An application of generalized Tsalli s-havrda-charvat entropy in coding theory through a generalization of Kraft inequality

Size: px
Start display at page:

Download "An application of generalized Tsalli s-havrda-charvat entropy in coding theory through a generalization of Kraft inequality"

Transcription

1 Internatonal Journal of Statstcs and Aled Mathematcs 206; (4): 0-05 ISS: Maths 206; (4): Stats & Maths wwwmathsjournalcom Receved: Acceted: Maharsh Markendeshwar Unversty, Mullana, Ambala, Haryana, Inda An alcaton of generalzed Tsall s-havrda-charvat entroy n codng theory through a generalzaton of Kraft nequalty Abstract A arametrc mean length s defned as the quantty, where 0, 0, u 0, s an nteger, Ths beng the useful mean length of code words weghted by utltes, u Lower and Uer bounds for are derved n terms of useful Tsall s-havrda-charvat nformaton measure for ower robablty dstrbuton Keywords: Tsall s Entroy, Useful Tsall s entroy, Utltes, Kraft nequalty, Holder s nequalty AMS Subject classfcaton: 94A5, 94A7, 94A24, 265 Introducton Consder the followng model for a random exerment S, S E ; P; U, where E E, E2,, E s a fnte system of events haenng wth resectve robabltes P, 2,,, 0, and credted wth utltes U u, u2,, u, u 0,, 2,, enote the model by S, where, E, E2,, E S, 2,, u, u2,, u () We call () a Utlty Informaton Scheme (UIS) Bels and Guasu [2] roosed a measure of nformaton called useful nformaton for ths scheme, gven by H U ; P u log ( ), (2) Corresondence: Maharsh Markendeshwar Unversty, Mullana, Ambala, Haryana, Inda where HU; P reduces to Shannon s [5] entroy when the utlty asect of the scheme s gnored e, when u for each Throughout the aer, wll stand for unless otherwse stated and logarthms are taken to base Guasu and Pcard [4] consdered the roblem of encodng the outcomes n () by means of a refx code wth codewords w, w, 2, w havng lengths n, n, 2, n and satsfyng Kraft s nequalty [3] ~ ~

2 Internatonal Journal of Statstcs and Aled Mathematcs n (3) Where s the sze of the code alhabet The useful mean length u L u of code was defned as: n L u, (4) u and the authors obtaned bounds for t n terms of HU; P Generalzed codng theorems by consderng dfferent generalzed measures under condton (3) of unque decherablty were nvestgated by several authors, see for nstance the aers [4, 8-0, 3] In ths aer, we study some codng theorems by consderng a new functon deendng on the arameters, and a utlty functon Our motvaton for studyng ths new functon s that t generalzes useful nformaton measure already exstng n the lterature such Tsall s entroy [7], Havrda-Charvat [6] etc 2 Codng Theorems In ths secton, we defne a new nformaton measure as : ; H U P, (2) 0, 0, u 0, 0,, 2,, and where () If, Then (2) becomes a useful nformaton measure e, ; H U P (22) () When u for each, e, when the utlty asect s gnored,, and, then (2) reduces to Tsall s-havrda- Charvat entroy e, HP (23) () When, and, then (2) reduces to a measure of useful nformaton due to Hooda and Bhaker [] log ( ) e, HU; P (24) (v) When u for each, then (2) reduced to Satsh and Arun [3] entroy e, ; H U P (25) (v) When u for each, e, When the utlty asect s gnored,,, and, the measure (2) reduces to Shannon s entroy [5] H P (26) e, log ( ) Further consder, efnton: The useful mean length wth resect to useful R-norm nformaton measure s defned as :, (27) n under the condton, u (28) Clearly the nequalty (28) s the generalzaton of Kraft s nequalty (3) A code satsfyng (28) would be termed as a useful ersonal robablty code (>2) s the sze of the code alhabet When, u for each and,, (28) reduces to (3) ~ 2 ~

3 Internatonal Journal of Statstcs and Aled Mathematcs () For u for each and, and, becomes the otmal code length defned by Shannon [5] () For u for each and, then (27) becomes a new mean code word length corresondng to the Tsall s entroy n e, L (29) () If, then (27) becomes a new mean codewords length corresondng to the entroy (22) n e, (v) If u, then (27) becomes a mean codewords length corresondng to the entroy (25) e, L We establsh a result, that n a sense, rovdes a characterzaton of H U; P under the condton of unque decherablty Theorem 2 Let u,, n,,2,,, satsfy the nequalty (28) Then H ( U; P), 0, 0 (20) Proof: By Holder s nequalty, we have q q x y xy, (2) q ; ( 0), q 0 or q( 0), 0; x, y 0 for each where ( ) Settng,, q, and n, x y, (22) Puttng these values n (2) and usng the nequalty (28), we get n ( ) (23) It mles n ( ) (24) ow consder two cases: Case : Let 0 Rasng both sdes of (24) to the ower ( ), we get n ( ) Snce, ( ) 0for 0, we get from (25) the nequalty (20) (25) Case 2: Let The roof follows on the same lnes It s clear that the equalty n (20) s true f and only f n whch mles that log (26) n ~ 3 ~

4 Internatonal Journal of Statstcs and Aled Mathematcs Thus, t s always ossble to have a codeword satsfyng the requrement log n log, whch s equvalent to n (27) In the followng theorem, we gve an er bound for n terms of H ( U; P) Theorem 22 By roerly choosng the lengths n, n2,, n n the code of Theorem 2, can be made to satsy the followng nequalty: ( ) ( ; ) ( L ) u H U P (28) Proof: From (27), t s clear that n (29) We have agan the followng two ossbltes () Let n ( ) ( ) Rasng both sdes of (29) to the ower ( ) Multlyng both sdes by and then summng over we get n ( ) ( ), we have (220) Obvously (220) can be wrtten as n ( ) ( ) (22) Snce 0 for, we get the nequalty (28) from (22) () If 0, the roof follows smlarly But the nequalty (22) s reversed Theorem 23 For arbtrary, 0, 0, and for every codeword lengths n,,2,, of Theorem 2, L u can be made to satsy the followng nequalty: H ( U ; P) H ( U ; P) Proof: Sose, log, 0 n (222) (223) Clearly n and n satsfy the equalty n Holder s nequalty (2) Moreover, nteger between n and n, then obvously, n satsfes (28) Snce 0, 0, we have n ( ) n ( ) n ( ) n ( ) ~ 4 ~ n satsfes (28) Sose n s the unque (224) Snce,

