New Formulas for Semi-Primes. Testing, Counting and Identification of the n th

Size: px
Start display at page:

Download "New Formulas for Semi-Primes. Testing, Counting and Identification of the n th"

Transcription

1 Iteratoal Joural of Comuter ad Iformato Techology (ISS: ) Volume 06 Issue 01, Jauary 017 ew Formulas for Sem-Prmes. Testg, Coutg ad Idetfcato of e ad et Sem-Prmes Issam Kaddoura, Khadja Al-Akhrass Deartmet of Maematcs, school of arts ad sceces Lebaese Iteratoal Uversty Sada, Lebao Samh Abdul-ab Deartmet of comuters ad commucatos egeerg, Lebaese Iteratoal Uversty Berut, Lebao Emal: Samh.abdulab [AT] lu.edu.lb Abstract I s aer we gve a ew semrmalty test ad we costruct a ew formula for () ( ), e fucto at couts e umber of semrmes ot eceedg a gve umber. We also reset ew formulas to detfy e semrme ad e et semrme to a gve umber. The ew formulas are based o e kowledge of e rmes less a or equal to e cube roots of : P1, P... P. Keywords-comoet; rme, semrme, semrme I. ITRODUCTIO semrme, et Securg data remas a cocer for every dvdual ad every orgazato o e globe. I telecommucato, crytograhy s oe of e studes at ermt e secure trasfer of formato [1] over e Iteret. Prme umbers have secal roertes at make em of fudametal mortace crytograhy. The core of e Iteret securty s based o rotocols, such as SSL ad TSL [] released 1994 ad ersst as e bass for securg dfferet asects of today's Iteret []. The Rvest-Shamr-Adlema ecryto meod [4], released 1978, uses asymmetrc keys for echagg data. A secret key S k ad a ublc key P k are geerated by e recet w e followg roerty: A message echered by P k ca oly be dechered by S k ad vce versa. The ublc key s ublcly trasmtted to e seder ad used to echer data at oly e recet ca decher. RSA s based o geeratg two large rme umbers, say P ad Q ad ts securty s eforced by e fact at albet e fact at e roduct of ese two rmes P Q s ublshed, t s of eormous dffculty to factorze. A semrme or ( almost rme) or (q umber) s a atural umber at s a roduct of rmes ot ecessary dstct. The semrme s eer a square of rme or square free. Also e square of ay rme umber s a semrme umber. Maematcas have bee terested may asects of e semrme umbers. I [5] auors derve a robablstc fucto g( y ) for a umber y to be semrme ad a asymtotc formula for coutg g( y ) whe y s very large. I [6] auors are terested factorzg semrmes ad use a aromato to ( ) e fucto at couts e rme umbers. Whle maematcas have acheved may mortat results cocerg dstrbuto of rme umbers, may are terested semrme roertes as to coutg rme ad semrme umbers ot eceedg a gve umber. () From [7, 8, 9], e formula for ( ) at couts e semrme umbers ot eceedg s gve by (1). P () ( ) 1 1 (1) Ths formula s based o e rmes P 1, P., Our cotrbuto s of several folds. Frst, we reset a formula to test e semrmalty of a gve teger, s formula s used to buld a ew fucto () ( ) at couts e semrmes ot eceedg a gve teger usg 1 oly P, P,... P. Secod, we reset a elct formula at detfes e semrme umber. Ad fally we gve a formula at fds e et semrme to ay gve umber. II. SEMIPRIMALITY TEST W e same comlety O( ) as e Seve of Eratosees to test a rmalty of a gve umber, we emloy e Eucldea Algorm ad e fact at every rme umber greater a has e form 6k 1 ad wout 49

2 Iteratoal Joural of Comuter ad Iformato Techology (ISS: ) Volume 06 Issue 01, Jauary 017 revous kowledge about ay rme, we ca test e rmalty of 8 usg e followg rocedure: Defe e followg fuctos 1 T0 ( ) 1 T1 ( ) 6 1 T ( ) 6 6 6k1 6k1 k 1 () () 6 6k1 6k1 (4) k 1 T0 T1 T T( ) Where ad are e floor ad e celg fuctos of e real umber resectvely. We have e followg eorem whch s aalog to at aeared [10] w slght modfcato ad e detals of e roof are eactly e same. (5) Theorem 1: Gve ay ostve teger 7, e 1. s rme f ad oly f T( ) 1. s comoste f ad oly f T( ) 0. For (6) ( ) 4 T(6 j 7) T(6 j 5) j1 j1 couts e umber of rmes ot eceedg. ow we roof e followg Lemma: Lemma 1: If s a ostve teger w at least factors, e ere ests a rme such at: ad dvdes Proof. If has at least factors e t ca be rereseted as: a.. b c w e assumto 1 a b c, we deduce at a or a. By e fudametal eorem of armetc, a rme umber such at dvdes a. That meas a, but dvdes a ad a dvdes, hece dvdes w e roerty. Lemma 1: tells at, f s ot dvsble by ay rme, e has at most rme factors,.e., s rme or semrme. Usg e roosed rmalty test defed by T( ) we costruct e semrmalty test as follows: For 8, defe e fuctos K 1 ( ) ad K ( ) as follows: K ( ) 1 = 1 ( ) 1 (7) K ( ) = 1 1 T ( ) 1 (8) Where ( ) s e classcal rme coutg fucto reseted (6), T( ) s e same as Theorem 1. Obvously T( ) s deedet of ay revous kowledge of e rme umbers. Lemma : If K1 ( ) 0, e s dvsble by some rme. Proof. For K1 ( ) 0, we have 0 for some, e s dvsble by for some. rme Lemma : If K1 ( ) 1,e has at most rme factors eceedg. Proof. If K ( ) 1 1, e 1 for all erefore by Lemma 1:, s ot dvsble by ay eceedg., erefore has at most two rme factors Lemma 4: If T( ) 0 ad K1 ( ) 1, e s semrme ad K ( ) 0. Proof. If K 1 ( ) 1, e has at most rme factors but T( ) 0 whch meas at s comoste, hece has eactly two rme factors ad bo factors are greater a 50

