Integrality Gap for the Knapsack Problem

Size: px
Start display at page:

Download "Integrality Gap for the Knapsack Problem"

Transcription

1 Proof, beiefs, ad agorihms hrough he es of sum-of-squares 1 Iegraiy Gap for he Kapsack Probem Noe: These oes are si somewha rough. Suppose we have iems of ui size ad we wa o pack as may as possibe i a kapsack of size r. The, we ca pack a mos r umber of iems i he kapsack. I his ecure, we show ha his seemigy simpe reasoig ha oe ca pack a fracioa" iem is o capured by ow-degree SoS agorihm. Specificay, we prove Grigoriev s heorem (Grigoriev [2001]: 1. Theorem (SoS Hardess for Kapsack. For ay r /2, here is a pseudodisribuio of degree Ω(r suppored o pois x {0, 1} such ha x i = r. (1 i=1 Observe ha for o-iegra r he above yieds a iegraiy gap for he specia case of kapsack preseed above of degree Ω(r. Grigoriev s origia proof of his resu was a eega argume ha aayzed a aura, maximay symmeric pseudodisribuio ha we wi momeariy describe. This argume esseiay iveed severa basic resus abou he specrum of marices from he Johso Scheme sudied i agebraic combiaorics. We highy recommed readig Grigoriev s origia proof - i he ecure however, we wi rey o sadard resus from he heory of associaio schemes ad ge a shorer proof. This argume was firs preseed by Meka ad Wigderso (Meka ad Wigderso [2013] i a work ha aemped o show he firs SoS ower boud for he Paed Cique probem. The Pseudodisribuio Fix d = Θ(r o be chose aer. The idea for cosrucig degree d pseudodisribuio is very aura - he cosrai i x i = r is symmeric i r. Thus, if we had a arbirary degree 2d pseudodisribuio, we coud average i over a permuaios σ of [] ad obai aoher degree d pseudodisribuio over he hypercube ha is symmeric w.r.. permuaios of [] ad saisfies Eq. (1. Thus, for ay symmeric pseudodisribuio µ, here exiss a fucio f such ha for every muiiear moomia x S = Π i S x i, Ẽ[x S ] = f ( S. I urs ou ha here s o choice i f eiher.

2 Boaz Barak ad David Seurer 2 Firs, i=1 Ẽµ[x i ] = f (1. O he oher had, sice µ is suppored o x saisfyig Eq. (1, i=1 Ẽ[x i] = r. Thus, f (1 = r/ for every i. r f (1 = r Ẽ[x i ] = Ẽ[x i ( j=1 x j ] = Ẽ[x i ] + ( 1 Ẽ[x i x j ] = f (1 + ( 1 f (2, which impies ha f (2 = (r 1/( 1. Oe ca repea his argume o obai ha for every S such ha S 2d. (2 Ẽ[x S ] = f ( S = ( r S ( S, (3 I is easy o check ha Ẽ cosruced here saisfies he cosrai Eq. (1. Thus, as usua, we are ef wih showig he posiiviy of Ẽ. Reducio o Posiiviy over Squared Homogeous Poyomias Because of he iear equaiy cosrai Eq. (1, i urs ou ha i is eough o prove posiiviy of Ẽ[p 2 ] for ay homogeous degree d poyomia. 2. Lemma (Posiiviy over Squared Homogeous Poyomias is Eough. Suppose M be ay iear operaor o degree d poyomias ha is cosise wih he cosrais xi 2 = x i for every i [] ad i=1 x i r = 0. Suppose M(p 2 0 for every degree d homogeous poyomia p. The, M(q 2 0 for ay degree d poyomia q. Proof Skech. The idea is ha ay poyomia p of degree d ca be wrie as p 1 + i=1 (x 2 i x i p i + q( i=1 x i r (4 for a homogeous degree d poyomia p 1, degree a mos d 2 poyomias p i ad degree a mos d 1 poyomia q. This ca be show by poyomia divisio, for exampe. Oce we have he above represeaio, squarig he righ had side of Eq. (4 yieds p 2 1 pus erms ha have eiher (x2 i x i or ( i=1 x i r as a facor. Appyig M o he RHS, usig ieariy ad he fac ha M saisfies boh Booea ad he kapsack cosrais hus yieds ha M(p 2 = M(p We ca ow go o he marix view of higs i order o show posiiviy of Ẽ o homogeous degree d poyomias - we have see his argume severa imes i he ecures before.

3 Proof, beiefs, ad agorihms hrough he es of sum-of-squares 3 3. Exercise (Mome Marix. Le M R ( d ( d be defied by M(S, T = Ẽ[x S T ] = f (2d S T for ay S, T ( d. Show ha Ẽ[p 2 ] 0 for every homogeous degree d poyomia if ad oy if M 0. Noe ha M is he ( d ( d dimesioa pricipa sub-marix of he usua mome-marix of Ẽ. Johso Scheme The discussio here is based o (Meka ad Wigderso [2013] which i ur is based o (Godsi [1993]. Associaio schemes are wesudied objecs i Agebraic Combiaorics. For our purposes, we ca hik of hem as a commuaive agebra of square marices - i.e. addig or muipyig ay wo marices from he se yieds aoher marix i he se ad for ay wo marices A, B i he se, AB = BA. We are ieresed i oe such we-sudied scheme. 4. Defiiio (Johso Scheme. Le d < /2 N be parameers. The Johso Scheme J,d of order d o [] is he iear subspace of a marices J i R ( d ( d ha are se symmeric i.e. J(I, J = h( I J for ay I, J ( d. I oher words, ay ery of ay marix i he subspace depeds oy o he size of he iersecio of he row ad coum idex ses. We ow defie wo basis for J,d. 5. Defiiio (D Basis. For 0 d, e D R ( d ( d be he marix defied by 1 if I J = D (I, J = (5 0 oherwise.. D 0 is he he we-sudied Se Disjoiess marix from commuicaio compexiy. I is easy o check ha D for d spa J,d. Furher, i s aso easy o verify ha he D s commue wih each oher - ad hus every pair of marices i J,d commue esabishig ha J,d is ideed a commuaive agebra of marices. For he purposes of provig PSDess, aoher basis of J,d is very usefu - he P-Basis. 6. Defiiio (P Basis. For 0 d, e P R ( d ( d be defied by ( I J P (I, J =, (6