5 Internatonal Journal of Statstcs and Aled Mathematcs Hence (224) becomes n ( ) Whch gves (222) 3 References Bhaker US, Hooda S Mean value Characterzaton of useful nformaton measures, Tamkang J Math 993; 24: Bels M, Guasu S A Qualtatve-Quanttatve Measure of Informaton n Cybernetcs Systems, IEEE Trans Informaton Theory, 968; IT-4: Fensten A Foundaton of Informaton Theory, McGraw Hll, ew York Guasu S, Pcard CF Borne Infercutre de la Longueur Utle de Certan Codes, CR Acad Sc, Pars, 97; 273A: Gurdal, Pessoa F On Useful Informaton of Order, J Comb Informaton and Syst Sc 977; 2: Havrda JF, Charvat F Qualfcaton Method of Classfcaton Process the concet of Structural α-entroy, Kybernetka, 967; 3:30-35, Kumar S Some more results on R-orm nformaton measure, Tamkang Journal of Mathematcs, 2009; 40(): Kumar S Some more results on a generalzed useful R-orm nformaton measure, Tamkang Journal of Mathematcs, 2009; 40(2): Kumar S, Choudhary A Some More oseless Codng Theorem on Generalzed R-orm Entroy, Journal of Mathematcs Research 20; 3(): Kumar S, Choudhary A Codng Theorem Connected on R-orm Entroy, Internatonal Journal of Contemorary Mathematcal Scences 20; 6(7): Kumar S, Choudhary A Some Codng Theorems Based on Three Tyes of the Exonental Form of Cost Functons, Oen Systems and Informaton ynamcs, 202; 9(4):-4 2 Kumar S, Kumar R, Choudhary A Some more results on a generalzed arametrc R-norm nformaton measure of tye Alha Journal of Aled Scence and Engg 204; 7(4): Kumar S, Choudhary A Some codng theorems on generalzed Havrda-Charvat and Tsall s entroy, Tamkang journal of mathematcs, 202; 43(3): Longo G A oseless Codng Theorem for Sources Havng Utltes, SIAM J Al Math, 976; 30(4): Shannon CE A Mathematcal Theory of Communcaton, Bell System Tech-J 948; 27: , Shsha O Inequaltes, Academc Press, ew York Tsall s C Possble generalzaton of Boltzmann Gbbs statstcs J Stat Phys 988; 52: ~ 5 ~

Parametric Useful Information Measure of Order Some Coding Theorem

Parametric Useful Information Measure of Order Some Coding Theorem Avalable onlne wwwejaetcom Eroean Jornal of Advances n Engneerng and Technoy, 207, 4(8): 603-607 Research Artcle ISS: 2394-658X Parametrc Usefl Informaton Measre of Order Some Codng Theorem egree and hanesh

More information

SOME NOISELESS CODING THEOREM CONNECTED WITH HAVRDA AND CHARVAT AND TSALLIS S ENTROPY. 1. Introduction

SOME NOISELESS CODING THEOREM CONNECTED WITH HAVRDA AND CHARVAT AND TSALLIS S ENTROPY. 1. Introduction Kragujevac Journal of Mathematcs Volume 35 Number (20, Pages 7 SOME NOISELESS COING THEOREM CONNECTE WITH HAVRA AN CHARVAT AN TSALLIS S ENTROPY SATISH KUMAR AN RAJESH KUMAR 2 Abstract A new measure L,

More information

Lower and upper bound for parametric Useful R-norm information measure

Lower and upper bound for parametric Useful R-norm information measure Iteratoal Joral of Statstcs ad Aled Mathematcs 206; (3): 6-20 ISS: 2456-452 Maths 206; (3): 6-20 206 Stats & Maths wwwmathsjoralcom eceved: 04-07-206 Acceted: 05-08-206 haesh Garg Satsh Kmar ower ad er

More information

The Order Relation and Trace Inequalities for. Hermitian Operators

The Order Relation and Trace Inequalities for. Hermitian Operators Internatonal Mathematcal Forum, Vol 3, 08, no, 507-57 HIKARI Ltd, wwwm-hkarcom https://doorg/0988/mf088055 The Order Relaton and Trace Inequaltes for Hermtan Operators Y Huang School of Informaton Scence

More information

arxiv: v1 [math.co] 1 Mar 2014

arxiv: v1 [math.co] 1 Mar 2014 Unon-ntersectng set systems Gyula O.H. Katona and Dánel T. Nagy March 4, 014 arxv:1403.0088v1 [math.co] 1 Mar 014 Abstract Three ntersecton theorems are proved. Frst, we determne the sze of the largest

More information

SOME NOISELESS CODING THEOREMS OF INACCURACY MEASURE OF ORDER α AND TYPE β

SOME NOISELESS CODING THEOREMS OF INACCURACY MEASURE OF ORDER α AND TYPE β SARAJEVO JOURNAL OF MATHEMATICS Vol.3 (15) (2007), 137 143 SOME NOISELESS CODING THEOREMS OF INACCURACY MEASURE OF ORDER α AND TYPE β M. A. K. BAIG AND RAYEES AHMAD DAR Absrac. In hs paper, we propose

More information

Some congruences related to harmonic numbers and the terms of the second order sequences

Some congruences related to harmonic numbers and the terms of the second order sequences Mathematca Moravca Vol. 0: 06, 3 37 Some congruences related to harmonc numbers the terms of the second order sequences Neşe Ömür Sbel Koaral Abstract. In ths aer, wth hels of some combnatoral denttes,