3 Iteratoal Joural of Comuter ad Iformato Techology (ISS: ) Volume 06 Issue 01, Jauary 017 ad 1 0 erefore K ( ) 0. for each rme, Lemma 5: If T( ) 0 ad K1 ( ) 0, e s a semrme umber f ad oly f K ( ) T( ) 0 ad K 1 ( ) 1 or. T( ) 0, K 1 ( ) 0 ad K ( ) 1 Proof. If s semrme, e q where ad q are two rmes. If ad q bo are greater a e T( ) 0 ad q q q q 1 q 1 K1( q) 1 q ' f q where ' ad ad q' q ' are two rmes such at e T( ) 0 ad Fgure 1: MATLAB code for e comutato of K 1 ad K Proof. If T( ) 0 ad K1 ( ) 0 e dvdes a rme, but s semrme at meas q ad q s rme umber hece for rme ad q we have: q q q 1 T 1 T 1 Cosequetly, K ( ) 1 because at least oe of e terms s ot zero. Coversely, f K ( ) 1 e 1T s ot zero for some ad e q ad T 1for some q rme e T T( q) 1 hece q s a rme umber ad s a semrme umber. Fgure 1 shows e MATLAB code for e comutato of K 1 ad K. We are ow a osto to rove e followg eorem at characterzes e semrme umbers. Theorem : (Semrmalty Test): Gve ay ostve teger 7, e s semrme f ad oly f: q ' ' 1 ' q ' ' q ' K1( ' q ') 0 ' ' q ' ' 1 q ' ' ' q ' because 0 ad ' ' q ' ' 1 ' q ' ' q ' ' q ' K( ' q ') 1 T 1 ' ' ' q ' ' 1 ' q ' ' q ' ' q ' because 1 T( ) q' q' 1 T( q') 1. ' ' ' The coverse ca be roved by e same argumets. Corollary 1 A ostve teger 7 s semrme f ad oly f K ( ) K ( ) T ( ) 1. 1 Proof. A drect cosequece of e revous eorem ad lemmas. III. SEMIPRIME COUTIG FUCTIO otce at e trle ( T ( ), K1( ), K( )) have oly e followg 4 ossble cases oly: Case 1. ( T ( ), K1( ), K( )) (1,1,0) dcates at s rme umber. Case. ( T ( ), K1( ), K( )) (0,1,0) dcates at s semrme e form q where ad q are rmes at q. ad 51

4 Iteratoal Joural of Comuter ad Iformato Techology (ISS: ) Volume 06 Issue 01, Jauary 017 Case. ( T ( ), K1( ), K( )) (0,0,1) dcates at s semrme e form q where ad q are rmes such at ad q. Case 4. ( T ( ), K1( ), K( )) (0,0,0) dcates at has at least rme factors. Usg e revous observatos, lemmas as well as Theorem : ad corollary, we rove e followg eorem at cludes a fucto at couts all semrmes ot eceedg a gve umber. () Theorem : For 8 e, 1 (9) 8 ( ) ( K ( ) K ( ) T( )) s a fucto at couts all semrmes ot eceedg. Fgure shows e MATLAB code for comutato. Kowg at e boud of e rme s P log [11], we ca say at e P 4 log. semrme s Theorem 4: For 8 ad, s e semrme s gve by e formula: 4l 4l s 8 8 () 8 1 ( ) 8 K1( m) K( m) T ( m) m8 The formula full s gve by: 4 l s 8 m m 1 m m 1 m m m 1 T T ( m) m 8 m 1 m 1 8 where Tm ( ) s gve by T0 ( m) T1 ( m) T ( m) Tm ( ) Fgure : MATLAB code for IV. SEMIPRIME FORMULA The frst few semrmes ascedg order are s1 4, s 6, s 9, s4 10, s5 14, s6 15, s7 1, etc. We defe e fucto 1,,... ad 0,1,,... clearly G(, ) 1 1 G(, ) 1 0 where m m m m m m 1 m m 1 m m m k1 6k 1 6k 1 m k1 6k 1 6k 1 % 6 6 Proof. For e semrme s, () ( s ) ad () () for s, ( ) ( s ) 1,,,...,. Usg e roertes of e fucto we comute 1 G(, ) 1 0 4l 4l () 8 8 G(, ( )) () 8 % 1 ( ) 8 () () 8 G(, (8)) G(, (9)) G G P G () () (, (10))... (, ( 1))... () (, ( P 1 1)) () ()... G(, ( P) G(, ( P 1) s where e last 1 e summato s e value of () G(, ( s 1)) ad e followed by () G(, ( s ) G(, ) 0 followed by zeroes for e rest terms of e summato, hece 4l 4l s 8 G(, ( )) 8 () ( ) 5

5 Iteratoal Joural of Comuter ad Iformato Techology (ISS: ) Volume 06 Issue 01, Jauary 017 As a eamle, comutg e 5 semrme umber gves s as show Table 1. (8) (9) (10) 4 (11) 4 (1) 4 (1) 4 (14) 5 G(5, (8)) 1 G(5, (9)) 1 G(5, (10)) 1 G(5, (11)) 1 G(5, (1)) 1 G(5, (1)) 1 G(5, (14)) 0 Table 1: Comutg e 5 semrme Fgure shows e MATLAB code for e comutato. semrme ow we troduce a algorm at comutes e et semrme to ay gve ostve teger. Theorem 5: If s ay ostve teger greater a 8 e e et semrme to s gve by: etsp( ) 1 1 T( ) K ( ) K ( ) (10) where T ( ), K1( ), K( ) are e fuctos defed Secto. Proof. We comute e summato: 1 1 (1 T ( ) K1( ) K( )) etsp( ) 1 (1 T ( ) K1( ) K( )) 1 1 etsp( ) 1 etp( ) 1 1 etp( ) etsp( ) 1 hese (1 T ( ) K1( ) K( )) (1) (0) etsp( ) 1 (1 T( ) K1( ) K( )) 1 1 Fgure : MATLAB code for V. EXT SEMIPRIE s I our revous work [10], we troduced a formula at fds e et rme to a gve umber. I s secto, we use a ehacemet formula to fd e et rme to a gve umber ad we troduce a formula to comute e et semrme to ay gve umber. Recall at e teger 8 s a semrme umber f ad oly f K1( ) K( ) T ( ) 1 ad f s ot semrme e K1( ) K( ) T ( ) 0. () Tme secods Table : Testg o VI. RESULTS () ( ) We mlemeted e roosed fuctos usg MATLAB ad comlete e testg o a Itel Core K w 8M cache ad a clock seed of 4.0GHz. Table shows e results related to () for some selected values of. We have also comuted few Table. semrmes as show 5