4 Boaz Barak ad David Seurer 4 where i s udersood ha if I J = 0, he P (I, J = 0. There s a equivae defiiio of P s ha s hepfu i cacuaios. 7. Exercise (Aerae Defiiio of P-Basis. Le R T be he rak 1 marix defied by R T (I, J = 1(I T1(J T. Show ha P (I, J = T [], T = R T. The above exercise ca be used o obai expici basis chage coefficies bewee he D ad he P basis. 8. Exercise (Chage bewee P ad D Basis. Show ha 1. For 0 d, we have P = d = ( D. 2. For 0 d, we have D = ( 1 ( P. The mai resus from he heory of associaio schemes of ieres o us is he characerizaio of eigespaces ad eigevaues of he marices i J,d. This is doe usig he fac ha here s a aura acio (he reabeig acio o eemes of [] of S ha commues wih every marix i J,d (because of se-symmery of J,d -marices. This impies ha marices i J,d mus share eigespaces wih hose correspodig o he acio of he symmeric group S. The aer are we udersood as represeaios of S ad oe ca use his udersadig o arrive a a expici descripio of eigevaues ad eigespaces of marices i J,d. For our purposes, we wi jus sae he resus required for us. 9. Lemma (Eigespaces of Johso Scheme. For P = P,,d defied as above, here exis pairwise orhogoa subspaces V 0, V 1, V 2,..., V d such ha 1. V 0, V 1, V 2,..., V d are eigespaces for P for every 0 r ad cosequey for every marix i J,d. 2. dim(v j = ( j ( j For ay marix J J,d, e λ i (J deoe he eigevaue of J o he subspace V i. The, ( i d λ i (P = (d i i if i 0 oherwise. (7 The above emma heps us esimae he eigevaues of ay marix ha ca be wrie as a iear combiaio of he marices P or D marices. I paricuar, we wi use he foowig esimae o he

5 Proof, beiefs, ad agorihms hrough he es of sum-of-squares 5 eigevaues of such marices i our aaysis of M from he previous secio. 10. Lemma (Eigevaue Esimaes for Johso Scheme. Le Q = α D J,d ad β = ( α for α 0. The, for 0 j r, ( j λ j (Q β d j ( d j j. (8 Proof. α D = α ( ( 1 ( P α = P ( ( 1 ( P ( α = β P (9 Usig ha Q ad P share eigespaces ad appyig Lemma Lemma 9, we obai he esimae caimed. PSDess of Ẽ 11. Lemma (M is PSD. M 0. Proof. We are goig o use Lemma Lemma 10. For ay o-egaive α 1, α 2,..., α d, oe ha: 0 α P = r =0 ( =0 ( α D (10 From he defiiio of M, we kow ha M = d =0 f (D 2d. We wi hus be doe from above if we ca fid o-egaive α such ha ( =0 α ( = f (2d for every. Observe ha f (2d = f (2d ( 2d+ ( r 2d+. Choose α = α = f (d ( d We ca ow verify: ( 2d + f (2d = f (2d = = ( r. ( r 2d+ 1 α =0 ( ( r r 2d + 1 ( ( r 2d + 1 r 2d + 1 =0 =0 α ( ( r 2d +. (11

6 Boaz Barak ad David Seurer 6 The emma ow foows ad i fac shows ha he miimum eigevaue of M is α d. Refereces Chris D. Godsi. Agebraic combiaorics. Chapma ad Ha mahemaics series. Chapma ad Ha, Dima Grigoriev. Compexiy of posiivseesaz proofs for he kapsack. Compuaioa Compexiy, 10(2: , Raghu Meka ad Avi Wigderso. Associaio schemes, ocommuaive poyomia coceraio, ad sum-of-squares ower bouds for paed cique. Eecroic Cooquium o Compuaioa Compexiy (ECCC, 20:105, 2013.

Zhi-Wei Sun and Hao Pan (Nanjing)

Zhi-Wei Sun and Hao Pan (Nanjing) Aca Arih. 5(006, o., 39. IDENTITIES CONCERNING BERNOULLI AND EULER POLYNOMIALS Zhi-Wei Su ad Hao Pa (Najig Absrac. We esabish wo geera ideiies for Beroui ad Euer poyomias, which are of a ew ype ad have

More information

1 Notes on Little s Law (l = λw)

1 Notes on Little s Law (l = λw) Copyrigh c 26 by Karl Sigma Noes o Lile s Law (l λw) We cosider here a famous ad very useful law i queueig heory called Lile s Law, also kow as l λw, which assers ha he ime average umber of cusomers i

More information

Ideal Amplifier/Attenuator. Memoryless. where k is some real constant. Integrator. System with memory

Ideal Amplifier/Attenuator. Memoryless. where k is some real constant. Integrator. System with memory Liear Time-Ivaria Sysems (LTI Sysems) Oulie Basic Sysem Properies Memoryless ad sysems wih memory (saic or dyamic) Causal ad o-causal sysems (Causaliy) Liear ad o-liear sysems (Lieariy) Sable ad o-sable

More information

Extremal graph theory II: K t and K t,t

Extremal graph theory II: K t and K t,t Exremal graph heory II: K ad K, Lecure Graph Theory 06 EPFL Frak de Zeeuw I his lecure, we geeralize he wo mai heorems from he las lecure, from riagles K 3 o complee graphs K, ad from squares K, o complee

More information

BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS

BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS Opimal ear Forecasig Alhough we have o meioed hem explicily so far i he course, here are geeral saisical priciples for derivig he bes liear forecas, ad

More information

An interesting result about subset sums. Nitu Kitchloo. Lior Pachter. November 27, Abstract

An interesting result about subset sums. Nitu Kitchloo. Lior Pachter. November 27, Abstract A ieresig resul abou subse sums Niu Kichloo Lior Pacher November 27, 1993 Absrac We cosider he problem of deermiig he umber of subses B f1; 2; : : :; g such ha P b2b b k mod, where k is a residue class

More information

Big O Notation for Time Complexity of Algorithms

Big O Notation for Time Complexity of Algorithms BRONX COMMUNITY COLLEGE of he Ciy Uiversiy of New York DEPARTMENT OF MATHEMATICS AND COMPUTER SCIENCE CSI 33 Secio E01 Hadou 1 Fall 2014 Sepember 3, 2014 Big O Noaio for Time Complexiy of Algorihms Time

More information

Online Supplement to Reactive Tabu Search in a Team-Learning Problem

Online Supplement to Reactive Tabu Search in a Team-Learning Problem Olie Suppleme o Reacive abu Search i a eam-learig Problem Yueli She School of Ieraioal Busiess Admiisraio, Shaghai Uiversiy of Fiace ad Ecoomics, Shaghai 00433, People s Republic of Chia, she.yueli@mail.shufe.edu.c

More information

Actuarial Society of India

Actuarial Society of India Acuarial Sociey of Idia EXAMINAIONS Jue 5 C4 (3) Models oal Marks - 5 Idicaive Soluio Q. (i) a) Le U deoe he process described by 3 ad V deoe he process described by 4. he 5 e 5 PU [ ] PV [ ] ( e ).538!

