AN INTERIM REPORT ON SOFT SYSTEMS EVALUATION

Size: px
Start display at page:

Download "AN INTERIM REPORT ON SOFT SYSTEMS EVALUATION"

Transcription

1 AN INTERIM REPORT ON SOFT SYSTEMS EVALUATION Viljem Rupik INTERACTA, LTD, Busiess Iformatio Processig Parmova 53, Ljubljaa , Abstract: As applicatio areas rapidly grow beyod the theoretical framework of fudametal decisio theory we are very ofte temptated to see whether or ot soft systems may promise some efficiet modellig of real life problems. The pioeerig bust towards soft systems methodology has come from the eeds of mathematical sociology. Its cotemporary defiitio as well as its applied architecture have bee dealt with as i a paper proposed. Key words: fudametal decisio theory, soft system modellig 1. AN INTRODUCTION The origial decisio theory offers the largest formulatio of decisio process, compresed through fudametal equatio of deciso theory (FDT). Sice it is a brute force approach, our followig its lies of implemetatio hits upo severe obstacles to satisfyig solutios (see /1/). The appearece of soft systems methodology (SSM) seems to be a way out of these troubles, despite may trappig states threateig the applicatio of FDT. To check whether or ot the SSM is some step of improved system modellig, we shall here match the two cocepts. As a whole, there are two sigificat ad crucial scietific areas which ifluece pragmatic reputatio of OR: goseology ad hermeeutics. They iteract strogly, especially whe SSM is beig exercised. Both of them should be take ito accout: the first oe determies the problem solvig procedure ad the latter oe shapes the scope ad depth of its implemetatio i real busiess lives. 2. BASIC CONCEPT OF SSM The most outstadig authors are Peter Checklad ad Jim Scholes (see /2/) which itroduce basic SSM cocept ad the possibility to coceive the potetial difficulties whe applyig it. Their book discloses various applicatios as variatios of the basic SSM cocept. They are as follow i pure verbal form: 43

2 a real world situatio of cocer yields of choices relevat systems of purposeful activity compariso of models with perceived real situatio actio eeded to improve the situatio 3. BASIC CONCEPT OF FDT To judge the SSM as it stads today we first have to reiterpret the above milig stoes i terms of geeral decisio theory (FDT), which we reproduce as follows. To illumiate it, let us start with geeral decisio theory ad its fudametal equatio, whose costituets are as follows: - let X be a arbitrary fiite dimesioal vector space, represetig all coceivable decisio variables x X ; let be a object of decisio makig process (which, i geeral, is ot a problem itself); - there is always at least oe cosequece y Ym, agai from fiite dimesioal vector space, correspodig to iput x X ; - we also itroduce a operator Y = SX producig output from iput; let us call it as decisiogeerator; - X a should serves as a space of admissible decisio variables, X a X ; - let z Z be a estimate of a cosequece of decisio variable y Ym (of a arbitrary dimesio); - there is a mappig U of y Ym ito z Z ; - let q Q be a primary (backwards) costructio of a problem, based o, uder the coditios of certaity; - q~ ( z... z ) ~ Q as a aalogy, let q~ ~ Q be a secodary (backwards) costructio of the same 1, m problem, based o, uder the coditios of ucertaity; 44

3 - let is a mapppig of Y ito Q ( a primary geerator of problem costructios); i real situatios, this mappig reduces Q ito some part Q ; - for the case of ucertaity, let maps Z ito Q ~ ; here agai, this mappig may produce some shrikage of Q ~ ; - the shrikage of Q ~ is the projected oto Z * Z via operator ; - is a operator of iduced subspace X * d Z * Z ; of alterative admisible decisios, beig mapped from Based o these miimal categories of FDT processig the followig graph might be useful (see/1/): X X d X * d S y Y m U Z Z* Q? Q ~ Q ~ Q ~ Q ~ 3. SSM-FDT MATCHING Now, the matchig is ow as follows: A real world situatio of cocer may be iterpreted as q~ ~ Q strictly i FDT sese although is ot defied ad expressed explicitly; cosequetly, SSM is»dealig«with ; yields of choices may be iterpreted as z, where z Z accordig to (FDT); 45