6 Iteratoal Joural of Comuter ad Iformato Techology (ISS: ) Volume 06 Issue 01, Jauary 017 s Tme secods Table : Testg o semrmes Ad fally we show e et semrmes to some selected tegers Table 4. etsp( ) Tme secods Table 4: Testg o etsp( ) semrmes VII. COCLUSIO I s work, we reseted ew formulas for semrmes. Frst, () ( ) at couts e umber of semrmes ot eceedg a gve umber. Our roosed formula requres kowg oly e rmes at are less or equal whle estg formulas requre at least kowg e rmes at are less or equal. We also reset a ew formula to detfy e semrme ad fally, a ew formula at gves e et semrme to ay teger. REFERECES [1] Stadard secfcatos for ublc key crytograhy (16), , [Ole]. Avalable: htt://grouer.eee.org/grous/16/ [] E. Rescorla, SSL ad TLS: desgg ad buldg secure systems. Addso-Wesley Readg, 001, vol. 1. [] J. Clark ad P. C. va Oorschot, Sok: Ssl ad htts: Revstg ast challeges ad evaluatg certfcate trust model ehacemets, Securty ad Prvacy (SP), 01 IEEE Symosum o. IEEE, 01, [4] R. L. Rvest, A. Shamr, ad L. Adlema, A meod for obtag dgtal sgatures ad ublc-key crytosystems, Commucatos of e ACM, vol. 1, o., , [5] S. Ishmukhametov ad F. F. Sharfulla, O a dstrubuto of semrme umbers, Izvestya Vysshkh Uchebykh Zavede. Matematka, o. 8,. 5 59, 014. [6] R. Doss, A aromato for euler h, orcetral Uversty Prescott Valley, Uted States o, 01. [7] E. W. Wesste, Semrme, Wolfram Research, Ic., 00. [8] J. H. Coway, H. Detrch, ad E. A. Bre, Coutg grous: gus, moas ad oer eotca, Ma. Itellgecer, vol. 0, o.,. 6 15, 008. [9] D. A. Goldsto, S. Graham, J. Ptz, ad C. Y. Yldrm, Small gas betwee rmes or almost rmes, Trasactos of e Amerca Maematcal Socety, , 009. [10] I. Kaddoura ad S. Abdul-ab, O formula to comute rmes ad e rme, Aled Maematcal Sceces, vol. 6, o. 76, , 01. [11] G. Rob, Estmato de la focto de tchebychef Ө sur le k-ème ombre remer et grades valeurs de la focto w() ombre de dvseurs remers de, Acta Armetca, vol. 4, o. 4, ,

Factorization of Finite Abelian Groups

Factorization of Finite Abelian Groups Iteratoal Joural of Algebra, Vol 6, 0, o 3, 0-07 Factorzato of Fte Abela Grous Khald Am Uversty of Bahra Deartmet of Mathematcs PO Box 3038 Sakhr, Bahra kamee@uobedubh Abstract If G s a fte abela grou

More information

Introducing Sieve of Eratosthenes as a Theorem

Introducing Sieve of Eratosthenes as a Theorem ISSN(Ole 9-8 ISSN (Prt - Iteratoal Joural of Iovatve Research Scece Egeerg ad echolog (A Hgh Imact Factor & UGC Aroved Joural Webste wwwrsetcom Vol Issue 9 Setember Itroducg Seve of Eratosthees as a heorem

More information

2. Independence and Bernoulli Trials

2. Independence and Bernoulli Trials . Ideedece ad Beroull Trals Ideedece: Evets ad B are deedet f B B. - It s easy to show that, B deedet mles, B;, B are all deedet ars. For examle, ad so that B or B B B B B φ,.e., ad B are deedet evets.,

More information

Comparing Different Estimators of three Parameters for Transmuted Weibull Distribution

Comparing Different Estimators of three Parameters for Transmuted Weibull Distribution Global Joural of Pure ad Appled Mathematcs. ISSN 0973-768 Volume 3, Number 9 (207), pp. 55-528 Research Ida Publcatos http://www.rpublcato.com Comparg Dfferet Estmators of three Parameters for Trasmuted

More information

Computations with large numbers

Computations with large numbers Comutatos wth large umbers Wehu Hog, Det. of Math, Clayto State Uversty, 2 Clayto State lvd, Morrow, G 326, Mgshe Wu, Det. of Mathematcs, Statstcs, ad Comuter Scece, Uversty of Wscos-Stout, Meomoe, WI

More information

Semi-Riemann Metric on. the Tangent Bundle and its Index

Semi-Riemann Metric on. the Tangent Bundle and its Index t J Cotem Math Sceces ol 5 o 3 33-44 Sem-Rema Metrc o the Taet Budle ad ts dex smet Ayha Pamuale Uversty Educato Faculty Dezl Turey ayha@auedutr Erol asar Mers Uversty Art ad Scece Faculty 33343 Mers Turey

More information

FACTORIZATION NUMBERS OF FINITE ABELIAN GROUPS. Communicated by Bernhard Amberg. 1. Introduction

FACTORIZATION NUMBERS OF FINITE ABELIAN GROUPS. Communicated by Bernhard Amberg. 1. Introduction Iteratoal Joural of Grou Theory ISSN (rt): 2251-7650, ISSN (o-le): 2251-7669 Vol 2 No 2 (2013), 1-8 c 2013 Uversty of Isfaha wwwtheoryofgrousr wwwuacr FACTORIZATION NUMBERS OF FINITE ABELIAN GROUPS M FARROKHI

More information

THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Series A, OF THE ROMANIAN ACADEMY Volume 9, Number 3/2008, pp

THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Series A, OF THE ROMANIAN ACADEMY Volume 9, Number 3/2008, pp THE PUBLISHIN HOUSE PROCEEDINS OF THE ROMANIAN ACADEMY, Seres A, OF THE ROMANIAN ACADEMY Volume 9, Number 3/8, THE UNITS IN Stela Corelu ANDRONESCU Uversty of Pteşt, Deartmet of Mathematcs, Târgu Vale

More information

Channel Models with Memory. Channel Models with Memory. Channel Models with Memory. Channel Models with Memory

Channel Models with Memory. Channel Models with Memory. Channel Models with Memory. Channel Models with Memory Chael Models wth Memory Chael Models wth Memory Hayder radha Electrcal ad Comuter Egeerg Mchga State Uversty I may ractcal etworkg scearos (cludg the Iteret ad wreless etworks), the uderlyg chaels are

More information

2SLS Estimates ECON In this case, begin with the assumption that E[ i

2SLS Estimates ECON In this case, begin with the assumption that E[ i SLS Estmates ECON 3033 Bll Evas Fall 05 Two-Stage Least Squares (SLS Cosder a stadard lear bvarate regresso model y 0 x. I ths case, beg wth the assumto that E[ x] 0 whch meas that OLS estmates of wll

More information

Quantum Plain and Carry Look-Ahead Adders

Quantum Plain and Carry Look-Ahead Adders Quatum Pla ad Carry Look-Ahead Adders Ka-We Cheg u8984@cc.kfust.edu.tw Che-Cheg Tseg tcc@ccms.kfust.edu.tw Deartmet of Comuter ad Commucato Egeerg, Natoal Kaohsug Frst Uversty of Scece ad Techology, Yechao,

More information

K-Even Edge-Graceful Labeling of Some Cycle Related Graphs

K-Even Edge-Graceful Labeling of Some Cycle Related Graphs Iteratoal Joural of Egeerg Scece Iveto ISSN (Ole): 9 7, ISSN (Prt): 9 7 www.jes.org Volume Issue 0ǁ October. 0 ǁ PP.0-7 K-Eve Edge-Graceful Labelg of Some Cycle Related Grahs Dr. B. Gayathr, S. Kousalya

More information

Minimizing Total Completion Time in a Flow-shop Scheduling Problems with a Single Server