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Analyss of Varance and Desgn of Exerments-I MODULE III LECTURE - 2 EXPERIMENTAL DESIGN MODELS Dr. Shalabh Deartment of Mathematcs and Statstcs Indan Insttute of Technology Kanur 2 We consder the models

More information

The lower and upper bounds on Perron root of nonnegative irreducible matrices

The lower and upper bounds on Perron root of nonnegative irreducible matrices Journal of Computatonal Appled Mathematcs 217 (2008) 259 267 wwwelsevercom/locate/cam The lower upper bounds on Perron root of nonnegatve rreducble matrces Guang-Xn Huang a,, Feng Yn b,keguo a a College

More information

ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EQUATION

ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EQUATION Advanced Mathematcal Models & Applcatons Vol.3, No.3, 2018, pp.215-222 ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EUATION

More information

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family IOSR Journal of Mathematcs IOSR-JM) ISSN: 2278-5728. Volume 3, Issue 3 Sep-Oct. 202), PP 44-48 www.osrjournals.org Usng T.O.M to Estmate Parameter of dstrbutons that have not Sngle Exponental Famly Jubran

More information

FACTORIZATION IN KRULL MONOIDS WITH INFINITE CLASS GROUP

FACTORIZATION IN KRULL MONOIDS WITH INFINITE CLASS GROUP C O L L O Q U I U M M A T H E M A T I C U M VOL. 80 1999 NO. 1 FACTORIZATION IN KRULL MONOIDS WITH INFINITE CLASS GROUP BY FLORIAN K A I N R A T H (GRAZ) Abstract. Let H be a Krull monod wth nfnte class

More information

Excess Error, Approximation Error, and Estimation Error

Excess Error, Approximation Error, and Estimation Error E0 370 Statstcal Learnng Theory Lecture 10 Sep 15, 011 Excess Error, Approxaton Error, and Estaton Error Lecturer: Shvan Agarwal Scrbe: Shvan Agarwal 1 Introducton So far, we have consdered the fnte saple

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Analyss of Varance and Desgn of Exerments-I MODULE II LECTURE - GENERAL LINEAR HYPOTHESIS AND ANALYSIS OF VARIANCE Dr. Shalabh Deartment of Mathematcs and Statstcs Indan Insttute of Technology Kanur 3.

More information

Volume 18 Figure 1. Notation 1. Notation 2. Observation 1. Remark 1. Remark 2. Remark 3. Remark 4. Remark 5. Remark 6. Theorem A [2]. Theorem B [2].

Volume 18 Figure 1. Notation 1. Notation 2. Observation 1. Remark 1. Remark 2. Remark 3. Remark 4. Remark 5. Remark 6. Theorem A [2]. Theorem B [2]. Bulletn of Mathematcal Scences and Applcatons Submtted: 016-04-07 ISSN: 78-9634, Vol. 18, pp 1-10 Revsed: 016-09-08 do:10.1805/www.scpress.com/bmsa.18.1 Accepted: 016-10-13 017 ScPress Ltd., Swtzerland

More information

Comparing two Quantiles: the Burr Type X and Weibull Cases

Comparing two Quantiles: the Burr Type X and Weibull Cases IOSR Journal of Mathematcs (IOSR-JM) e-issn: 78-578, -ISSN: 39-765X. Volume, Issue 5 Ver. VII (Se. - Oct.06), PP 8-40 www.osrjournals.org Comarng two Quantles: the Burr Tye X and Webull Cases Mohammed

More information

A Note on Bound for Jensen-Shannon Divergence by Jeffreys

A Note on Bound for Jensen-Shannon Divergence by Jeffreys OPEN ACCESS Conference Proceedngs Paper Entropy www.scforum.net/conference/ecea- A Note on Bound for Jensen-Shannon Dvergence by Jeffreys Takuya Yamano, * Department of Mathematcs and Physcs, Faculty of

More information

Non-Ideality Through Fugacity and Activity

Non-Ideality Through Fugacity and Activity Non-Idealty Through Fugacty and Actvty S. Patel Deartment of Chemstry and Bochemstry, Unversty of Delaware, Newark, Delaware 19716, USA Corresondng author. E-mal: saatel@udel.edu 1 I. FUGACITY In ths dscusson,

More information

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number

More information

Perfect Competition and the Nash Bargaining Solution

Perfect Competition and the Nash Bargaining Solution Perfect Competton and the Nash Barganng Soluton Renhard John Department of Economcs Unversty of Bonn Adenauerallee 24-42 53113 Bonn, Germany emal: rohn@un-bonn.de May 2005 Abstract For a lnear exchange

More information

Estimation: Part 2. Chapter GREG estimation

Estimation: Part 2. Chapter GREG estimation Chapter 9 Estmaton: Part 2 9. GREG estmaton In Chapter 8, we have seen that the regresson estmator s an effcent estmator when there s a lnear relatonshp between y and x. In ths chapter, we generalzed the

More information

On New Selection Procedures for Unequal Probability Sampling

On New Selection Procedures for Unequal Probability Sampling Int. J. Oen Problems Comt. Math., Vol. 4, o. 1, March 011 ISS 1998-66; Coyrght ICSRS Publcaton, 011 www.-csrs.org On ew Selecton Procedures for Unequal Probablty Samlng Muhammad Qaser Shahbaz, Saman Shahbaz

More information

Supplementary Material for Spectral Clustering based on the graph p-laplacian

Supplementary Material for Spectral Clustering based on the graph p-laplacian Sulementary Materal for Sectral Clusterng based on the grah -Lalacan Thomas Bühler and Matthas Hen Saarland Unversty, Saarbrücken, Germany {tb,hen}@csun-sbde May 009 Corrected verson, June 00 Abstract

More information

Malaya J. Mat. 2(1)(2014) 49 60

Malaya J. Mat. 2(1)(2014) 49 60 Malaya J. Mat. 2(1)(2014) 49 60 Functonal equaton orgnatng from sum of hgher owers of arthmetc rogresson usng dfference oerator s stable n Banach sace: drect and fxed ont methods M. Arunumar a, and G.