4 Z* oly teds to be reached; cosequetly, is ot formalised, at least ot at each step of approximatio; relevat systems of purposeful activity might be roughly iterpreted by U, igorig y Ym ; compariso of models with perceived real situatio could be coceived by a pair of, but Q ~ ad Q ~ are ot explicitly computed; actio eeded to improve the situatio could be uderstood as although SSM is ot explicitly stemmig from Q ~ ; cosequetly, Z* ca ot be reached strictly; q Q is ot ivolved i SSM ad, cosequetly, it ca ot serve as a basis for Q ~ ad Q ~ : costructio parameters describig do ot appear; by SSM approach, a fact which worries most of all is that the perceptio of a) uderlies a subjective decriptio/perceptio ad b) chages over a series of approximatios; cosequetly, it is difficult to expect the procedure to be coverget; withi the FDT framework we usually simplify»our«space of cosequeces to be Z=(US)X ad thus X= ( US) 1 Z (if possible?!), ad cosequetly US 1 : it meas that a) we eglect ay alterative solutios, b) we eglect admissible solutios, c) we are too bold to assume that all iputs could be solutios; withi the SSM framework these questios are still more obscure: i case of FDT the questio is whether S is kow to us; it is the same with SSM case; operator U is questioable i both cases either; the two operators U ad S i FED are tacitly assumed to be ucertai: do we use it i a SSM case (or as determiistic operators)? i FDT case: are we sure that Q ~ is sufficiet for our decisio o X * d? What about SSM case? a similar doubt as to the operators U ad S to hold true for all other operators i FED as well as i SSM case ; a trasitio from Q to Q ~ has ot bee examied whatsoever; it has bee a reflectio of our dagerous oversimplificatio of FDT decisio proces; a bridge betwee hard ad soft scieces is ofte demolished by usig U= idetity operator which is, i geeral, very far from beig realistic ad adequate approach; it may well hape that a/the solutio y Ym is acceptable from techical aspects oly, but ot from the others; i FDT case where we are ot worried about makig some adequate sapshots of to get problem space Q, or, eve worse, Q ~? How about SSM case? 46

5 As we may coclude, it is ot possible to derive the fudametal equatio of decisio theory (FED): X * d ( US) X as a couterpart to SSM. 4. EXAMPLES ON SOME REAL LIFE PROJECTS There have bee several projects at the earest past which iduced our tempatio to exercise SSM approach. To exhibit some most importat features we chose the three projects: A: The miimal methodology of maagemet ad cotrol of agriculture developmet i Sloveia (see /3/); B: Network ecoomics modellig o electroic data iterchage, developed for ATNET (Advaced techoilogy etwork) (see /4/); C. The aalysis of New York Stock Exchage operatios (see /5/) The reappraisal of the above projects had bee focused i the light of SSM-FDT compariso. The mai fidigs are listed below resultig project A project B project C features closer to SSM fully FDT defied well FDT defied Q large dimesioal FDT space, stochastic small dimesioal FDT space, stochastic variable modest dimesioal FDT space, y Y m stochastic, closer to SSM space ad stochastic o-formal operators (see /8/) stochastic FDT space determiistic FDT operators stochastic ot defied stochastic FDT operators loose looped feedback stochastic FDT operators mild stochasticity operator (see /6/) Q ~ partitio subject to iteractio aalysis partitio arbitrary fixed SSM coditioal partitio X * d o observability o observability o observability Z* cotrollabilty via Q ~ suspected of wild stochasticity, of SSM type fully eabled, of FDT type mild stochasticity space, of FDT type U requires fluid modellig (see /7/) piecewise determiistic fuctioal o-existig 5. A PARTIAL CONCLUSION It follows from the examples above that either SSM or FDT is apt to serve as a complete ad satisfactory device of modellig. Each of them should somehow be modified to offset the real situatio. Modificatios are expected to take place at differet costituets of each particular type of modellig discussed above. 47