Minimizing Total Completion Time in a Flow-shop Scheduling Problems with a Single Server Joural of Aled Mathematcs & Boformatcs vol. o.3 0 33-38 SSN: 79-660 (rt) 79-6939 (ole) Sceress Ltd 0 Mmzg Total omleto Tme a Flow-sho Schedulg Problems wth a Sgle Server Sh lg ad heg xue-guag Abstract

More information

Asymptotic Formulas Composite Numbers II

Asymptotic Formulas Composite Numbers II Iteratoal Matematcal Forum, Vol. 8, 203, o. 34, 65-662 HIKARI Ltd, www.m-kar.com ttp://d.do.org/0.2988/mf.203.3854 Asymptotc Formulas Composte Numbers II Rafael Jakmczuk Dvsó Matemátca, Uversdad Nacoal

More information

On the introductory notes on Artin s Conjecture

On the introductory notes on Artin s Conjecture O the troductory otes o Art s Cojecture The urose of ths ote s to make the surveys [5 ad [6 more accessble to bachelor studets. We rovde some further relmares ad some exercses. We also reset the calculatos

More information

Econometric Methods. Review of Estimation

Econometric Methods. Review of Estimation Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators

More information

Modified Cosine Similarity Measure between Intuitionistic Fuzzy Sets

Modified Cosine Similarity Measure between Intuitionistic Fuzzy Sets Modfed ose mlarty Measure betwee Itutostc Fuzzy ets hao-mg wag ad M-he Yag,* Deartmet of led Mathematcs, hese ulture Uversty, Tae, Tawa Deartmet of led Mathematcs, hug Yua hrsta Uversty, hug-l, Tawa msyag@math.cycu.edu.tw

More information

STK3100 and STK4100 Autumn 2018

STK3100 and STK4100 Autumn 2018 SK3 ad SK4 Autum 8 Geeralzed lear models Part III Covers the followg materal from chaters 4 ad 5: Cofdece tervals by vertg tests Cosder a model wth a sgle arameter β We may obta a ( α% cofdece terval for

More information

On the Rational Valued Characters Table of the

On the Rational Valued Characters Table of the Aled Mathematcal Sceces, Vol., 7, o. 9, 95-9 HIKARI Ltd, www.m-hkar.com htts://do.or/.9/ams.7.7576 O the Ratoal Valued Characters Table of the Grou (Q m C Whe m s a Eve Number Raaa Hassa Abass Deartmet

More information

MATH 371 Homework assignment 1 August 29, 2013

MATH 371 Homework assignment 1 August 29, 2013 MATH 371 Homework assgmet 1 August 29, 2013 1. Prove that f a subset S Z has a smallest elemet the t s uque ( other words, f x s a smallest elemet of S ad y s also a smallest elemet of S the x y). We kow

More information

STK3100 and STK4100 Autumn 2017

STK3100 and STK4100 Autumn 2017 SK3 ad SK4 Autum 7 Geeralzed lear models Part III Covers the followg materal from chaters 4 ad 5: Sectos 4..5, 4.3.5, 4.3.6, 4.4., 4.4., ad 4.4.3 Sectos 5.., 5.., ad 5.5. Ørulf Borga Deartmet of Mathematcs

More information

Lecture 9. Some Useful Discrete Distributions. Some Useful Discrete Distributions. The observations generated by different experiments have

Lecture 9. Some Useful Discrete Distributions. Some Useful Discrete Distributions. The observations generated by different experiments have NM 7 Lecture 9 Some Useful Dscrete Dstrbutos Some Useful Dscrete Dstrbutos The observatos geerated by dfferet eermets have the same geeral tye of behavor. Cosequetly, radom varables assocated wth these

More information

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b CS 70 Dscrete Mathematcs ad Probablty Theory Fall 206 Sesha ad Walrad DIS 0b. Wll I Get My Package? Seaky delvery guy of some compay s out delverg packages to customers. Not oly does he had a radom package

More information

Non-uniform Turán-type problems

Non-uniform Turán-type problems Joural of Combatoral Theory, Seres A 111 2005 106 110 wwwelsevercomlocatecta No-uform Turá-type problems DhruvMubay 1, Y Zhao 2 Departmet of Mathematcs, Statstcs, ad Computer Scece, Uversty of Illos at

More information

Two Fuzzy Probability Measures

Two Fuzzy Probability Measures Two Fuzzy robablty Measures Zdeěk Karíšek Isttute of Mathematcs Faculty of Mechacal Egeerg Bro Uversty of Techology Techcká 2 66 69 Bro Czech Reublc e-mal: karsek@umfmevutbrcz Karel Slavíček System dmstrato

More information

STRONG CONSISTENCY FOR SIMPLE LINEAR EV MODEL WITH v/ -MIXING

STRONG CONSISTENCY FOR SIMPLE LINEAR EV MODEL WITH v/ -MIXING Joural of tatstcs: Advaces Theory ad Alcatos Volume 5, Number, 6, Pages 3- Avalable at htt://scetfcadvaces.co. DOI: htt://d.do.org/.864/jsata_7678 TRONG CONITENCY FOR IMPLE LINEAR EV MODEL WITH v/ -MIXING

More information

Unit 9. The Tangent Bundle

Unit 9. The Tangent Bundle Ut 9. The Taget Budle ========================================================================================== ---------- The taget sace of a submafold of R, detfcato of taget vectors wth dervatos at

More information

Measures of Entropy based upon Statistical Constants

Measures of Entropy based upon Statistical Constants Proceedgs of the World Cogress o Egeerg 07 Vol I WCE 07, July 5-7, 07, Lodo, UK Measures of Etroy based uo Statstcal Costats GSButtar, Member, IAENG Abstract---The reset artcle deals wth mortat vestgatos

More information

Application of Generating Functions to the Theory of Success Runs

Application of Generating Functions to the Theory of Success Runs Aled Mathematcal Sceces, Vol. 10, 2016, o. 50, 2491-2495 HIKARI Ltd, www.m-hkar.com htt://dx.do.org/10.12988/ams.2016.66197 Alcato of Geeratg Fuctos to the Theory of Success Rus B.M. Bekker, O.A. Ivaov

More information

Sebastián Martín Ruiz. Applications of Smarandache Function, and Prime and Coprime Functions

Sebastián Martín Ruiz. Applications of Smarandache Function, and Prime and Coprime Functions Sebastá Martí Ruz Alcatos of Saradache Fucto ad Pre ad Core Fuctos 0 C L f L otherwse are core ubers Aerca Research Press Rehoboth 00 Sebastá Martí Ruz Avda. De Regla 43 Choa 550 Cadz Sa Sarada@telele.es

More information

ON BIVARIATE GEOMETRIC DISTRIBUTION. K. Jayakumar, D.A. Mundassery 1. INTRODUCTION