More information

ODEs II, Supplement to Lectures 6 & 7: The Jordan Normal Form: Solving Autonomous, Homogeneous Linear Systems. April 2, 2003

ODEs II, Supplement to Lectures 6 & 7: The Jordan Normal Form: Solving Autonomous, Homogeneous Linear Systems. April 2, 2003 ODEs II, Suppleme o Lecures 6 & 7: The Jorda Normal Form: Solvig Auoomous, Homogeeous Liear Sysems April 2, 23 I his oe, we describe he Jorda ormal form of a marix ad use i o solve a geeral homogeeous

More information

λiv Av = 0 or ( λi Av ) = 0. In order for a vector v to be an eigenvector, it must be in the kernel of λi

λiv Av = 0 or ( λi Av ) = 0. In order for a vector v to be an eigenvector, it must be in the kernel of λi Liear lgebra Lecure #9 Noes This week s lecure focuses o wha migh be called he srucural aalysis of liear rasformaios Wha are he irisic properies of a liear rasformaio? re here ay fixed direcios? The discussio

More information

Notes 03 largely plagiarized by %khc

Notes 03 largely plagiarized by %khc 1 1 Discree-Time Covoluio Noes 03 largely plagiarized by %khc Le s begi our discussio of covoluio i discree-ime, sice life is somewha easier i ha domai. We sar wih a sigal x[] ha will be he ipu io our

More information

Supplement for SADAGRAD: Strongly Adaptive Stochastic Gradient Methods"

Supplement for SADAGRAD: Strongly Adaptive Stochastic Gradient Methods Suppleme for SADAGRAD: Srogly Adapive Sochasic Gradie Mehods" Zaiyi Che * 1 Yi Xu * Ehog Che 1 iabao Yag 1. Proof of Proposiio 1 Proposiio 1. Le ɛ > 0 be fixed, H 0 γi, γ g, EF (w 1 ) F (w ) ɛ 0 ad ieraio

More information

COS 522: Complexity Theory : Boaz Barak Handout 10: Parallel Repetition Lemma

COS 522: Complexity Theory : Boaz Barak Handout 10: Parallel Repetition Lemma COS 522: Complexiy Theory : Boaz Barak Hadou 0: Parallel Repeiio Lemma Readig: () A Parallel Repeiio Theorem / Ra Raz (available o his websie) (2) Parallel Repeiio: Simplificaios ad he No-Sigallig Case

More information

1. Solve by the method of undetermined coefficients and by the method of variation of parameters. (4)

1. Solve by the method of undetermined coefficients and by the method of variation of parameters. (4) 7 Differeial equaios Review Solve by he mehod of udeermied coefficies ad by he mehod of variaio of parameers (4) y y = si Soluio; we firs solve he homogeeous equaio (4) y y = 4 The correspodig characerisic

More information

Calculus Limits. Limit of a function.. 1. One-Sided Limits...1. Infinite limits 2. Vertical Asymptotes...3. Calculating Limits Using the Limit Laws.

Calculus Limits. Limit of a function.. 1. One-Sided Limits...1. Infinite limits 2. Vertical Asymptotes...3. Calculating Limits Using the Limit Laws. Limi of a fucio.. Oe-Sided..... Ifiie limis Verical Asympoes... Calculaig Usig he Limi Laws.5 The Squeeze Theorem.6 The Precise Defiiio of a Limi......7 Coiuiy.8 Iermediae Value Theorem..9 Refereces..

More information

Math 6710, Fall 2016 Final Exam Solutions

Math 6710, Fall 2016 Final Exam Solutions Mah 67, Fall 6 Fial Exam Soluios. Firs, a sude poied ou a suble hig: if P (X i p >, he X + + X (X + + X / ( evaluaes o / wih probabiliy p >. This is roublesome because a radom variable is supposed o be

More information

L-functions and Class Numbers

L-functions and Class Numbers L-fucios ad Class Numbers Sude Number Theory Semiar S. M.-C. 4 Sepember 05 We follow Romyar Sharifi s Noes o Iwasawa Theory, wih some help from Neukirch s Algebraic Number Theory. L-fucios of Dirichle

More information

Section 8 Convolution and Deconvolution

Section 8 Convolution and Deconvolution APPLICATIONS IN SIGNAL PROCESSING Secio 8 Covoluio ad Decovoluio This docume illusraes several echiques for carryig ou covoluio ad decovoluio i Mahcad. There are several operaors available for hese fucios:

More information

Energy Density / Energy Flux / Total Energy in 1D. Key Mathematics: density, flux, and the continuity equation.

Energy Density / Energy Flux / Total Energy in 1D. Key Mathematics: density, flux, and the continuity equation. ecure Phys 375 Eergy Desiy / Eergy Flu / oal Eergy i D Overview ad Moivaio: Fro your sudy of waves i iroducory physics you should be aware ha waves ca raspor eergy fro oe place o aoher cosider he geeraio

More information

Four equations describe the dynamic solution to RBC model. Consumption-leisure efficiency condition. Consumption-investment efficiency condition

Four equations describe the dynamic solution to RBC model. Consumption-leisure efficiency condition. Consumption-investment efficiency condition LINEARIZING AND APPROXIMATING THE RBC MODEL SEPTEMBER 7, 200 For f( x, y, z ), mulivariable Taylor liear expasio aroud ( x, yz, ) f ( x, y, z) f( x, y, z) + f ( x, y, z)( x x) + f ( x, y, z)( y y) + f

More information

N! AND THE GAMMA FUNCTION

N! AND THE GAMMA FUNCTION N! AND THE GAMMA FUNCTION Cosider he produc of he firs posiive iegers- 3 4 5 6 (-) =! Oe calls his produc he facorial ad has ha produc of he firs five iegers equals 5!=0. Direcly relaed o he discree! fucio

More information

12 Getting Started With Fourier Analysis

12 Getting Started With Fourier Analysis Commuicaios Egieerig MSc - Prelimiary Readig Geig Sared Wih Fourier Aalysis Fourier aalysis is cocered wih he represeaio of sigals i erms of he sums of sie, cosie or complex oscillaio waveforms. We ll

More information

Moment Generating Function

Moment Generating Function 1 Mome Geeraig Fucio m h mome m m m E[ ] x f ( x) dx m h ceral mome m m m E[( ) ] ( ) ( x ) f ( x) dx Mome Geeraig Fucio For a real, M () E[ e ] e k x k e p ( x ) discree x k e f ( x) dx coiuous Example

More information

BE.430 Tutorial: Linear Operator Theory and Eigenfunction Expansion

BE.430 Tutorial: Linear Operator Theory and Eigenfunction Expansion BE.43 Tuorial: Liear Operaor Theory ad Eigefucio Expasio (adaped fro Douglas Lauffeburger) 9//4 Moivaig proble I class, we ecouered parial differeial equaios describig rasie syses wih cheical diffusio.