More information

Maximizing the number of nonnegative subsets

Maximizing the number of nonnegative subsets Maxmzng the number of nonnegatve subsets Noga Alon Hao Huang December 1, 213 Abstract Gven a set of n real numbers, f the sum of elements of every subset of sze larger than k s negatve, what s the maxmum

More information

Matching Dyadic Distributions to Channels

Matching Dyadic Distributions to Channels Matchng Dyadc Dstrbutons to Channels G. Böcherer and R. Mathar Insttute for Theoretcal Informaton Technology RWTH Aachen Unversty, 5256 Aachen, Germany Emal: {boecherer,mathar}@t.rwth-aachen.de Abstract

More information

Another converse of Jensen s inequality

Another converse of Jensen s inequality Another converse of Jensen s nequalty Slavko Smc Abstract. We gve the best possble global bounds for a form of dscrete Jensen s nequalty. By some examples ts frutfulness s shown. 1. Introducton Throughout

More information

Fuzzy approach to solve multi-objective capacitated transportation problem

Fuzzy approach to solve multi-objective capacitated transportation problem Internatonal Journal of Bonformatcs Research, ISSN: 0975 087, Volume, Issue, 00, -0-4 Fuzzy aroach to solve mult-objectve caactated transortaton roblem Lohgaonkar M. H. and Bajaj V. H.* * Deartment of

More information

Lec 02 Entropy and Lossless Coding I

Lec 02 Entropy and Lossless Coding I Multmeda Communcaton, Fall 208 Lec 02 Entroy and Lossless Codng I Zhu L Z. L Multmeda Communcaton, Fall 208. Outlne Lecture 0 ReCa Info Theory on Entroy Lossless Entroy Codng Z. L Multmeda Communcaton,

More information

Randić Energy and Randić Estrada Index of a Graph

Randić Energy and Randić Estrada Index of a Graph EUROPEAN JOURNAL OF PURE AND APPLIED MATHEMATICS Vol. 5, No., 202, 88-96 ISSN 307-5543 www.ejpam.com SPECIAL ISSUE FOR THE INTERNATIONAL CONFERENCE ON APPLIED ANALYSIS AND ALGEBRA 29 JUNE -02JULY 20, ISTANBUL

More information

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix Lectures - Week 4 Matrx norms, Condtonng, Vector Spaces, Lnear Independence, Spannng sets and Bass, Null space and Range of a Matrx Matrx Norms Now we turn to assocatng a number to each matrx. We could

More information

The Jacobsthal and Jacobsthal-Lucas Numbers via Square Roots of Matrices

The Jacobsthal and Jacobsthal-Lucas Numbers via Square Roots of Matrices Internatonal Mathematcal Forum, Vol 11, 2016, no 11, 513-520 HIKARI Ltd, wwwm-hkarcom http://dxdoorg/1012988/mf20166442 The Jacobsthal and Jacobsthal-Lucas Numbers va Square Roots of Matrces Saadet Arslan

More information

Managing Capacity Through Reward Programs. on-line companion page. Byung-Do Kim Seoul National University College of Business Administration

Managing Capacity Through Reward Programs. on-line companion page. Byung-Do Kim Seoul National University College of Business Administration Managng Caacty Through eward Programs on-lne comanon age Byung-Do Km Seoul Natonal Unversty College of Busness Admnstraton Mengze Sh Unversty of Toronto otman School of Management Toronto ON M5S E6 Canada

More information

PARTIAL QUOTIENTS AND DISTRIBUTION OF SEQUENCES. Department of Mathematics University of California Riverside, CA

PARTIAL QUOTIENTS AND DISTRIBUTION OF SEQUENCES. Department of Mathematics University of California Riverside, CA PARTIAL QUOTIETS AD DISTRIBUTIO OF SEQUECES 1 Me-Chu Chang Deartment of Mathematcs Unversty of Calforna Rversde, CA 92521 mcc@math.ucr.edu Abstract. In ths aer we establsh average bounds on the artal quotents

More information

Quantum and Classical Information Theory with Disentropy

Quantum and Classical Information Theory with Disentropy Quantum and Classcal Informaton Theory wth Dsentropy R V Ramos rubensramos@ufcbr Lab of Quantum Informaton Technology, Department of Telenformatc Engneerng Federal Unversty of Ceara - DETI/UFC, CP 6007

More information

Sharp integral inequalities involving high-order partial derivatives. Journal Of Inequalities And Applications, 2008, v. 2008, article no.

Sharp integral inequalities involving high-order partial derivatives. Journal Of Inequalities And Applications, 2008, v. 2008, article no. Ttle Sharp ntegral nequaltes nvolvng hgh-order partal dervatves Authors Zhao, CJ; Cheung, WS Ctaton Journal Of Inequaltes And Applcatons, 008, v. 008, artcle no. 5747 Issued Date 008 URL http://hdl.handle.net/07/569

More information

Introduction to Information Theory, Data Compression,

Introduction to Information Theory, Data Compression, Introducton to Informaton Theory, Data Compresson, Codng Mehd Ibm Brahm, Laura Mnkova Aprl 5, 208 Ths s the augmented transcrpt of a lecture gven by Luc Devroye on the 3th of March 208 for a Data Structures

More information

A NOTE ON THE DISCRETE FOURIER RESTRICTION PROBLEM

A NOTE ON THE DISCRETE FOURIER RESTRICTION PROBLEM A NOTE ON THE DISCRETE FOURIER RESTRICTION PROBLEM XUDONG LAI AND YONG DING arxv:171001481v1 [mathap] 4 Oct 017 Abstract In ths aer we establsh a general dscrete Fourer restrcton theorem As an alcaton

More information

On some variants of Jensen s inequality

On some variants of Jensen s inequality On some varants of Jensen s nequalty S S DRAGOMIR School of Communcatons & Informatcs, Vctora Unversty, Vc 800, Australa EMMA HUNT Department of Mathematcs, Unversty of Adelade, SA 5005, Adelade, Australa