ON BIVARIATE GEOMETRIC DISTRIBUTION. K. Jayakumar, D.A. Mundassery 1. INTRODUCTION STATISTICA, ao LXVII, 4, 007 O BIVARIATE GEOMETRIC DISTRIBUTIO ITRODUCTIO Probablty dstrbutos of radom sums of deedetly ad detcally dstrbuted radom varables are maly aled modelg ractcal roblems that deal

More information

Establishing Relations among Various Measures by Using Well Known Inequalities

Establishing Relations among Various Measures by Using Well Known Inequalities Iteratoal OPEN ACCESS Joural Of Moder Egeerg Research (IJMER) Establshg Relatos amog Varous Measures by Usg Well Kow Ieualtes K. C. Ja, Prahull Chhabra, Deartmet of Mathematcs, Malavya Natoal Isttute of

More information

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur

More information

Random Variables. ECE 313 Probability with Engineering Applications Lecture 8 Professor Ravi K. Iyer University of Illinois

Random Variables. ECE 313 Probability with Engineering Applications Lecture 8 Professor Ravi K. Iyer University of Illinois Radom Varables ECE 313 Probablty wth Egeerg Alcatos Lecture 8 Professor Rav K. Iyer Uversty of Illos Iyer - Lecture 8 ECE 313 Fall 013 Today s Tocs Revew o Radom Varables Cumulatve Dstrbuto Fucto (CDF

More information

Evaluating Polynomials

Evaluating Polynomials Uverst of Nebraska - Lcol DgtalCommos@Uverst of Nebraska - Lcol MAT Exam Expostor Papers Math the Mddle Isttute Partershp 7-7 Evaluatg Polomals Thomas J. Harrgto Uverst of Nebraska-Lcol Follow ths ad addtoal

More information

On the characteristics of partial differential equations

On the characteristics of partial differential equations Sur les caractérstques des équatos au dérvées artelles Bull Soc Math Frace 5 (897) 8- O the characterstcs of artal dfferetal equatos By JULES BEUDON Traslated by D H Delhech I a ote that was reseted to

More information

Lecture 9: Tolerant Testing

Lecture 9: Tolerant Testing Lecture 9: Tolerat Testg Dael Kae Scrbe: Sakeerth Rao Aprl 4, 07 Abstract I ths lecture we prove a quas lear lower boud o the umber of samples eeded to do tolerat testg for L dstace. Tolerat Testg We have

More information

A New Measure of Probabilistic Entropy. and its Properties

A New Measure of Probabilistic Entropy. and its Properties Appled Mathematcal Sceces, Vol. 4, 200, o. 28, 387-394 A New Measure of Probablstc Etropy ad ts Propertes Rajeesh Kumar Departmet of Mathematcs Kurukshetra Uversty Kurukshetra, Ida rajeesh_kuk@redffmal.com

More information

CIS 800/002 The Algorithmic Foundations of Data Privacy October 13, Lecture 9. Database Update Algorithms: Multiplicative Weights

CIS 800/002 The Algorithmic Foundations of Data Privacy October 13, Lecture 9. Database Update Algorithms: Multiplicative Weights CIS 800/002 The Algorthmc Foudatos of Data Prvacy October 13, 2011 Lecturer: Aaro Roth Lecture 9 Scrbe: Aaro Roth Database Update Algorthms: Multplcatve Weghts We ll recall aga) some deftos from last tme:

More information

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information Malaysa Joural of Mathematcal Sceces (): 97- (9) Bayes Estmator for Expoetal Dstrbuto wth Exteso of Jeffery Pror Iformato Hadeel Salm Al-Kutub ad Noor Akma Ibrahm Isttute for Mathematcal Research, Uverst

More information

Patterns of Continued Fractions with a Positive Integer as a Gap

Patterns of Continued Fractions with a Positive Integer as a Gap IOSR Jourl of Mthemtcs (IOSR-JM) e-issn: 78-578, -ISSN: 39-765X Volume, Issue 3 Ver III (My - Ju 6), PP -5 wwwosrjourlsorg Ptters of Cotued Frctos wth Postve Iteger s G A Gm, S Krth (Mthemtcs, Govermet

More information

STK4011 and STK9011 Autumn 2016

STK4011 and STK9011 Autumn 2016 STK4 ad STK9 Autum 6 Pot estmato Covers (most of the followg materal from chapter 7: Secto 7.: pages 3-3 Secto 7..: pages 3-33 Secto 7..: pages 35-3 Secto 7..3: pages 34-35 Secto 7.3.: pages 33-33 Secto

More information

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted

More information

Chain Rules for Entropy

Chain Rules for Entropy Cha Rules for Etroy The etroy of a collecto of radom varables s the sum of codtoal etroes. Theorem: Let be radom varables havg the mass robablty x x.x. The...... The roof s obtaed by reeatg the alcato

More information

Laboratory I.10 It All Adds Up

Laboratory I.10 It All Adds Up Laboratory I. It All Adds Up Goals The studet wll work wth Rema sums ad evaluate them usg Derve. The studet wll see applcatos of tegrals as accumulatos of chages. The studet wll revew curve fttg sklls.

More information

Q-analogue of a Linear Transformation Preserving Log-concavity

Q-analogue of a Linear Transformation Preserving Log-concavity Iteratoal Joural of Algebra, Vol. 1, 2007, o. 2, 87-94 Q-aalogue of a Lear Trasformato Preservg Log-cocavty Daozhog Luo Departmet of Mathematcs, Huaqao Uversty Quazhou, Fua 362021, P. R. Cha ldzblue@163.com

More information

Median as a Weighted Arithmetic Mean of All Sample Observations

Median as a Weighted Arithmetic Mean of All Sample Observations Meda as a Weghted Arthmetc Mea of All Sample Observatos SK Mshra Dept. of Ecoomcs NEHU, Shllog (Ida). Itroducto: Iumerably may textbooks Statstcs explctly meto that oe of the weakesses (or propertes) of

More information

Bounds for the Connective Eccentric Index

Bounds for the Connective Eccentric Index It. J. Cotemp. Math. Sceces, Vol. 7, 0, o. 44, 6-66 Bouds for the Coectve Eccetrc Idex Nlaja De Departmet of Basc Scece, Humates ad Socal Scece (Mathematcs Calcutta Isttute of Egeerg ad Maagemet Kolkata,

More information

A Note on Ratio Estimators in two Stage Sampling

A Note on Ratio Estimators in two Stage Sampling Iteratoal Joural of Scetfc ad Research Publcatos, Volume, Issue, December 0 ISS 0- A ote o Rato Estmators two Stage Samplg Stashu Shekhar Mshra Lecturer Statstcs, Trdet Academy of Creatve Techology (TACT),

More information

On generalized fuzzy mean code word lengths. Department of Mathematics, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

On generalized fuzzy mean code word lengths. Department of Mathematics, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India merca Joural of ppled Mathematcs 04; (4): 7-34 Publshed ole ugust 30, 04 (http://www.scecepublshggroup.com//aam) do: 0.648/.aam.04004.3 ISSN: 330-0043 (Prt); ISSN: 330-006X (Ole) O geeralzed fuzzy mea