More information

Using Linnik's Identity to Approximate the Prime Counting Function with the Logarithmic Integral

Using Linnik's Identity to Approximate the Prime Counting Function with the Logarithmic Integral Usig Lii's Ideiy o Approimae he Prime Couig Fucio wih he Logarihmic Iegral Naha McKezie /26/2 aha@icecreambreafas.com Summary:This paper will show ha summig Lii's ideiy from 2 o ad arragig erms i a cerai

More information

K3 p K2 p Kp 0 p 2 p 3 p

K3 p K2 p Kp 0 p 2 p 3 p Mah 80-00 Mo Ar 0 Chaer 9 Fourier Series ad alicaios o differeial equaios (ad arial differeial equaios) 9.-9. Fourier series defiiio ad covergece. The idea of Fourier series is relaed o he liear algebra

More information

A TAUBERIAN THEOREM FOR THE WEIGHTED MEAN METHOD OF SUMMABILITY

A TAUBERIAN THEOREM FOR THE WEIGHTED MEAN METHOD OF SUMMABILITY U.P.B. Sci. Bull., Series A, Vol. 78, Iss. 2, 206 ISSN 223-7027 A TAUBERIAN THEOREM FOR THE WEIGHTED MEAN METHOD OF SUMMABILITY İbrahim Çaak I his paper we obai a Tauberia codiio i erms of he weighed classical

More information

LINEAR APPROXIMATION OF THE BASELINE RBC MODEL JANUARY 29, 2013

LINEAR APPROXIMATION OF THE BASELINE RBC MODEL JANUARY 29, 2013 LINEAR APPROXIMATION OF THE BASELINE RBC MODEL JANUARY 29, 203 Iroducio LINEARIZATION OF THE RBC MODEL For f( x, y, z ) = 0, mulivariable Taylor liear expasio aroud f( x, y, z) f( x, y, z) + f ( x, y,

More information

2 f(x) dx = 1, 0. 2f(x 1) dx d) 1 4t t6 t. t 2 dt i)

2 f(x) dx = 1, 0. 2f(x 1) dx d) 1 4t t6 t. t 2 dt i) Mah PracTes Be sure o review Lab (ad all labs) There are los of good quesios o i a) Sae he Mea Value Theorem ad draw a graph ha illusraes b) Name a impora heorem where he Mea Value Theorem was used i he

More information

Lecture 9: Polynomial Approximations

Lecture 9: Polynomial Approximations CS 70: Complexiy Theory /6/009 Lecure 9: Polyomial Approximaios Isrucor: Dieer va Melkebeek Scribe: Phil Rydzewski & Piramaayagam Arumuga Naiar Las ime, we proved ha o cosa deph circui ca evaluae he pariy

More information

FIXED FUZZY POINT THEOREMS IN FUZZY METRIC SPACE

FIXED FUZZY POINT THEOREMS IN FUZZY METRIC SPACE Mohia & Samaa, Vol. 1, No. II, December, 016, pp 34-49. ORIGINAL RESEARCH ARTICLE OPEN ACCESS FIED FUZZY POINT THEOREMS IN FUZZY METRIC SPACE 1 Mohia S. *, Samaa T. K. 1 Deparme of Mahemaics, Sudhir Memorial

More information

LINEAR APPROXIMATION OF THE BASELINE RBC MODEL SEPTEMBER 17, 2013

LINEAR APPROXIMATION OF THE BASELINE RBC MODEL SEPTEMBER 17, 2013 LINEAR APPROXIMATION OF THE BASELINE RBC MODEL SEPTEMBER 7, 203 Iroducio LINEARIZATION OF THE RBC MODEL For f( xyz,, ) = 0, mulivariable Taylor liear expasio aroud f( xyz,, ) f( xyz,, ) + f( xyz,, )( x

More information

th m m m m central moment : E[( X X) ] ( X X) ( x X) f ( x)

th m m m m central moment : E[( X X) ] ( X X) ( x X) f ( x) 1 Trasform Techiques h m m m m mome : E[ ] x f ( x) dx h m m m m ceral mome : E[( ) ] ( ) ( x) f ( x) dx A coveie wa of fidig he momes of a radom variable is he mome geeraig fucio (MGF). Oher rasform echiques

More information

The Central Limit Theorem

The Central Limit Theorem The Ceral Limi Theorem The ceral i heorem is oe of he mos impora heorems i probabiliy heory. While here a variey of forms of he ceral i heorem, he mos geeral form saes ha give a sufficiely large umber,

More information

David Randall. ( )e ikx. k = u x,t. u( x,t)e ikx dx L. x L /2. Recall that the proof of (1) and (2) involves use of the orthogonality condition.

David Randall. ( )e ikx. k = u x,t. u( x,t)e ikx dx L. x L /2. Recall that the proof of (1) and (2) involves use of the orthogonality condition. ! Revised April 21, 2010 1:27 P! 1 Fourier Series David Radall Assume ha u( x,) is real ad iegrable If he domai is periodic, wih period L, we ca express u( x,) exacly by a Fourier series expasio: ( ) =

More information

Exercise 3 Stochastic Models of Manufacturing Systems 4T400, 6 May

Exercise 3 Stochastic Models of Manufacturing Systems 4T400, 6 May Exercise 3 Sochasic Models of Maufacurig Sysems 4T4, 6 May. Each week a very popular loery i Adorra pris 4 ickes. Each ickes has wo 4-digi umbers o i, oe visible ad he oher covered. The umbers are radomly

More information

A Note on Random k-sat for Moderately Growing k

A Note on Random k-sat for Moderately Growing k A Noe o Radom k-sat for Moderaely Growig k Ju Liu LMIB ad School of Mahemaics ad Sysems Sciece, Beihag Uiversiy, Beijig, 100191, P.R. Chia juliu@smss.buaa.edu.c Zogsheg Gao LMIB ad School of Mahemaics

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 4 9/16/2013. Applications of the large deviation technique