6 REFERENCES /1/ Rupik V., (2005),»O Some Trappig States to OR«, Proceedigs of the 8th Iteratioal Symposium o OR, Nova Gorica, Sloveia, Sept , pp /2/ Checklad P. ad Scholes J., (1990), Soft Systems Methodology i Actio, J.Wiley &Sos. /3/ Rupik V., (2006),»The miimal methodology of maagemet ad cotrol of agriculture developmet i Sloveia«, project proposal to Chamber of Agriculture ad Forestry, Sloveia. /4/ Rupik V.,(1995),»Network ecoomics modellig o electroic data iterchage«, project developed for ATNET (Advaced techology etwork), Koper, Sloveia. /5/ Rupik V.,»The aalysis of New York Stock Exchage operatios«, project beig developed for INTERACTA, Busiess Iformatio Egieerig, Ljubljaa, Sloveia, sice 2002 o. /6/ Volcjak R., (2000),»Applicability Limits to the Quality of Ecoomic Modellig«, PhD thesis, Departmet of Ecoomics, Uiversity of Ljubljaa. /7/ Rupik V., (1985),»Some Experieces with Fluid Modellig of Ecoomic Systems«, Systems Research, The Official Joural of the Iteratioal Federatio for System Research, Vol. 2, No.3, pp /8/ Rupik V., (1997),»A Attempt to No-Formal Modellig«, Proceedigs of the Fourth Iteratioal Symposium o Operatioal Research, Preddvor Sloveia, Oct , pp

6.3 Testing Series With Positive Terms

6.3 Testing Series With Positive Terms 6.3. TESTING SERIES WITH POSITIVE TERMS 307 6.3 Testig Series With Positive Terms 6.3. Review of what is kow up to ow I theory, testig a series a i for covergece amouts to fidig the i= sequece of partial

More information

Series III. Chapter Alternating Series

Series III. Chapter Alternating Series Chapter 9 Series III With the exceptio of the Null Sequece Test, all the tests for series covergece ad divergece that we have cosidered so far have dealt oly with series of oegative terms. Series with

More information

NUMERICAL METHODS FOR SOLVING EQUATIONS

NUMERICAL METHODS FOR SOLVING EQUATIONS Mathematics Revisio Guides Numerical Methods for Solvig Equatios Page 1 of 11 M.K. HOME TUITION Mathematics Revisio Guides Level: GCSE Higher Tier NUMERICAL METHODS FOR SOLVING EQUATIONS Versio:. Date:

More information

4.3 Growth Rates of Solutions to Recurrences

4.3 Growth Rates of Solutions to Recurrences 4.3. GROWTH RATES OF SOLUTIONS TO RECURRENCES 81 4.3 Growth Rates of Solutios to Recurreces 4.3.1 Divide ad Coquer Algorithms Oe of the most basic ad powerful algorithmic techiques is divide ad coquer.

More information

MA131 - Analysis 1. Workbook 9 Series III

MA131 - Analysis 1. Workbook 9 Series III MA3 - Aalysis Workbook 9 Series III Autum 004 Cotets 4.4 Series with Positive ad Negative Terms.............. 4.5 Alteratig Series.......................... 4.6 Geeral Series.............................

More information

Commutativity in Permutation Groups

Commutativity in Permutation Groups Commutativity i Permutatio Groups Richard Wito, PhD Abstract I the group Sym(S) of permutatios o a oempty set S, fixed poits ad trasiet poits are defied Prelimiary results o fixed ad trasiet poits are

More information

EE / EEE SAMPLE STUDY MATERIAL. GATE, IES & PSUs Signal System. Electrical Engineering. Postal Correspondence Course

EE / EEE SAMPLE STUDY MATERIAL. GATE, IES & PSUs Signal System. Electrical Engineering. Postal Correspondence Course Sigal-EE Postal Correspodece Course 1 SAMPLE STUDY MATERIAL Electrical Egieerig EE / EEE Postal Correspodece Course GATE, IES & PSUs Sigal System Sigal-EE Postal Correspodece Course CONTENTS 1. SIGNAL

More information

Lecture XVI - Lifting of paths and homotopies

Lecture XVI - Lifting of paths and homotopies Lecture XVI - Liftig of paths ad homotopies I the last lecture we discussed the liftig problem ad proved that the lift if it exists is uiquely determied by its value at oe poit. I this lecture we shall

More information

Lectures on Stochastic System Analysis and Bayesian Updating

Lectures on Stochastic System Analysis and Bayesian Updating Lectures o Stochastic System Aalysis ad Bayesia Updatig Jue 29-July 13 2005 James L. Beck, Califoria Istitute of Techology Jiaye Chig, Natioal Taiwa Uiversity of Sciece & Techology Siu-Kui (Iva) Au, Nayag

More information

Alternating Series. 1 n 0 2 n n THEOREM 9.14 Alternating Series Test Let a n > 0. The alternating series. 1 n a n.