More information

COMPUTERISED ALGEBRA USED TO CALCULATE X n COST AND SOME COSTS FROM CONVERSIONS OF P-BASE SYSTEM WITH REFERENCES OF P-ADIC NUMBERS FROM

COMPUTERISED ALGEBRA USED TO CALCULATE X n COST AND SOME COSTS FROM CONVERSIONS OF P-BASE SYSTEM WITH REFERENCES OF P-ADIC NUMBERS FROM U.P.B. Sc. Bull., Seres A, Vol. 68, No. 3, 6 COMPUTERISED ALGEBRA USED TO CALCULATE X COST AND SOME COSTS FROM CONVERSIONS OF P-BASE SYSTEM WITH REFERENCES OF P-ADIC NUMBERS FROM Z AND Q C.A. MURESAN Autorul

More information

Entropy, Relative Entropy and Mutual Information

Entropy, Relative Entropy and Mutual Information Etro Relatve Etro ad Mutual Iformato rof. Ja-Lg Wu Deartmet of Comuter Scece ad Iformato Egeerg Natoal Tawa Uverst Defto: The Etro of a dscrete radom varable s defed b : base : 0 0 0 as bts 0 : addg terms

More information

Analysis of Lagrange Interpolation Formula

Analysis of Lagrange Interpolation Formula P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal

More information

IS 709/809: Computational Methods in IS Research. Simple Markovian Queueing Model

IS 709/809: Computational Methods in IS Research. Simple Markovian Queueing Model IS 79/89: Comutatoal Methods IS Research Smle Marova Queueg Model Nrmalya Roy Deartmet of Iformato Systems Uversty of Marylad Baltmore Couty www.umbc.edu Queueg Theory Software QtsPlus software The software

More information

CS 2750 Machine Learning Lecture 5. Density estimation. Density estimation

CS 2750 Machine Learning Lecture 5. Density estimation. Density estimation CS 750 Mache Learg Lecture 5 esty estmato Mlos Hausrecht mlos@tt.edu 539 Seott Square esty estmato esty estmato: s a usuervsed learg roblem Goal: Lear a model that rereset the relatos amog attrbutes the

More information

= lim. (x 1 x 2... x n ) 1 n. = log. x i. = M, n

= lim. (x 1 x 2... x n ) 1 n. = log. x i. = M, n .. Soluto of Problem. M s obvously cotuous o ], [ ad ], [. Observe that M x,..., x ) M x,..., x ) )..) We ext show that M s odecreasg o ], [. Of course.) mles that M s odecreasg o ], [ as well. To show

More information

PTAS for Bin-Packing

PTAS for Bin-Packing CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,

More information

1 Edge Magic Labeling for Special Class of Graphs

1 Edge Magic Labeling for Special Class of Graphs S.Srram et. al. / Iteratoal Joural of Moder Sceces ad Egeerg Techology (IJMSET) ISSN 349-3755; Avalable at https://www.jmset.com Volume, Issue 0, 05, pp.60-67 Edge Magc Labelg for Specal Class of Graphs

More information

TESTS BASED ON MAXIMUM LIKELIHOOD

TESTS BASED ON MAXIMUM LIKELIHOOD ESE 5 Toy E. Smth. The Basc Example. TESTS BASED ON MAXIMUM LIKELIHOOD To llustrate the propertes of maxmum lkelhood estmates ad tests, we cosder the smplest possble case of estmatg the mea of the ormal

More information

L Inequalities for Polynomials

L Inequalities for Polynomials Aled Mathematcs 3-38 do:436/am338 Publshed Ole March (htt://wwwscrorg/oural/am) L Iequaltes for Polyomals Abstract Abdul A Nsar A Rather Deartmet of Mathematcs Kashmr Uversty Sragar Ida E-mal: drarather@gmalcom

More information

Minkowski s inequality and sums of squares

Minkowski s inequality and sums of squares Cet Eur J Math 13 014 510-516 DOI: 10478/s11533-013-0346-1 Cetral Euroea Joural of Mathematcs Mows s equalty ad sums of squares Research Artcle Péter E Freel 1, Péter Horváth 1 1 Deartmet of Algebra ad

More information

MEASURES OF DISPERSION

MEASURES OF DISPERSION MEASURES OF DISPERSION Measure of Cetral Tedecy: Measures of Cetral Tedecy ad Dsperso ) Mathematcal Average: a) Arthmetc mea (A.M.) b) Geometrc mea (G.M.) c) Harmoc mea (H.M.) ) Averages of Posto: a) Meda

More information

Rademacher Complexity. Examples

Rademacher Complexity. Examples Algorthmc Foudatos of Learg Lecture 3 Rademacher Complexty. Examples Lecturer: Patrck Rebesch Verso: October 16th 018 3.1 Itroducto I the last lecture we troduced the oto of Rademacher complexty ad showed

More information

The Number of the Two Dimensional Run Length Constrained Arrays

The Number of the Two Dimensional Run Length Constrained Arrays 2009 Iteratoal Coferece o Mache Learg ad Coutg IPCSIT vol.3 (20) (20) IACSIT Press Sgaore The Nuber of the Two Desoal Ru Legth Costraed Arrays Tal Ataa Naohsa Otsua 2 Xuerog Yog 3 School of Scece ad Egeerg

More information

VOL. 3, NO. 11, November 2013 ISSN ARPN Journal of Science and Technology All rights reserved.

VOL. 3, NO. 11, November 2013 ISSN ARPN Journal of Science and Technology All rights reserved. VOL., NO., November 0 ISSN 5-77 ARPN Joural of Scece ad Techology 0-0. All rghts reserved. http://www.ejouralofscece.org Usg Square-Root Iverted Gamma Dstrbuto as Pror to Draw Iferece o the Raylegh Dstrbuto

More information

The internal structure of natural numbers, one method for the definition of large prime numbers, and a factorization test

The internal structure of natural numbers, one method for the definition of large prime numbers, and a factorization test Fal verso The teral structure of atural umbers oe method for the defto of large prme umbers ad a factorzato test Emmaul Maousos APM Isttute for the Advacemet of Physcs ad Mathematcs 3 Poulou str. 53 Athes

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 Aalyss of Varace ad Desg of Exermets-I MODULE II LECTURE - GENERAL LINEAR HYPOTHESIS AND ANALYSIS OF VARIANCE Dr Shalabh Deartmet of Mathematcs ad Statstcs Ida Isttute of Techology Kaur Tukey s rocedure

More information

22 Nonparametric Methods.

22 Nonparametric Methods. 22 oparametrc Methods. I parametrc models oe assumes apror that the dstrbutos have a specfc form wth oe or more ukow parameters ad oe tres to fd the best or atleast reasoably effcet procedures that aswer