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 4 9/16/2013. Applications of the large deviation technique MASSACHUSETTS ISTITUTE OF TECHOLOGY 6.265/5.070J Fall 203 Lecure 4 9/6/203 Applicaios of he large deviaio echique Coe.. Isurace problem 2. Queueig problem 3. Buffer overflow probabiliy Safey capial for

More information

Four equations describe the dynamic solution to RBC model. Consumption-leisure efficiency condition. Consumption-investment efficiency condition

Four equations describe the dynamic solution to RBC model. Consumption-leisure efficiency condition. Consumption-investment efficiency condition LINEAR APPROXIMATION OF THE BASELINE RBC MODEL FEBRUARY, 202 Iroducio For f(, y, z ), mulivariable Taylor liear epasio aroud (, yz, ) f (, y, z) f(, y, z) + f (, y, z)( ) + f (, y, z)( y y) + f (, y, z)(

More information

Mathematical Statistics. 1 Introduction to the materials to be covered in this course

Mathematical Statistics. 1 Introduction to the materials to be covered in this course Mahemaical Saisics Iroducio o he maerials o be covered i his course. Uivariae & Mulivariae r.v s 2. Borl-Caelli Lemma Large Deviaios. e.g. X,, X are iid r.v s, P ( X + + X where I(A) is a umber depedig

More information

Review Answers for E&CE 700T02

Review Answers for E&CE 700T02 Review Aswers for E&CE 700T0 . Deermie he curre soluio, all possible direcios, ad sepsizes wheher improvig or o for he simple able below: 4 b ma c 0 0 0-4 6 0 - B N B N ^0 0 0 curre sol =, = Ch for - -

More information

Extended Laguerre Polynomials

Extended Laguerre Polynomials I J Coemp Mah Scieces, Vol 7, 1, o, 189 194 Exeded Laguerre Polyomials Ada Kha Naioal College of Busiess Admiisraio ad Ecoomics Gulberg-III, Lahore, Pakisa adakhaariq@gmailcom G M Habibullah Naioal College

More information

Pure Math 30: Explained!

Pure Math 30: Explained! ure Mah : Explaied! www.puremah.com 6 Logarihms Lesso ar Basic Expoeial Applicaios Expoeial Growh & Decay: Siuaios followig his ype of chage ca be modeled usig he formula: (b) A = Fuure Amou A o = iial

More information

Fermat Numbers in Multinomial Coefficients

Fermat Numbers in Multinomial Coefficients 1 3 47 6 3 11 Joural of Ieger Sequeces, Vol. 17 (014, Aricle 14.3. Ferma Numbers i Muliomial Coefficies Shae Cher Deparme of Mahemaics Zhejiag Uiversiy Hagzhou, 31007 Chia chexiaohag9@gmail.com Absrac

More information

Fresnel Dragging Explained

Fresnel Dragging Explained Fresel Draggig Explaied 07/05/008 Decla Traill Decla@espace.e.au The Fresel Draggig Coefficie required o explai he resul of he Fizeau experime ca be easily explaied by usig he priciples of Eergy Field

More information

Some Properties of Semi-E-Convex Function and Semi-E-Convex Programming*

Some Properties of Semi-E-Convex Function and Semi-E-Convex Programming* The Eighh Ieraioal Symposium o Operaios esearch ad Is Applicaios (ISOA 9) Zhagjiajie Chia Sepember 2 22 29 Copyrigh 29 OSC & APOC pp 33 39 Some Properies of Semi-E-Covex Fucio ad Semi-E-Covex Programmig*

More information

The Eigen Function of Linear Systems

The Eigen Function of Linear Systems 1/25/211 The Eige Fucio of Liear Sysems.doc 1/7 The Eige Fucio of Liear Sysems Recall ha ha we ca express (expad) a ime-limied sigal wih a weighed summaio of basis fucios: v ( ) a ψ ( ) = where v ( ) =

More information

LIMITS OF FUNCTIONS (I)

LIMITS OF FUNCTIONS (I) LIMITS OF FUNCTIO (I ELEMENTARY FUNCTIO: (Elemeary fucios are NOT piecewise fucios Cosa Fucios: f(x k, where k R Polyomials: f(x a + a x + a x + a x + + a x, where a, a,..., a R Raioal Fucios: f(x P (x,

More information

ECE-314 Fall 2012 Review Questions

ECE-314 Fall 2012 Review Questions ECE-34 Fall 0 Review Quesios. A liear ime-ivaria sysem has he ipu-oupu characerisics show i he firs row of he diagram below. Deermie he oupu for he ipu show o he secod row of he diagram. Jusify your aswer.

More information

Math 2414 Homework Set 7 Solutions 10 Points

Math 2414 Homework Set 7 Solutions 10 Points Mah Homework Se 7 Soluios 0 Pois #. ( ps) Firs verify ha we ca use he iegral es. The erms are clearly posiive (he epoeial is always posiive ad + is posiive if >, which i is i his case). For decreasig we

More information

CLOSED FORM EVALUATION OF RESTRICTED SUMS CONTAINING SQUARES OF FIBONOMIAL COEFFICIENTS

CLOSED FORM EVALUATION OF RESTRICTED SUMS CONTAINING SQUARES OF FIBONOMIAL COEFFICIENTS PB Sci Bull, Series A, Vol 78, Iss 4, 2016 ISSN 1223-7027 CLOSED FORM EVALATION OF RESTRICTED SMS CONTAINING SQARES OF FIBONOMIAL COEFFICIENTS Emrah Kılıc 1, Helmu Prodiger 2 We give a sysemaic approach

More information

A Note on Prediction with Misspecified Models

A Note on Prediction with Misspecified Models ITB J. Sci., Vol. 44 A, No. 3,, 7-9 7 A Noe o Predicio wih Misspecified Models Khresha Syuhada Saisics Research Divisio, Faculy of Mahemaics ad Naural Scieces, Isiu Tekologi Badug, Jala Gaesa Badug, Jawa

More information

A note on deviation inequalities on {0, 1} n. by Julio Bernués*

A note on deviation inequalities on {0, 1} n. by Julio Bernués* A oe o deviaio iequaliies o {0, 1}. by Julio Berués* Deparameo de Maemáicas. Faculad de Ciecias Uiversidad de Zaragoza 50009-Zaragoza (Spai) I. Iroducio. Le f: (Ω, Σ, ) IR be a radom variable. Roughly

More information

Hadamard matrices from the Multiplication Table of the Finite Fields

Hadamard matrices from the Multiplication Table of the Finite Fields adamard marice from he Muliplicaio Table of he Fiie Field 신민호 송홍엽 노종선 * Iroducio adamard mari biary m-equece New Corucio Coe Theorem. Corucio wih caoical bai Theorem. Corucio wih ay bai Remark adamard

More information

Electrical Engineering Department Network Lab.