Alternating Series. 1 n 0 2 n n THEOREM 9.14 Alternating Series Test Let a n > 0. The alternating series. 1 n a n. 0_0905.qxd //0 :7 PM Page SECTION 9.5 Alteratig Series Sectio 9.5 Alteratig Series Use the Alteratig Series Test to determie whether a ifiite series coverges. Use the Alteratig Series Remaider to approximate

More information

Chapter 6 Infinite Series

Chapter 6 Infinite Series Chapter 6 Ifiite Series I the previous chapter we cosidered itegrals which were improper i the sese that the iterval of itegratio was ubouded. I this chapter we are goig to discuss a topic which is somewhat

More information

SRC Technical Note June 17, Tight Thresholds for The Pure Literal Rule. Michael Mitzenmacher. d i g i t a l

SRC Technical Note June 17, Tight Thresholds for The Pure Literal Rule. Michael Mitzenmacher. d i g i t a l SRC Techical Note 1997-011 Jue 17, 1997 Tight Thresholds for The Pure Literal Rule Michael Mitzemacher d i g i t a l Systems Research Ceter 130 Lytto Aveue Palo Alto, Califoria 94301 http://www.research.digital.com/src/

More information

10.6 ALTERNATING SERIES

10.6 ALTERNATING SERIES 0.6 Alteratig Series Cotemporary Calculus 0.6 ALTERNATING SERIES I the last two sectios we cosidered tests for the covergece of series whose terms were all positive. I this sectio we examie series whose

More information

The Riemann Zeta Function

The Riemann Zeta Function Physics 6A Witer 6 The Riema Zeta Fuctio I this ote, I will sketch some of the mai properties of the Riema zeta fuctio, ζ(x). For x >, we defie ζ(x) =, x >. () x = For x, this sum diverges. However, we

More information

ANALYSIS OF EXPERIMENTAL ERRORS

ANALYSIS OF EXPERIMENTAL ERRORS ANALYSIS OF EXPERIMENTAL ERRORS All physical measuremets ecoutered i the verificatio of physics theories ad cocepts are subject to ucertaities that deped o the measurig istrumets used ad the coditios uder

More information

sin(n) + 2 cos(2n) n 3/2 3 sin(n) 2cos(2n) n 3/2 a n =

sin(n) + 2 cos(2n) n 3/2 3 sin(n) 2cos(2n) n 3/2 a n = 60. Ratio ad root tests 60.1. Absolutely coverget series. Defiitio 13. (Absolute covergece) A series a is called absolutely coverget if the series of absolute values a is coverget. The absolute covergece

More information

Product measures, Tonelli s and Fubini s theorems For use in MAT3400/4400, autumn 2014 Nadia S. Larsen. Version of 13 October 2014.

Product measures, Tonelli s and Fubini s theorems For use in MAT3400/4400, autumn 2014 Nadia S. Larsen. Version of 13 October 2014. Product measures, Toelli s ad Fubii s theorems For use i MAT3400/4400, autum 2014 Nadia S. Larse Versio of 13 October 2014. 1. Costructio of the product measure The purpose of these otes is to preset the

More information

Research Article Some E-J Generalized Hausdorff Matrices Not of Type M

Research Article Some E-J Generalized Hausdorff Matrices Not of Type M Abstract ad Applied Aalysis Volume 2011, Article ID 527360, 5 pages doi:10.1155/2011/527360 Research Article Some E-J Geeralized Hausdorff Matrices Not of Type M T. Selmaogullari, 1 E. Savaş, 2 ad B. E.