More information

IMPROVED GA-CONVEXITY INEQUALITIES

IMPROVED GA-CONVEXITY INEQUALITIES IMPROVED GA-CONVEXITY INEQUALITIES RAZVAN A. SATNOIANU Corresodece address: Deartmet of Mathematcs, Cty Uversty, LONDON ECV HB, UK; e-mal: r.a.satoau@cty.ac.uk; web: www.staff.cty.ac.uk/~razva/ Abstract

More information

A note on An efficient certificateless aggregate signature with constant pairing computations

A note on An efficient certificateless aggregate signature with constant pairing computations A ote o A effcet certfcateless aggregate sgature wth costat parg computatos Debao He Maomao Ta Jahua Che School of Mathematcs ad Statstcs Wuha Uversty Wuha Cha School of Computer Scece ad Techology Uversty

More information

Optimum Probability Distribution for Minimum Redundancy of Source Coding

Optimum Probability Distribution for Minimum Redundancy of Source Coding Aled Mathematcs, 04, 5, 96-05 Publshed Ole Jauary 04 (htt://www.scr.org/oural/am) htt://dx.do.org/0.436/am.04.50 Otmum Probablty strbuto for Mmum Redudacy of Source Codg Om Parkash, Pryaka Kakkar eartmet

More information

A tighter lower bound on the circuit size of the hardest Boolean functions

A tighter lower bound on the circuit size of the hardest Boolean functions Electroc Colloquum o Computatoal Complexty, Report No. 86 2011) A tghter lower boud o the crcut sze of the hardest Boolea fuctos Masak Yamamoto Abstract I [IPL2005], Fradse ad Mlterse mproved bouds o the

More information

#A27 INTEGERS 13 (2013) SOME WEIGHTED SUMS OF PRODUCTS OF LUCAS SEQUENCES

#A27 INTEGERS 13 (2013) SOME WEIGHTED SUMS OF PRODUCTS OF LUCAS SEQUENCES #A27 INTEGERS 3 (203) SOME WEIGHTED SUMS OF PRODUCTS OF LUCAS SEQUENCES Emrah Kılıç Mathematcs Departmet, TOBB Uversty of Ecoomcs ad Techology, Akara, Turkey eklc@etu.edu.tr Neşe Ömür Mathematcs Departmet,

More information

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy Bouds o the expected etropy ad KL-dvergece of sampled multomal dstrbutos Brado C. Roy bcroy@meda.mt.edu Orgal: May 18, 2011 Revsed: Jue 6, 2011 Abstract Iformato theoretc quattes calculated from a sampled

More information

On A Two Dimensional Finsler Space Whose Geodesics Are Semi- Elipses and Pair of Straight Lines

On A Two Dimensional Finsler Space Whose Geodesics Are Semi- Elipses and Pair of Straight Lines IOSR Joural of Mathematcs (IOSR-JM) e-issn: 78-578 -ISSN:39-765X Volume 0 Issue Ver VII (Mar-Ar 04) PP 43-5 wwwosrjouralsorg O A Two Dmesoal Fsler Sace Whose Geodescs Are Sem- Elses ad Par of Straght es

More information

PROJECTION PROBLEM FOR REGULAR POLYGONS

PROJECTION PROBLEM FOR REGULAR POLYGONS Joural of Mathematcal Sceces: Advaces ad Applcatos Volume, Number, 008, Pages 95-50 PROJECTION PROBLEM FOR REGULAR POLYGONS College of Scece Bejg Forestry Uversty Bejg 0008 P. R. Cha e-mal: sl@bjfu.edu.c

More information

Mu Sequences/Series Solutions National Convention 2014

Mu Sequences/Series Solutions National Convention 2014 Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed

More information

Bayes (Naïve or not) Classifiers: Generative Approach

Bayes (Naïve or not) Classifiers: Generative Approach Logstc regresso Bayes (Naïve or ot) Classfers: Geeratve Approach What do we mea by Geeratve approach: Lear p(y), p(x y) ad the apply bayes rule to compute p(y x) for makg predctos Ths s essetally makg

More information

CHAPTER 6. d. With success = observation greater than 10, x = # of successes = 4, and

CHAPTER 6. d. With success = observation greater than 10, x = # of successes = 4, and CHAPTR 6 Secto 6.. a. We use the samle mea, to estmate the oulato mea µ. Σ 9.80 µ 8.407 7 ~ 7. b. We use the samle meda, 7 (the mddle observato whe arraged ascedg order. c. We use the samle stadard devato,

More information

A study of the sum three or more consecutive natural numbers

A study of the sum three or more consecutive natural numbers Fal verso of the artcle A study of the sum three or more cosecutve atural umbers Emmaul Maousos APM Isttute for the Advacemet of Physcs ad Mathematcs 3 Poulou str. 53 Athes Greece Abstract It holds that

More information

The Strong Goldbach Conjecture: Proof for All Even Integers Greater than 362

The Strong Goldbach Conjecture: Proof for All Even Integers Greater than 362 The Strog Goldbach Cojecture: Proof for All Eve Itegers Greater tha 36 Persoal address: Dr. Redha M Bouras 5 Old Frakl Grove Drve Chael Hll, NC 754 PhD Electrcal Egeerg Systems Uversty of Mchga at A Arbor,

More information

CONSTRUCTING TRUNCATED IRRATIONAL NUMBERS AND DETERMINING THEIR NEIGHBORING PRIMES

CONSTRUCTING TRUNCATED IRRATIONAL NUMBERS AND DETERMINING THEIR NEIGHBORING PRIMES CONSTRUCTING TRUNCATED IRRATIONAL NUMBERS AND DETERMINING THEIR NEIGHBORING PRIMES It is well kow that there exist a ifiite set of irratioal umbers icludig, sqrt(), ad e. Such quatities are of ifiite legth

More information

D KL (P Q) := p i ln p i q i

D KL (P Q) := p i ln p i q i Cheroff-Bouds 1 The Geeral Boud Let P 1,, m ) ad Q q 1,, q m ) be two dstrbutos o m elemets, e,, q 0, for 1,, m, ad m 1 m 1 q 1 The Kullback-Lebler dvergece or relatve etroy of P ad Q s defed as m D KL

More information

NP!= P. By Liu Ran. Table of Contents. The P vs. NP problem is a major unsolved problem in computer

NP!= P. By Liu Ran. Table of Contents. The P vs. NP problem is a major unsolved problem in computer NP!= P By Lu Ra Table of Cotets. Itroduce 2. Strategy 3. Prelmary theorem 4. Proof 5. Expla 6. Cocluso. Itroduce The P vs. NP problem s a major usolved problem computer scece. Iformally, t asks whether

More information

Applied Poker Test for General Digital Sequences

Applied Poker Test for General Digital Sequences IOSR Joural of Mathematcs IOSR-JM e-issn: 78-578, -ISSN: 39-765X. Volume, Issue Ver. V Ja. - Feb. 6, PP 7-3 www.osrjourals.org Ale Poker Test for Geeral Dgtal Sequeces Sahar A. Mohamme Abstract: The Poker

More information

A study of the sum of three or more consecutive natural numbers

A study of the sum of three or more consecutive natural numbers A study of the sum of three or more cosecutve atural umbers Emmaul Maousos APM Isttute for the Advacemet of Physcs ad Mathematcs 3 Poulou str. 53 Athes Greece Abstract It holds that every product of atural

More information

TWO NEW WEIGHTED MEASURES OF FUZZY ENTROPY AND THEIR PROPERTIES

TWO NEW WEIGHTED MEASURES OF FUZZY ENTROPY AND THEIR PROPERTIES merca. Jr. of Mathematcs ad Sceces Vol., No.,(Jauary 0) Copyrght Md Reader Publcatos www.jouralshub.com TWO NEW WEIGTED MESURES OF FUZZY ENTROPY ND TEIR PROPERTIES R.K.Tul Departmet of Mathematcs S.S.M.