Electrical Engineering Department Network Lab. Par:- Elecrical Egieerig Deparme Nework Lab. Deermiaio of differe parameers of -por eworks ad verificaio of heir ierrelaio ships. Objecive: - To deermie Y, ad ABD parameers of sigle ad cascaded wo Por

More information

Comparison between Fourier and Corrected Fourier Series Methods

Comparison between Fourier and Corrected Fourier Series Methods Malaysia Joural of Mahemaical Scieces 7(): 73-8 (13) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES Joural homepage: hp://eispem.upm.edu.my/oural Compariso bewee Fourier ad Correced Fourier Series Mehods 1

More information

SUPER LINEAR ALGEBRA

SUPER LINEAR ALGEBRA Super Liear - Cover:Layou 7/7/2008 2:32 PM Page SUPER LINEAR ALGEBRA W. B. Vasaha Kadasamy e-mail: vasahakadasamy@gmail.com web: hp://ma.iim.ac.i/~wbv www.vasaha.e Florei Smaradache e-mail: smarad@um.edu

More information

Review Exercises for Chapter 9

Review Exercises for Chapter 9 0_090R.qd //0 : PM Page 88 88 CHAPTER 9 Ifiie Series I Eercises ad, wrie a epressio for he h erm of he sequece..,., 5, 0,,,, 0,... 7,... I Eercises, mach he sequece wih is graph. [The graphs are labeled

More information

STRONG CONVERGENCE OF MODIFIED MANN ITERATIONS FOR LIPSCHITZ PSEUDOCONTRACTIONS

STRONG CONVERGENCE OF MODIFIED MANN ITERATIONS FOR LIPSCHITZ PSEUDOCONTRACTIONS Joura of Mahemaica Scieces: Advaces ad Appicaios Voume, Number, 009, Pages 47-59 STRONG CONVERGENCE OF MODIFIED MANN ITERATIONS FOR LIPSCHITZ PSEUDOCONTRACTIONS JING HAN ad YISHENG SONG Mahmaicas ad Sciece

More information

Two Coupled Oscillators / Normal Modes

Two Coupled Oscillators / Normal Modes Lecure 3 Phys 3750 Two Coupled Oscillaors / Normal Modes Overview and Moivaion: Today we ake a small, bu significan, sep owards wave moion. We will no ye observe waves, bu his sep is imporan in is own

More information

Lecture 8 April 18, 2018

Lecture 8 April 18, 2018 Sas 300C: Theory of Saisics Sprig 2018 Lecure 8 April 18, 2018 Prof Emmauel Cades Scribe: Emmauel Cades Oulie Ageda: Muliple Tesig Problems 1 Empirical Process Viewpoi of BHq 2 Empirical Process Viewpoi

More information

Economics 8723 Macroeconomic Theory Problem Set 3 Sketch of Solutions Professor Sanjay Chugh Spring 2017

Economics 8723 Macroeconomic Theory Problem Set 3 Sketch of Solutions Professor Sanjay Chugh Spring 2017 Deparme of Ecoomic The Ohio Sae Uiveriy Ecoomic 8723 Macroecoomic Theory Problem Se 3 Skech of Soluio Profeor Sajay Chugh Sprig 27 Taylor Saggered Nomial Price-Seig Model There are wo group of moopoliically-compeiive

More information

Some Identities Relating to Degenerate Bernoulli Polynomials

Some Identities Relating to Degenerate Bernoulli Polynomials Fioma 30:4 2016), 905 912 DOI 10.2298/FIL1604905K Pubishe by Facuy of Scieces a Mahemaics, Uiversiy of Niš, Serbia Avaiabe a: hp://www.pmf.i.ac.rs/fioma Some Ieiies Reaig o Degeerae Beroui Poyomias Taekyu

More information

arxiv:math/ v1 [math.fa] 1 Feb 1994

arxiv:math/ v1 [math.fa] 1 Feb 1994 arxiv:mah/944v [mah.fa] Feb 994 ON THE EMBEDDING OF -CONCAVE ORLICZ SPACES INTO L Care Schü Abrac. I [K S ] i wa how ha Ave ( i a π(i) ) π i equivale o a Orlicz orm whoe Orlicz fucio i -cocave. Here we

More information

Union-Find Partition Structures Goodrich, Tamassia Union-Find 1

Union-Find Partition Structures Goodrich, Tamassia Union-Find 1 Uio-Fid Pariio Srucures 004 Goodrich, Tamassia Uio-Fid Pariios wih Uio-Fid Operaios makesex: Creae a sileo se coaii he eleme x ad reur he posiio sori x i his se uioa,b : Reur he se A U B, desroyi he old

More information

Common Fixed Point Theorem in Intuitionistic Fuzzy Metric Space via Compatible Mappings of Type (K)

Common Fixed Point Theorem in Intuitionistic Fuzzy Metric Space via Compatible Mappings of Type (K) Ieraioal Joural of ahemaics Treds ad Techology (IJTT) Volume 35 umber 4- July 016 Commo Fixed Poi Theorem i Iuiioisic Fuzzy eric Sace via Comaible aigs of Tye (K) Dr. Ramaa Reddy Assisa Professor De. of

More information

The analysis of the method on the one variable function s limit Ke Wu

The analysis of the method on the one variable function s limit Ke Wu Ieraioal Coferece o Advaces i Mechaical Egieerig ad Idusrial Iformaics (AMEII 5) The aalysis of he mehod o he oe variable fucio s i Ke Wu Deparme of Mahemaics ad Saisics Zaozhuag Uiversiy Zaozhuag 776

More information

xp (X = x) = P (X = 1) = θ. Hence, the method of moments estimator of θ is

xp (X = x) = P (X = 1) = θ. Hence, the method of moments estimator of θ is Exercise 7 / page 356 Noe ha X i are ii from Beroulli(θ where 0 θ a Meho of momes: Sice here is oly oe parameer o be esimae we ee oly oe equaio where we equae he rs sample mome wih he rs populaio mome,

More information

TAKA KUSANO. laculty of Science Hrosh tlnlersty 1982) (n-l) + + Pn(t)x 0, (n-l) + + Pn(t)Y f(t,y), XR R are continuous functions.