More information

The Growth of Functions. Theoretical Supplement

The Growth of Functions. Theoretical Supplement The Growth of Fuctios Theoretical Supplemet The Triagle Iequality The triagle iequality is a algebraic tool that is ofte useful i maipulatig absolute values of fuctios. The triagle iequality says that

More information

Math 113 Exam 3 Practice

Math 113 Exam 3 Practice Math Exam Practice Exam will cover.-.9. This sheet has three sectios. The first sectio will remid you about techiques ad formulas that you should kow. The secod gives a umber of practice questios for you

More information

Physics 116A Solutions to Homework Set #1 Winter Boas, problem Use equation 1.8 to find a fraction describing

Physics 116A Solutions to Homework Set #1 Winter Boas, problem Use equation 1.8 to find a fraction describing Physics 6A Solutios to Homework Set # Witer 0. Boas, problem. 8 Use equatio.8 to fid a fractio describig 0.694444444... Start with the formula S = a, ad otice that we ca remove ay umber of r fiite decimals

More information

MA131 - Analysis 1. Workbook 3 Sequences II

MA131 - Analysis 1. Workbook 3 Sequences II MA3 - Aalysis Workbook 3 Sequeces II Autum 2004 Cotets 2.8 Coverget Sequeces........................ 2.9 Algebra of Limits......................... 2 2.0 Further Useful Results........................

More information

A collocation method for singular integral equations with cosecant kernel via Semi-trigonometric interpolation

A collocation method for singular integral equations with cosecant kernel via Semi-trigonometric interpolation Iteratioal Joural of Mathematics Research. ISSN 0976-5840 Volume 9 Number 1 (017) pp. 45-51 Iteratioal Research Publicatio House http://www.irphouse.com A collocatio method for sigular itegral equatios

More information

ACO Comprehensive Exam 9 October 2007 Student code A. 1. Graph Theory

ACO Comprehensive Exam 9 October 2007 Student code A. 1. Graph Theory 1. Graph Theory Prove that there exist o simple plaar triagulatio T ad two distict adjacet vertices x, y V (T ) such that x ad y are the oly vertices of T of odd degree. Do ot use the Four-Color Theorem.

More information

Beurling Integers: Part 2

Beurling Integers: Part 2 Beurlig Itegers: Part 2 Isomorphisms Devi Platt July 11, 2015 1 Prime Factorizatio Sequeces I the last article we itroduced the Beurlig geeralized itegers, which ca be represeted as a sequece of real umbers

More information

Similarity between quantum mechanics and thermodynamics: Entropy, temperature, and Carnot cycle

Similarity between quantum mechanics and thermodynamics: Entropy, temperature, and Carnot cycle Similarity betwee quatum mechaics ad thermodyamics: Etropy, temperature, ad Carot cycle Sumiyoshi Abe 1,,3 ad Shiji Okuyama 1 1 Departmet of Physical Egieerig, Mie Uiversity, Mie 514-8507, Japa Istitut

More information

Solutions to Tutorial 5 (Week 6)

Solutions to Tutorial 5 (Week 6) The Uiversity of Sydey School of Mathematics ad Statistics Solutios to Tutorial 5 (Wee 6 MATH2962: Real ad Complex Aalysis (Advaced Semester, 207 Web Page: http://www.maths.usyd.edu.au/u/ug/im/math2962/

More information

Statistics 511 Additional Materials

Statistics 511 Additional Materials Cofidece Itervals o mu Statistics 511 Additioal Materials This topic officially moves us from probability to statistics. We begi to discuss makig ifereces about the populatio. Oe way to differetiate probability

More information

Definition 4.2. (a) A sequence {x n } in a Banach space X is a basis for X if. unique scalars a n (x) such that x = n. a n (x) x n. (4.

Definition 4.2. (a) A sequence {x n } in a Banach space X is a basis for X if. unique scalars a n (x) such that x = n. a n (x) x n. (4. 4. BASES I BAACH SPACES 39 4. BASES I BAACH SPACES Sice a Baach space X is a vector space, it must possess a Hamel, or vector space, basis, i.e., a subset {x γ } γ Γ whose fiite liear spa is all of X ad

More information

Inverse Matrix. A meaning that matrix B is an inverse of matrix A.