More information

On Unequal Probability Sampling Without Replacement Sample Size 2

On Unequal Probability Sampling Without Replacement Sample Size 2 Int J Oen Problems Com Math, Vol, o, March 009 On Unequal Probablt Samlng Wthout Relacement Samle Sze aser A Alodat Deartment of Mathematcs, Irbd atonal Unverst, Jordan e-mal: n_odat@ahoocom Communcated

More information

The binomial transforms of the generalized (s, t )-Jacobsthal matrix sequence

The binomial transforms of the generalized (s, t )-Jacobsthal matrix sequence Int. J. Adv. Appl. Math. and Mech. 6(3 (2019 14 20 (ISSN: 2347-2529 Journal homepage: www.jaamm.com IJAAMM Internatonal Journal of Advances n Appled Mathematcs and Mechancs The bnomal transforms of the

More information

( ) 2 ( ) ( ) Problem Set 4 Suggested Solutions. Problem 1

( ) 2 ( ) ( ) Problem Set 4 Suggested Solutions. Problem 1 Problem Set 4 Suggested Solutons Problem (A) The market demand functon s the soluton to the followng utlty-maxmzaton roblem (UMP): The Lagrangean: ( x, x, x ) = + max U x, x, x x x x st.. x + x + x y x,

More information

Appendix B. Criterion of Riemann-Stieltjes Integrability

Appendix B. Criterion of Riemann-Stieltjes Integrability Appendx B. Crteron of Remann-Steltes Integrablty Ths note s complementary to [R, Ch. 6] and [T, Sec. 3.5]. The man result of ths note s Theorem B.3, whch provdes the necessary and suffcent condtons for

More information

arxiv: v1 [math.co] 12 Sep 2014

arxiv: v1 [math.co] 12 Sep 2014 arxv:1409.3707v1 [math.co] 12 Sep 2014 On the bnomal sums of Horadam sequence Nazmye Ylmaz and Necat Taskara Department of Mathematcs, Scence Faculty, Selcuk Unversty, 42075, Campus, Konya, Turkey March

More information

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U) Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of

More information

The internal structure of natural numbers and one method for the definition of large prime numbers

The internal structure of natural numbers and one method for the definition of large prime numbers The nternal structure of natural numbers and one method for the defnton of large prme numbers Emmanul Manousos APM Insttute for the Advancement of Physcs and Mathematcs 3 Poulou str. 53 Athens Greece Abstract

More information

CSCE 790S Background Results

CSCE 790S Background Results CSCE 790S Background Results Stephen A. Fenner September 8, 011 Abstract These results are background to the course CSCE 790S/CSCE 790B, Quantum Computaton and Informaton (Sprng 007 and Fall 011). Each

More information

ISSN: ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 3, Issue 1, July 2013

ISSN: ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 3, Issue 1, July 2013 ISSN: 2277-375 Constructon of Trend Free Run Orders for Orthogonal rrays Usng Codes bstract: Sometmes when the expermental runs are carred out n a tme order sequence, the response can depend on the run

More information

Refined Coding Bounds for Network Error Correction

Refined Coding Bounds for Network Error Correction Refned Codng Bounds for Network Error Correcton Shenghao Yang Department of Informaton Engneerng The Chnese Unversty of Hong Kong Shatn, N.T., Hong Kong shyang5@e.cuhk.edu.hk Raymond W. Yeung Department

More information

EGR 544 Communication Theory

EGR 544 Communication Theory EGR 544 Communcaton Theory. Informaton Sources Z. Alyazcoglu Electrcal and Computer Engneerng Department Cal Poly Pomona Introducton Informaton Source x n Informaton sources Analog sources Dscrete sources

More information

On quasiperfect numbers

On quasiperfect numbers Notes on Number Theory and Dscrete Mathematcs Prnt ISSN 1310 5132, Onlne ISSN 2367 8275 Vol. 23, 2017, No. 3, 73 78 On quasperfect numbers V. Sva Rama Prasad 1 and C. Suntha 2 1 Nalla Malla Reddy Engneerng

More information

First day August 1, Problems and Solutions

First day August 1, Problems and Solutions FOURTH INTERNATIONAL COMPETITION FOR UNIVERSITY STUDENTS IN MATHEMATICS July 30 August 4, 997, Plovdv, BULGARIA Frst day August, 997 Problems and Solutons Problem. Let {ε n } n= be a sequence of postve

More information

A New Algorithm for Finding a Fuzzy Optimal. Solution for Fuzzy Transportation Problems

A New Algorithm for Finding a Fuzzy Optimal. Solution for Fuzzy Transportation Problems Appled Mathematcal Scences, Vol. 4, 200, no. 2, 79-90 A New Algorthm for Fndng a Fuzzy Optmal Soluton for Fuzzy Transportaton Problems P. Pandan and G. Nataraan Department of Mathematcs, School of Scence

More information

SL n (F ) Equals its Own Derived Group

SL n (F ) Equals its Own Derived Group Internatonal Journal of Algebra, Vol. 2, 2008, no. 12, 585-594 SL n (F ) Equals ts Own Derved Group Jorge Macel BMCC-The Cty Unversty of New York, CUNY 199 Chambers street, New York, NY 10007, USA macel@cms.nyu.edu

More information

Research Article Optimal Policies for a Finite-Horizon Production Inventory Model

Research Article Optimal Policies for a Finite-Horizon Production Inventory Model Advances n Oeratons Research Volume 2012, Artcle ID 768929, 16 ages do:10.1155/2012/768929 Research Artcle Otmal Polces for a Fnte-Horzon Producton Inventory Model Lakdere Benkherouf and Dalal Boushehr