TAKA KUSANO. laculty of Science Hrosh tlnlersty 1982) (n-l) + + Pn(t)x 0, (n-l) + + Pn(t)Y f(t,y), XR R are continuous functions. Iera. J. Mah. & Mah. Si. Vol. 6 No. 3 (1983) 559-566 559 ASYMPTOTIC RELATIOHIPS BETWEEN TWO HIGHER ORDER ORDINARY DIFFERENTIAL EQUATIONS TAKA KUSANO laculy of Sciece Hrosh llersy 1982) ABSTRACT. Some asympoic

More information

STK4080/9080 Survival and event history analysis

STK4080/9080 Survival and event history analysis STK48/98 Survival ad eve hisory aalysis Marigales i discree ime Cosider a sochasic process The process M is a marigale if Lecure 3: Marigales ad oher sochasic processes i discree ime (recap) where (formally

More information

B. Maddah INDE 504 Simulation 09/02/17

B. Maddah INDE 504 Simulation 09/02/17 B. Maddah INDE 54 Simulaio 9/2/7 Queueig Primer Wha is a queueig sysem? A queueig sysem cosiss of servers (resources) ha provide service o cusomers (eiies). A Cusomer requesig service will sar service

More information

Solutions to selected problems from the midterm exam Math 222 Winter 2015

Solutions to selected problems from the midterm exam Math 222 Winter 2015 Soluios o seleced problems from he miderm eam Mah Wier 5. Derive he Maclauri series for he followig fucios. (cf. Pracice Problem 4 log( + (a L( d. Soluio: We have he Maclauri series log( + + 3 3 4 4 +...,

More information

t is a basis for the solution space to this system, then the matrix having these solutions as columns, t x 1 t, x 2 t,... x n t x 2 t...

t is a basis for the solution space to this system, then the matrix having these solutions as columns, t x 1 t, x 2 t,... x n t x 2 t... Mah 228- Fri Mar 24 5.6 Marix exponenials and linear sysems: The analogy beween firs order sysems of linear differenial equaions (Chaper 5) and scalar linear differenial equaions (Chaper ) is much sronger

More information

CSE 202: Design and Analysis of Algorithms Lecture 16

CSE 202: Design and Analysis of Algorithms Lecture 16 CSE 202: Desig ad Aalysis of Algorihms Lecure 16 Isrucor: Kamalia Chaudhuri Iequaliy 1: Marov s Iequaliy Pr(X=x) Pr(X >= a) 0 x a If X is a radom variable which aes o-egaive values, ad a > 0, he Pr[X a]

More information

Sheffer sequences of polynomials and their applications

Sheffer sequences of polynomials and their applications Kim e a. Advaces i Differece Equaios 2013 2013:118 hp://www.advacesidiffereceequaios.com/coe/2013/1/118 R E V I E W Ope Access Sheffer sequeces of poyomias ad heir appicaios Dae Sa Kim 1 TaekyuKim 2* Seog-Hoo

More information

Discrete Fourier Transform

Discrete Fourier Transform Discrete Fourier Trasform ) Purpose The purpose is to represet a determiistic or stochastic siga u( t ) as a fiite Fourier sum, whe observatios of u() t ( ) are give o a reguar grid, each affected by a

More information

SHOCK AND VIBRATION RESPONSE SPECTRA COURSE Unit 21 Base Excitation Shock: Classical Pulse

SHOCK AND VIBRATION RESPONSE SPECTRA COURSE Unit 21 Base Excitation Shock: Classical Pulse SHOCK AND VIBRAION RESPONSE SPECRA COURSE Ui 1 Base Exciaio Shock: Classical Pulse By om Irvie Email: omirvie@aol.com Iroucio Cosier a srucure subjece o a base exciaio shock pulse. Base exciaio is also

More information

Chapter Three Systems of Linear Differential Equations

Chapter Three Systems of Linear Differential Equations Chaper Three Sysems of Linear Differenial Equaions In his chaper we are going o consier sysems of firs orer orinary ifferenial equaions. These are sysems of he form x a x a x a n x n x a x a x a n x n

More information

F.Y. Diploma : Sem. II [AE/CH/FG/ME/PT/PG] Applied Mathematics

F.Y. Diploma : Sem. II [AE/CH/FG/ME/PT/PG] Applied Mathematics F.Y. Diploma : Sem. II [AE/CH/FG/ME/PT/PG] Applied Mahemaics Prelim Quesio Paper Soluio Q. Aemp ay FIVE of he followig : [0] Q.(a) Defie Eve ad odd fucios. [] As.: A fucio f() is said o be eve fucio if

More information

Union-Find Partition Structures

Union-Find Partition Structures Uio-Fid //4 : Preseaio for use wih he exbook Daa Srucures ad Alorihms i Java, h ediio, by M. T. Goodrich, R. Tamassia, ad M. H. Goldwasser, Wiley, 04 Uio-Fid Pariio Srucures 04 Goodrich, Tamassia, Goldwasser

More information

OLS bias for econometric models with errors-in-variables. The Lucas-critique Supplementary note to Lecture 17

OLS bias for econometric models with errors-in-variables. The Lucas-critique Supplementary note to Lecture 17 OLS bias for ecoomeric models wih errors-i-variables. The Lucas-criique Supplemeary oe o Lecure 7 RNy May 6, 03 Properies of OLS i RE models I Lecure 7 we discussed he followig example of a raioal expecaios

More information

On The Generalized Type and Generalized Lower Type of Entire Function in Several Complex Variables With Index Pair (p, q)

On The Generalized Type and Generalized Lower Type of Entire Function in Several Complex Variables With Index Pair (p, q) O he eeralized ye ad eeralized Lower ye of Eire Fucio i Several Comlex Variables Wih Idex Pair, Aima Abdali Jaffar*, Mushaq Shakir A Hussei Dearme of Mahemaics, College of sciece, Al-Musasiriyah Uiversiy,

More information

EXISTENCE THEORY OF RANDOM DIFFERENTIAL EQUATIONS D. S. Palimkar

EXISTENCE THEORY OF RANDOM DIFFERENTIAL EQUATIONS D. S. Palimkar Ieraioal Joural of Scieific ad Research Publicaios, Volue 2, Issue 7, July 22 ISSN 225-353 EXISTENCE THEORY OF RANDOM DIFFERENTIAL EQUATIONS D S Palikar Depare of Maheaics, Vasarao Naik College, Naded

More information

Lecture 15 First Properties of the Brownian Motion

Lecture 15 First Properties of the Brownian Motion Lecure 15: Firs Properies 1 of 8 Course: Theory of Probabiliy II Term: Sprig 2015 Isrucor: Gorda Zikovic Lecure 15 Firs Properies of he Browia Moio This lecure deals wih some of he more immediae properies