Inverse Matrix. A meaning that matrix B is an inverse of matrix A. Iverse Matrix Two square matrices A ad B of dimesios are called iverses to oe aother if the followig holds, AB BA I (11) The otio is dual but we ofte write 1 B A meaig that matrix B is a iverse of matrix

More information

Research Article A New Second-Order Iteration Method for Solving Nonlinear Equations

Research Article A New Second-Order Iteration Method for Solving Nonlinear Equations Abstract ad Applied Aalysis Volume 2013, Article ID 487062, 4 pages http://dx.doi.org/10.1155/2013/487062 Research Article A New Secod-Order Iteratio Method for Solvig Noliear Equatios Shi Mi Kag, 1 Arif

More information

Linear Programming and the Simplex Method

Linear Programming and the Simplex Method Liear Programmig ad the Simplex ethod Abstract This article is a itroductio to Liear Programmig ad usig Simplex method for solvig LP problems i primal form. What is Liear Programmig? Liear Programmig is

More information

Teaching Mathematics Concepts via Computer Algebra Systems

Teaching Mathematics Concepts via Computer Algebra Systems Iteratioal Joural of Mathematics ad Statistics Ivetio (IJMSI) E-ISSN: 4767 P-ISSN: - 4759 Volume 4 Issue 7 September. 6 PP-- Teachig Mathematics Cocepts via Computer Algebra Systems Osama Ajami Rashaw,

More information

Lecture Notes for Analysis Class

Lecture Notes for Analysis Class Lecture Notes for Aalysis Class Topological Spaces A topology for a set X is a collectio T of subsets of X such that: (a) X ad the empty set are i T (b) Uios of elemets of T are i T (c) Fiite itersectios

More information

Generating Functions for Laguerre Type Polynomials. Group Theoretic method

Generating Functions for Laguerre Type Polynomials. Group Theoretic method It. Joural of Math. Aalysis, Vol. 4, 2010, o. 48, 257-266 Geeratig Fuctios for Laguerre Type Polyomials α of Two Variables L ( xy, ) by Usig Group Theoretic method Ajay K. Shula* ad Sriata K. Meher** *Departmet

More information

On an Application of Bayesian Estimation

On an Application of Bayesian Estimation O a Applicatio of ayesia Estimatio KIYOHARU TANAKA School of Sciece ad Egieerig, Kiki Uiversity, Kowakae, Higashi-Osaka, JAPAN Email: ktaaka@ifokidaiacjp EVGENIY GRECHNIKOV Departmet of Mathematics, auma

More information

Comparison Study of Series Approximation. and Convergence between Chebyshev. and Legendre Series

Comparison Study of Series Approximation. and Convergence between Chebyshev. and Legendre Series Applied Mathematical Scieces, Vol. 7, 03, o. 6, 3-337 HIKARI Ltd, www.m-hikari.com http://d.doi.org/0.988/ams.03.3430 Compariso Study of Series Approimatio ad Covergece betwee Chebyshev ad Legedre Series

More information

5.6 Absolute Convergence and The Ratio and Root Tests

5.6 Absolute Convergence and The Ratio and Root Tests 5.6 Absolute Covergece ad The Ratio ad Root Tests Bria E. Veitch 5.6 Absolute Covergece ad The Ratio ad Root Tests Recall from our previous sectio that diverged but ( ) coverged. Both of these sequeces

More information

Lesson 10: Limits and Continuity

Lesson 10: Limits and Continuity www.scimsacademy.com Lesso 10: Limits ad Cotiuity SCIMS Academy 1 Limit of a fuctio The cocept of limit of a fuctio is cetral to all other cocepts i calculus (like cotiuity, derivative, defiite itegrals

More information

Notes for Lecture 5. 1 Grover Search. 1.1 The Setting. 1.2 Motivation. Lecture 5 (September 26, 2018)

Notes for Lecture 5. 1 Grover Search. 1.1 The Setting. 1.2 Motivation. Lecture 5 (September 26, 2018) COS 597A: Quatum Cryptography Lecture 5 (September 6, 08) Lecturer: Mark Zhadry Priceto Uiversity Scribe: Fermi Ma Notes for Lecture 5 Today we ll move o from the slightly cotrived applicatios of quatum