More information

Several Theorems for the Trace of Self-conjugate Quaternion Matrix

Several Theorems for the Trace of Self-conjugate Quaternion Matrix Moder Aled Scece Setember, 008 Several Theorems for the Trace of Self-cojugate Quatero Matrx Qglog Hu Deartmet of Egeerg Techology Xchag College Xchag, Schua, 6503, Cha E-mal: shjecho@6com Lm Zou(Corresodg

More information

Chapter 5 Properties of a Random Sample

Chapter 5 Properties of a Random Sample Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample

More information

Fibonacci Identities as Binomial Sums

Fibonacci Identities as Binomial Sums It. J. Cotemp. Math. Sceces, Vol. 7, 1, o. 38, 1871-1876 Fboacc Idettes as Bomal Sums Mohammad K. Azara Departmet of Mathematcs, Uversty of Evasvlle 18 Lcol Aveue, Evasvlle, IN 477, USA E-mal: azara@evasvlle.edu

More information

Australian Journal of Basic and Applied Sciences. Full-Sweep SOR Iterative Method to Solve Space-Fractional Diffusion Equations

Australian Journal of Basic and Applied Sciences. Full-Sweep SOR Iterative Method to Solve Space-Fractional Diffusion Equations Australa Joural of Basc ad Aled Sceces, 8(4) Secal 14, Paes: 153-158 AENSI Jourals Australa Joural of Basc ad Aled Sceces ISSN:1991-8178 Joural home ae: www.abasweb.com Full-Swee SOR Iteratve Method to

More information

THE PROBABILISTIC STABILITY FOR THE GAMMA FUNCTIONAL EQUATION

THE PROBABILISTIC STABILITY FOR THE GAMMA FUNCTIONAL EQUATION Joural of Scece ad Arts Year 12, No. 3(2), pp. 297-32, 212 ORIGINAL AER THE ROBABILISTIC STABILITY FOR THE GAMMA FUNCTIONAL EQUATION DOREL MIHET 1, CLAUDIA ZAHARIA 1 Mauscrpt receved: 3.6.212; Accepted

More information

Strong Convergence of Weighted Averaged Approximants of Asymptotically Nonexpansive Mappings in Banach Spaces without Uniform Convexity

Strong Convergence of Weighted Averaged Approximants of Asymptotically Nonexpansive Mappings in Banach Spaces without Uniform Convexity BULLETIN of the MALAYSIAN MATHEMATICAL SCIENCES SOCIETY Bull. Malays. Math. Sc. Soc. () 7 (004), 5 35 Strog Covergece of Weghted Averaged Appromats of Asymptotcally Noepasve Mappgs Baach Spaces wthout

More information

Ideal multigrades with trigonometric coefficients

Ideal multigrades with trigonometric coefficients Ideal multgrades wth trgoometrc coeffcets Zarathustra Brady December 13, 010 1 The problem A (, k) multgrade s defed as a par of dstct sets of tegers such that (a 1,..., a ; b 1,..., b ) a j = =1 for all

More information

Unique Common Fixed Point of Sequences of Mappings in G-Metric Space M. Akram *, Nosheen

Unique Common Fixed Point of Sequences of Mappings in G-Metric Space M. Akram *, Nosheen Vol No : Joural of Facult of Egeerg & echolog JFE Pages 9- Uque Coo Fed Pot of Sequeces of Mags -Metrc Sace M. Ara * Noshee * Deartet of Matheatcs C Uverst Lahore Pasta. Eal: ara7@ahoo.co Deartet of Matheatcs

More information

Probability and Statistics. What is probability? What is statistics?

Probability and Statistics. What is probability? What is statistics? robablt ad Statstcs What s robablt? What s statstcs? robablt ad Statstcs robablt Formall defed usg a set of aoms Seeks to determe the lkelhood that a gve evet or observato or measuremet wll or has haeed

More information

MAX-MIN AND MIN-MAX VALUES OF VARIOUS MEASURES OF FUZZY DIVERGENCE

MAX-MIN AND MIN-MAX VALUES OF VARIOUS MEASURES OF FUZZY DIVERGENCE merca Jr of Mathematcs ad Sceces Vol, No,(Jauary 0) Copyrght Md Reader Publcatos wwwjouralshubcom MX-MIN ND MIN-MX VLUES OF VRIOUS MESURES OF FUZZY DIVERGENCE RKTul Departmet of Mathematcs SSM College

More information

On the Primitive Classes of K * KHALED S. FELALI Department of Mathematical Sciences, Umm Al-Qura University, Makkah Al-Mukarramah, Saudi Arabia

On the Primitive Classes of K * KHALED S. FELALI Department of Mathematical Sciences, Umm Al-Qura University, Makkah Al-Mukarramah, Saudi Arabia JKAU: Sc., O vol. the Prmtve, pp. 55-62 Classes (49 of A.H. K (BU) / 999 A.D.) * 55 O the Prmtve Classes of K * (BU) KHALED S. FELALI Departmet of Mathematcal Sceces, Umm Al-Qura Uversty, Makkah Al-Mukarramah,

More information

Section l h l Stem=Tens. 8l Leaf=Ones. 8h l 03. 9h 58

Section l h l Stem=Tens. 8l Leaf=Ones. 8h l 03. 9h 58 Secto.. 6l 34 6h 667899 7l 44 7h Stem=Tes 8l 344 Leaf=Oes 8h 5557899 9l 3 9h 58 Ths dsplay brgs out the gap the data: There are o scores the hgh 7's. 6. a. beams cylders 9 5 8 88533 6 6 98877643 7 488

More information

Lecture 1. (Part II) The number of ways of partitioning n distinct objects into k distinct groups containing n 1,

Lecture 1. (Part II) The number of ways of partitioning n distinct objects into k distinct groups containing n 1, Lecture (Part II) Materals Covered Ths Lecture: Chapter 2 (2.6 --- 2.0) The umber of ways of parttog dstct obects to dstct groups cotag, 2,, obects, respectvely, where each obect appears exactly oe group

More information