More information

Week 8 Lecture 3: Problems 49, 50 Fourier analysis Courseware pp (don t look at French very confusing look in the Courseware instead)

Week 8 Lecture 3: Problems 49, 50 Fourier analysis Courseware pp (don t look at French very confusing look in the Courseware instead) Week 8 Lecure 3: Problems 49, 5 Fourier lysis Coursewre pp 6-7 (do look Frech very cofusig look i he Coursewre ised) Fourier lysis ivolves ddig wves d heir hrmoics, so i would hve urlly followed fer he

More information

If boundary values are necessary, they are called mixed initial-boundary value problems. Again, the simplest prototypes of these IV problems are:

If boundary values are necessary, they are called mixed initial-boundary value problems. Again, the simplest prototypes of these IV problems are: 3. Iiial value problems: umerical soluio Fiie differeces - Trucaio errors, cosisecy, sabiliy ad covergece Crieria for compuaioal sabiliy Explici ad implici ime schemes Table of ime schemes Hyperbolic ad

More information

Lecture 15: Three-tank Mixing and Lead Poisoning

Lecture 15: Three-tank Mixing and Lead Poisoning Lecure 15: Three-ak Miig ad Lead Poisoig Eigevalues ad eigevecors will be used o fid he soluio of a sysem for ukow fucios ha saisfy differeial equaios The ukow fucios will be wrie as a 1 colum vecor [

More information

Additional Tables of Simulation Results

Additional Tables of Simulation Results Saisica Siica: Suppleme REGULARIZING LASSO: A CONSISTENT VARIABLE SELECTION METHOD Quefeg Li ad Ju Shao Uiversiy of Wiscosi, Madiso, Eas Chia Normal Uiversiy ad Uiversiy of Wiscosi, Madiso Supplemeary

More information

F D D D D F. smoothed value of the data including Y t the most recent data.

F D D D D F. smoothed value of the data including Y t the most recent data. Module 2 Forecasig 1. Wha is forecasig? Forecasig is defied as esimaig he fuure value ha a parameer will ake. Mos scieific forecasig mehods forecas he fuure value usig pas daa. I Operaios Maageme forecasig

More information

Math 10B: Mock Mid II. April 13, 2016

Math 10B: Mock Mid II. April 13, 2016 Name: Soluions Mah 10B: Mock Mid II April 13, 016 1. ( poins) Sae, wih jusificaion, wheher he following saemens are rue or false. (a) If a 3 3 marix A saisfies A 3 A = 0, hen i canno be inverible. True.

More information

Comparisons Between RV, ARV and WRV

Comparisons Between RV, ARV and WRV Comparisos Bewee RV, ARV ad WRV Cao Gag,Guo Migyua School of Maageme ad Ecoomics, Tiaji Uiversiy, Tiaji,30007 Absrac: Realized Volailiy (RV) have bee widely used sice i was pu forward by Aderso ad Bollerslev

More information

Biol. 356 Lab 8. Mortality, Recruitment, and Migration Rates

Biol. 356 Lab 8. Mortality, Recruitment, and Migration Rates Biol. 356 Lab 8. Moraliy, Recruimen, and Migraion Raes (modified from Cox, 00, General Ecology Lab Manual, McGraw Hill) Las week we esimaed populaion size hrough several mehods. One assumpion of all hese

More information

Dynamic h-index: the Hirsch index in function of time

Dynamic h-index: the Hirsch index in function of time Dyamic h-idex: he Hirsch idex i fucio of ime by L. Egghe Uiversiei Hassel (UHassel), Campus Diepebeek, Agoralaa, B-3590 Diepebeek, Belgium ad Uiversiei Awerpe (UA), Campus Drie Eike, Uiversieisplei, B-260

More information

Sampling Example. ( ) δ ( f 1) (1/2)cos(12πt), T 0 = 1

Sampling Example. ( ) δ ( f 1) (1/2)cos(12πt), T 0 = 1 Samplig Example Le x = cos( 4π)cos( π). The fudameal frequecy of cos 4π fudameal frequecy of cos π is Hz. The ( f ) = ( / ) δ ( f 7) + δ ( f + 7) / δ ( f ) + δ ( f + ). ( f ) = ( / 4) δ ( f 8) + δ ( f

More information

A Complex Neural Network Algorithm for Computing the Largest Real Part Eigenvalue and the corresponding Eigenvector of a Real Matrix

A Complex Neural Network Algorithm for Computing the Largest Real Part Eigenvalue and the corresponding Eigenvector of a Real Matrix 4h Ieraioal Coferece o Sesors, Mecharoics ad Auomaio (ICSMA 06) A Complex Neural Newor Algorihm for Compuig he Larges eal Par Eigevalue ad he correspodig Eigevecor of a eal Marix HANG AN, a, XUESONG LIANG,

More information

SLOW INCREASING FUNCTIONS AND THEIR APPLICATIONS TO SOME PROBLEMS IN NUMBER THEORY

SLOW INCREASING FUNCTIONS AND THEIR APPLICATIONS TO SOME PROBLEMS IN NUMBER THEORY VOL. 8, NO. 7, JULY 03 ISSN 89-6608 ARPN Jourl of Egieerig d Applied Sciece 006-03 Ai Reerch Publihig Nework (ARPN). All righ reerved. www.rpjourl.com SLOW INCREASING FUNCTIONS AND THEIR APPLICATIONS TO

More information

SUMMATION OF INFINITE SERIES REVISITED

SUMMATION OF INFINITE SERIES REVISITED SUMMATION OF INFINITE SERIES REVISITED I several aricles over he las decade o his web page we have show how o sum cerai iiie series icludig he geomeric series. We wa here o eed his discussio o he geeral

More information

Hamilton- J acobi Equation: Weak S olution We continue the study of the Hamilton-Jacobi equation:

Hamilton- J acobi Equation: Weak S olution We continue the study of the Hamilton-Jacobi equation: M ah 5 7 Fall 9 L ecure O c. 4, 9 ) Hamilon- J acobi Equaion: Weak S oluion We coninue he sudy of he Hamilon-Jacobi equaion: We have shown ha u + H D u) = R n, ) ; u = g R n { = }. ). In general we canno

More information

Lecture 24. Calderon-Zygmund Decomposition Technique: Cube decomposition

Lecture 24. Calderon-Zygmund Decomposition Technique: Cube decomposition Lecure 24 May 3 h, 2004 Our moivaion for his as ecure in he course is o show a resu using our reguariy heory which is oherwise unprovabe using cassica echniques. This is he previous Theorem, and in paricuar

More information