More information

Riemann Sums y = f (x)

Riemann Sums y = f (x) Riema Sums Recall that we have previously discussed the area problem I its simplest form we ca state it this way: The Area Problem Let f be a cotiuous, o-egative fuctio o the closed iterval [a, b] Fid

More information

Lecture 2: April 3, 2013

Lecture 2: April 3, 2013 TTIC/CMSC 350 Mathematical Toolkit Sprig 203 Madhur Tulsiai Lecture 2: April 3, 203 Scribe: Shubhedu Trivedi Coi tosses cotiued We retur to the coi tossig example from the last lecture agai: Example. Give,

More information

Linear Regression Demystified

Linear Regression Demystified Liear Regressio Demystified Liear regressio is a importat subject i statistics. I elemetary statistics courses, formulae related to liear regressio are ofte stated without derivatio. This ote iteds to

More information

Research Article Approximate Riesz Algebra-Valued Derivations

Research Article Approximate Riesz Algebra-Valued Derivations Abstract ad Applied Aalysis Volume 2012, Article ID 240258, 5 pages doi:10.1155/2012/240258 Research Article Approximate Riesz Algebra-Valued Derivatios Faruk Polat Departmet of Mathematics, Faculty of

More information

Chapter 6 Sampling Distributions

Chapter 6 Sampling Distributions Chapter 6 Samplig Distributios 1 I most experimets, we have more tha oe measuremet for ay give variable, each measuremet beig associated with oe radomly selected a member of a populatio. Hece we eed to

More information

MAT1026 Calculus II Basic Convergence Tests for Series

MAT1026 Calculus II Basic Convergence Tests for Series MAT026 Calculus II Basic Covergece Tests for Series Egi MERMUT 202.03.08 Dokuz Eylül Uiversity Faculty of Sciece Departmet of Mathematics İzmir/TURKEY Cotets Mootoe Covergece Theorem 2 2 Series of Real

More information

ROLL CUTTING PROBLEMS UNDER STOCHASTIC DEMAND

ROLL CUTTING PROBLEMS UNDER STOCHASTIC DEMAND Pacific-Asia Joural of Mathematics, Volume 5, No., Jauary-Jue 20 ROLL CUTTING PROBLEMS UNDER STOCHASTIC DEMAND SHAKEEL JAVAID, Z. H. BAKHSHI & M. M. KHALID ABSTRACT: I this paper, the roll cuttig problem

More information

CHAPTER 10 INFINITE SEQUENCES AND SERIES

CHAPTER 10 INFINITE SEQUENCES AND SERIES CHAPTER 10 INFINITE SEQUENCES AND SERIES 10.1 Sequeces 10.2 Ifiite Series 10.3 The Itegral Tests 10.4 Compariso Tests 10.5 The Ratio ad Root Tests 10.6 Alteratig Series: Absolute ad Coditioal Covergece

More information

11 Hidden Markov Models

11 Hidden Markov Models Hidde Markov Models Hidde Markov Models are a popular machie learig approach i bioiformatics. Machie learig algorithms are preseted with traiig data, which are used to derive importat isights about the

More information

Stability Analysis of the Euler Discretization for SIR Epidemic Model

Stability Analysis of the Euler Discretization for SIR Epidemic Model Stability Aalysis of the Euler Discretizatio for SIR Epidemic Model Agus Suryato Departmet of Mathematics, Faculty of Scieces, Brawijaya Uiversity, Jl Vetera Malag 6545 Idoesia Abstract I this paper we

More information

Math 113, Calculus II Winter 2007 Final Exam Solutions

Math 113, Calculus II Winter 2007 Final Exam Solutions Math, Calculus II Witer 7 Fial Exam Solutios (5 poits) Use the limit defiitio of the defiite itegral ad the sum formulas to compute x x + dx The check your aswer usig the Evaluatio Theorem Solutio: I this

More information

Zeros of Polynomials

Zeros of Polynomials Math 160 www.timetodare.com 4.5 4.6 Zeros of Polyomials I these sectios we will study polyomials algebraically. Most of our work will be cocered with fidig the solutios of polyomial equatios of ay degree

More information

Similarity Solutions to Unsteady Pseudoplastic. Flow Near a Moving Wall

Similarity Solutions to Unsteady Pseudoplastic. Flow Near a Moving Wall Iteratioal Mathematical Forum, Vol. 9, 04, o. 3, 465-475 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/0.988/imf.04.48 Similarity Solutios to Usteady Pseudoplastic Flow Near a Movig Wall W. Robi Egieerig

More information

The Method of Least Squares. To understand least squares fitting of data.

