dense and J∗∗ = J Proof: As = Identity) = [X(Xυ(J)⊥)]⊥ (∵ X is unitary) = [Xυ(J∗)]⊥ (∵ By Theorem 3.1) (3) As J is closed Suppose D(J∗) is not dense. Then ∃ 0 6= η such that As η 6= 0 then there is no linear operator with graph υ(J). which is a contradiction to J is closed. ∴ D(J∗) is dense ⇒ J∗∗ exists. Then from Theorem 3.1 and (3) we have υ(J∗∗) = [Xυ(J∗)]⊥ = υ(J) ⇒ J = J∗∗ Corollary 3.3:(Closed graph Theorem) AclosedoperatorJ defined everywhere on H then J is bounded. Proof: As J is a linear operator defined everywhere on H. Then J∗ exists and By Proposition 1.6 it is also bounded. Also By Theorem 3.1 we have J∗ is closed ⇒ D(J∗) is closed [2]. By Theorem 3.2 we have and = J ⇒ J∗ is defined everywhere on H. Then again By Proposition 1.6 we have J∗∗ is bounded Hence J is bounded REFERENCE [1] Reed, Michael, and Barry Simon. I: Functional analysis. Vol. 1. Academic press, 1980. [2] Kreyszig, Erwin.Introductoryfunctionalanalysiswith applications. Vol. 1. New York: wiley, 1978. [3] Rudin, Walter. Principles of mathematical analysis. Vol. 3. New York: McGraw-hill, 1964. [4] Jacobson Nathan, Lie algebras. Interscience Publishers,New York (1979).