More information

MATH 5707 HOMEWORK 4 SOLUTIONS 2. 2 i 2p i E(X i ) + E(Xi 2 ) ä i=1. i=1

MATH 5707 HOMEWORK 4 SOLUTIONS 2. 2 i 2p i E(X i ) + E(Xi 2 ) ä i=1. i=1 MATH 5707 HOMEWORK 4 SOLUTIONS CİHAN BAHRAN 1. Let v 1,..., v n R m, all lengths v are not larger than 1. Let p 1,..., p n [0, 1] be arbtrary and set w = p 1 v 1 + + p n v n. Then there exst ε 1,..., ε

More information

STEINHAUS PROPERTY IN BANACH LATTICES

STEINHAUS PROPERTY IN BANACH LATTICES DEPARTMENT OF MATHEMATICS TECHNICAL REPORT STEINHAUS PROPERTY IN BANACH LATTICES DAMIAN KUBIAK AND DAVID TIDWELL SPRING 2015 No. 2015-1 TENNESSEE TECHNOLOGICAL UNIVERSITY Cookevlle, TN 38505 STEINHAUS

More information

An (almost) unbiased estimator for the S-Gini index

An (almost) unbiased estimator for the S-Gini index An (almost unbased estmator for the S-Gn ndex Thomas Demuynck February 25, 2009 Abstract Ths note provdes an unbased estmator for the absolute S-Gn and an almost unbased estmator for the relatve S-Gn for

More information

The L(2, 1)-Labeling on -Product of Graphs

The L(2, 1)-Labeling on -Product of Graphs Annals of Pure and Appled Mathematcs Vol 0, No, 05, 9-39 ISSN: 79-087X (P, 79-0888(onlne Publshed on 7 Aprl 05 wwwresearchmathscorg Annals of The L(, -Labelng on -Product of Graphs P Pradhan and Kamesh

More information

Foundations of Arithmetic

Foundations of Arithmetic Foundatons of Arthmetc Notaton We shall denote the sum and product of numbers n the usual notaton as a 2 + a 2 + a 3 + + a = a, a 1 a 2 a 3 a = a The notaton a b means a dvdes b,.e. ac = b where c s an

More information

On the Connectedness of the Solution Set for the Weak Vector Variational Inequality 1

On the Connectedness of the Solution Set for the Weak Vector Variational Inequality 1 Journal of Mathematcal Analyss and Alcatons 260, 15 2001 do:10.1006jmaa.2000.7389, avalable onlne at htt:.dealbrary.com on On the Connectedness of the Soluton Set for the Weak Vector Varatonal Inequalty

More information

Caps and Colouring Steiner Triple Systems

Caps and Colouring Steiner Triple Systems Desgns, Codes and Cryptography, 13, 51 55 (1998) c 1998 Kluwer Academc Publshers, Boston. Manufactured n The Netherlands. Caps and Colourng Stener Trple Systems AIDEN BRUEN* Department of Mathematcs, Unversty

More information

CODING THEOREMS ON NEW ADDITIVE INFORMATION MEASURE OF ORDER

CODING THEOREMS ON NEW ADDITIVE INFORMATION MEASURE OF ORDER Pak. J. Statist. 2018 Vol. 34(2), 137-146 CODING THEOREMS ON NEW ADDITIVE INFORMATION MEASURE OF ORDER Ashiq Hussain Bhat 1 and M.A.K. Baig 2 Post Graduate Department of Statistics, University of Kashmir,

More information

SMARANDACHE-GALOIS FIELDS

SMARANDACHE-GALOIS FIELDS SMARANDACHE-GALOIS FIELDS W. B. Vasantha Kandasamy Deartment of Mathematcs Indan Insttute of Technology, Madras Chenna - 600 036, Inda. E-mal: vasantak@md3.vsnl.net.n Abstract: In ths aer we study the

More information

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction ECONOMICS 5* -- NOTE (Summary) ECON 5* -- NOTE The Multple Classcal Lnear Regresson Model (CLRM): Specfcaton and Assumptons. Introducton CLRM stands for the Classcal Lnear Regresson Model. The CLRM s also

More information

Confidence intervals for weighted polynomial calibrations

Confidence intervals for weighted polynomial calibrations Confdence ntervals for weghted olynomal calbratons Sergey Maltsev, Amersand Ltd., Moscow, Russa; ur Kalambet, Amersand Internatonal, Inc., Beachwood, OH e-mal: kalambet@amersand-ntl.com htt://www.chromandsec.com

More information

A note on almost sure behavior of randomly weighted sums of φ-mixing random variables with φ-mixing weights

A note on almost sure behavior of randomly weighted sums of φ-mixing random variables with φ-mixing weights ACTA ET COMMENTATIONES UNIVERSITATIS TARTUENSIS DE MATHEMATICA Volume 7, Number 2, December 203 Avalable onlne at http://acutm.math.ut.ee A note on almost sure behavor of randomly weghted sums of φ-mxng

More information

Yong Joon Ryang. 1. Introduction Consider the multicommodity transportation problem with convex quadratic cost function. 1 2 (x x0 ) T Q(x x 0 )

Yong Joon Ryang. 1. Introduction Consider the multicommodity transportation problem with convex quadratic cost function. 1 2 (x x0 ) T Q(x x 0 ) Kangweon-Kyungk Math. Jour. 4 1996), No. 1, pp. 7 16 AN ITERATIVE ROW-ACTION METHOD FOR MULTICOMMODITY TRANSPORTATION PROBLEMS Yong Joon Ryang Abstract. The optmzaton problems wth quadratc constrants often

More information

A SUMMARY ON ENTROPY STATISTICS

A SUMMARY ON ENTROPY STATISTICS A SUARY ON ENTROPY STATISTICS Esteban,.D. and orales, D. Departamento de Estadístca e I.O. Facultad de atemátcas Unversdad Complutense de adrd 28040 - ADRID SPAIN). Abstract Wth the purpose to study as

More information

Math 217 Fall 2013 Homework 2 Solutions

Math 217 Fall 2013 Homework 2 Solutions Math 17 Fall 013 Homework Solutons Due Thursday Sept. 6, 013 5pm Ths homework conssts of 6 problems of 5 ponts each. The total s 30. You need to fully justfy your answer prove that your functon ndeed has

More information

More metrics on cartesian products

More metrics on cartesian products More metrcs on cartesan products If (X, d ) are metrc spaces for 1 n, then n Secton II4 of the lecture notes we defned three metrcs on X whose underlyng topologes are the product topology The purpose of

More information

A General Class of Selection Procedures and Modified Murthy Estimator

A General Class of Selection Procedures and Modified Murthy Estimator ISS 684-8403 Journal of Statstcs Volume 4, 007,. 3-9 A General Class of Selecton Procedures and Modfed Murthy Estmator Abdul Bast and Muhammad Qasar Shahbaz Abstract A new selecton rocedure for unequal

More information

Research Journal of Pure Algebra -2(12), 2012, Page: Available online through ISSN

Research Journal of Pure Algebra -2(12), 2012, Page: Available online through  ISSN Research Journal of Pure Algebra (, 0, Page: 37038 Avalable onlne through www.rja.nfo ISSN 48 9037 A NEW GENERALISATION OF SAMSOLAI S MULTIVARIATE ADDITIVE EXPONENTIAL DISTRIBUTION* Dr. G. S. Davd Sam

More information

Some Notes on Consumer Theory

Some Notes on Consumer Theory Some Notes on Consumer Theory. Introducton In ths lecture we eamne the theory of dualty n the contet of consumer theory and ts use n the measurement of the benefts of rce and other changes. Dualty s not

More information

CHAPTER-5 INFORMATION MEASURE OF FUZZY MATRIX AND FUZZY BINARY RELATION

CHAPTER-5 INFORMATION MEASURE OF FUZZY MATRIX AND FUZZY BINARY RELATION CAPTER- INFORMATION MEASURE OF FUZZY MATRI AN FUZZY BINARY RELATION Introducton The basc concept of the fuzz matr theor s ver smple and can be appled to socal and natural stuatons A branch of fuzz matr

More information

Y. Guo. A. Liu, T. Liu, Q. Ma UDC

Y. Guo. A. Liu, T. Liu, Q. Ma UDC UDC 517. 9 OSCILLATION OF A CLASS OF NONLINEAR PARTIAL DIFFERENCE EQUATIONS WITH CONTINUOUS VARIABLES* ОСЦИЛЯЦIЯ КЛАСУ НЕЛIНIЙНИХ ЧАСТКОВО РIЗНИЦЕВИХ РIВНЯНЬ З НЕПЕРЕРВНИМИ ЗМIННИМИ Y. Guo Graduate School

More information

On the set of natural numbers

On the set of natural numbers On the set of natural numbers by Jalton C. Ferrera Copyrght 2001 Jalton da Costa Ferrera Introducton The natural numbers have been understood as fnte numbers, ths wor tres to show that the natural numbers

More information

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification E395 - Pattern Recognton Solutons to Introducton to Pattern Recognton, Chapter : Bayesan pattern classfcaton Preface Ths document s a soluton manual for selected exercses from Introducton to Pattern Recognton

More information

ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE

ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE School of Computer and Communcaton Scences Handout 0 Prncples of Dgtal Communcatons Solutons to Problem Set 4 Mar. 6, 08 Soluton. If H = 0, we have Y = Z Z = Y

More information

Beyond Zudilin s Conjectured q-analog of Schmidt s problem

Beyond Zudilin s Conjectured q-analog of Schmidt s problem Beyond Zudln s Conectured q-analog of Schmdt s problem Thotsaporn Ae Thanatpanonda thotsaporn@gmalcom Mathematcs Subect Classfcaton: 11B65 33B99 Abstract Usng the methodology of (rgorous expermental mathematcs

More information

Complete subgraphs in multipartite graphs

Complete subgraphs in multipartite graphs Complete subgraphs n multpartte graphs FLORIAN PFENDER Unverstät Rostock, Insttut für Mathematk D-18057 Rostock, Germany Floran.Pfender@un-rostock.de Abstract Turán s Theorem states that every graph G

More information

TAIL BOUNDS FOR SUMS OF GEOMETRIC AND EXPONENTIAL VARIABLES

TAIL BOUNDS FOR SUMS OF GEOMETRIC AND EXPONENTIAL VARIABLES TAIL BOUNDS FOR SUMS OF GEOMETRIC AND EXPONENTIAL VARIABLES SVANTE JANSON Abstract. We gve explct bounds for the tal probabltes for sums of ndependent geometrc or exponental varables, possbly wth dfferent

More information

AN ASYMMETRIC GENERALIZED FGM COPULA AND ITS PROPERTIES

AN ASYMMETRIC GENERALIZED FGM COPULA AND ITS PROPERTIES Pa. J. Statst. 015 Vol. 31(1), 95-106 AN ASYMMETRIC GENERALIZED FGM COPULA AND ITS PROPERTIES Berzadeh, H., Parham, G.A. and Zadaram, M.R. Deartment of Statstcs, Shahd Chamran Unversty, Ahvaz, Iran. Corresondng

More information

n ). This is tight for all admissible values of t, k and n. k t + + n t

n ). This is tight for all admissible values of t, k and n. k t + + n t MAXIMIZING THE NUMBER OF NONNEGATIVE SUBSETS NOGA ALON, HAROUT AYDINIAN, AND HAO HUANG Abstract. Gven a set of n real numbers, f the sum of elements of every subset of sze larger than k s negatve, what

More information

HnUf> xk) = S0Jf(xk) (k = 1,..., «; j = 0,..., m - 1).

HnUf> xk) = S0Jf(xk) (k = 1,..., «; j = 0,..., m - 1). PROCEEDINGS of the AMERICAN MATHEMATICAL SOCIETY Volume 09, Number 4, August 990 ON (0,,2) INTERPOLATION IN UNIFORM METRIC J. SZABADOS AND A. K. VARMA (Communcated by R. Danel Mauldn) Abstract. From the

More information

Research Article A Generalized Sum-Difference Inequality and Applications to Partial Difference Equations

Research Article A Generalized Sum-Difference Inequality and Applications to Partial Difference Equations Hndaw Publshng Corporaton Advances n Dfference Equatons Volume 008, Artcle ID 695495, pages do:0.55/008/695495 Research Artcle A Generalzed Sum-Dfference Inequalty and Applcatons to Partal Dfference Equatons

More information

Power law and dimension of the maximum value for belief distribution with the max Deng entropy

Power law and dimension of the maximum value for belief distribution with the max Deng entropy Power law and dmenson of the maxmum value for belef dstrbuton wth the max Deng entropy Bngy Kang a, a College of Informaton Engneerng, Northwest A&F Unversty, Yanglng, Shaanx, 712100, Chna. Abstract Deng

More information

An Upper Bound on SINR Threshold for Call Admission Control in Multiple-Class CDMA Systems with Imperfect Power-Control

An Upper Bound on SINR Threshold for Call Admission Control in Multiple-Class CDMA Systems with Imperfect Power-Control An Upper Bound on SINR Threshold for Call Admsson Control n Multple-Class CDMA Systems wth Imperfect ower-control Mahmoud El-Sayes MacDonald, Dettwler and Assocates td. (MDA) Toronto, Canada melsayes@hotmal.com

More information

Fuzzy Set Approach to Solve Multi-objective Linear plus Fractional Programming Problem

Fuzzy Set Approach to Solve Multi-objective Linear plus Fractional Programming Problem Internatonal Journal of Oeratons Research Vol.8, o. 3, 5-3 () Internatonal Journal of Oeratons Research Fuzzy Set Aroach to Solve Mult-objectve Lnear lus Fractonal Programmng Problem Sanjay Jan Kalash

More information

Anti-van der Waerden numbers of 3-term arithmetic progressions.

Anti-van der Waerden numbers of 3-term arithmetic progressions. Ant-van der Waerden numbers of 3-term arthmetc progressons. Zhanar Berkkyzy, Alex Schulte, and Mchael Young Aprl 24, 2016 Abstract The ant-van der Waerden number, denoted by aw([n], k), s the smallest

More information

Algorithms for factoring

Algorithms for factoring CSA E0 235: Crytograhy Arl 9,2015 Instructor: Arta Patra Algorthms for factorng Submtted by: Jay Oza, Nranjan Sngh Introducton Factorsaton of large ntegers has been a wdely studed toc manly because of

More information

Departure Process from a M/M/m/ Queue

Departure Process from a M/M/m/ Queue Dearture rocess fro a M/M// Queue Q - (-) Q Q3 Q4 (-) Knowledge of the nature of the dearture rocess fro a queue would be useful as we can then use t to analyze sle cases of queueng networs as shown. The

More information

2 S. S. DRAGOMIR, N. S. BARNETT, AND I. S. GOMM Theorem. Let V :(d d)! R be a twce derentable varogram havng the second dervatve V :(d d)! R whch s bo

2 S. S. DRAGOMIR, N. S. BARNETT, AND I. S. GOMM Theorem. Let V :(d d)! R be a twce derentable varogram havng the second dervatve V :(d d)! R whch s bo J. KSIAM Vol.4, No., -7, 2 FURTHER BOUNDS FOR THE ESTIMATION ERROR VARIANCE OF A CONTINUOUS STREAM WITH STATIONARY VARIOGRAM S. S. DRAGOMIR, N. S. BARNETT, AND I. S. GOMM Abstract. In ths paper we establsh

More information

Asymptotics of the Solution of a Boundary Value. Problem for One-Characteristic Differential. Equation Degenerating into a Parabolic Equation

Asymptotics of the Solution of a Boundary Value. Problem for One-Characteristic Differential. Equation Degenerating into a Parabolic Equation Nonl. Analyss and Dfferental Equatons, ol., 4, no., 5 - HIKARI Ltd, www.m-har.com http://dx.do.org/.988/nade.4.456 Asymptotcs of the Soluton of a Boundary alue Problem for One-Characterstc Dfferental Equaton

More information

n α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0

n α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0 MODULE 2 Topcs: Lnear ndependence, bass and dmenson We have seen that f n a set of vectors one vector s a lnear combnaton of the remanng vectors n the set then the span of the set s unchanged f that vector

More information

} Often, when learning, we deal with uncertainty:

} Often, when learning, we deal with uncertainty: Uncertanty and Learnng } Often, when learnng, we deal wth uncertanty: } Incomplete data sets, wth mssng nformaton } Nosy data sets, wth unrelable nformaton } Stochastcty: causes and effects related non-determnstcally

More information

System in Weibull Distribution

System in Weibull Distribution Internatonal Matheatcal Foru 4 9 no. 9 94-95 Relablty Equvalence Factors of a Seres-Parallel Syste n Webull Dstrbuton M. A. El-Dacese Matheatcs Departent Faculty of Scence Tanta Unversty Tanta Egypt eldacese@yahoo.co

More information

Mixture of Gaussians Expectation Maximization (EM) Part 2

Mixture of Gaussians Expectation Maximization (EM) Part 2 Mture of Gaussans Eectaton Mamaton EM Part 2 Most of the sldes are due to Chrstoher Bsho BCS Summer School Eeter 2003. The rest of the sldes are based on lecture notes by A. Ng Lmtatons of K-means Hard

More information

A new construction of 3-separable matrices via an improved decoding of Macula s construction

A new construction of 3-separable matrices via an improved decoding of Macula s construction Dscrete Optmzaton 5 008 700 704 Contents lsts avalable at ScenceDrect Dscrete Optmzaton journal homepage: wwwelsevercom/locate/dsopt A new constructon of 3-separable matrces va an mproved decodng of Macula

More information

Existence results for a fourth order multipoint boundary value problem at resonance

Existence results for a fourth order multipoint boundary value problem at resonance Avalable onlne at www.scencedrect.com ScenceDrect Journal of the Ngeran Mathematcal Socety xx (xxxx) xxx xxx www.elsever.com/locate/jnnms Exstence results for a fourth order multpont boundary value problem

More information