The Method of Least Squares. To understand least squares fitting of data. The Method of Least Squares KEY WORDS Curve fittig, least square GOAL To uderstad least squares fittig of data To uderstad the least squares solutio of icosistet systems of liear equatios 1 Motivatio Curve

More information

1 Adiabatic and diabatic representations

1 Adiabatic and diabatic representations 1 Adiabatic ad diabatic represetatios 1.1 Bor-Oppeheimer approximatio The time-idepedet Schrödiger equatio for both electroic ad uclear degrees of freedom is Ĥ Ψ(r, R) = E Ψ(r, R), (1) where the full molecular

More information

Analytic Theory of Probabilities

Analytic Theory of Probabilities Aalytic Theory of Probabilities PS Laplace Book II Chapter II, 4 pp 94 03 4 A lottery beig composed of umbered tickets of which r exit at each drawig, oe requires the probability that after i drawigs all

More information

Please do NOT write in this box. Multiple Choice. Total

Please do NOT write in this box. Multiple Choice. Total Istructor: Math 0560, Worksheet Alteratig Series Jauary, 3000 For realistic exam practice solve these problems without lookig at your book ad without usig a calculator. Multiple choice questios should

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 19 11/17/2008 LAWS OF LARGE NUMBERS II THE STRONG LAW OF LARGE NUMBERS

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 19 11/17/2008 LAWS OF LARGE NUMBERS II THE STRONG LAW OF LARGE NUMBERS MASSACHUSTTS INSTITUT OF TCHNOLOGY 6.436J/5.085J Fall 2008 Lecture 9 /7/2008 LAWS OF LARG NUMBRS II Cotets. The strog law of large umbers 2. The Cheroff boud TH STRONG LAW OF LARG NUMBRS While the weak

More information

3.2 Properties of Division 3.3 Zeros of Polynomials 3.4 Complex and Rational Zeros of Polynomials

3.2 Properties of Division 3.3 Zeros of Polynomials 3.4 Complex and Rational Zeros of Polynomials Math 60 www.timetodare.com 3. Properties of Divisio 3.3 Zeros of Polyomials 3.4 Complex ad Ratioal Zeros of Polyomials I these sectios we will study polyomials algebraically. Most of our work will be cocered

More information

Numerical Conformal Mapping via a Fredholm Integral Equation using Fourier Method ABSTRACT INTRODUCTION

Numerical Conformal Mapping via a Fredholm Integral Equation using Fourier Method ABSTRACT INTRODUCTION alaysia Joural of athematical Scieces 3(1): 83-93 (9) umerical Coformal appig via a Fredholm Itegral Equatio usig Fourier ethod 1 Ali Hassa ohamed urid ad Teh Yua Yig 1, Departmet of athematics, Faculty

More information

Integrable Functions. { f n } is called a determining sequence for f. If f is integrable with respect to, then f d does exist as a finite real number

Integrable Functions. { f n } is called a determining sequence for f. If f is integrable with respect to, then f d does exist as a finite real number MATH 532 Itegrable Fuctios Dr. Neal, WKU We ow shall defie what it meas for a measurable fuctio to be itegrable, show that all itegral properties of simple fuctios still hold, ad the give some coditios

More information

ABOUT CHAOS AND SENSITIVITY IN TOPOLOGICAL DYNAMICS

ABOUT CHAOS AND SENSITIVITY IN TOPOLOGICAL DYNAMICS ABOUT CHAOS AND SENSITIVITY IN TOPOLOGICAL DYNAMICS EDUARD KONTOROVICH Abstract. I this work we uify ad geeralize some results about chaos ad sesitivity. Date: March 1, 005. 1 1. Symbolic Dyamics Defiitio

More information

Research Article A Unified Weight Formula for Calculating the Sample Variance from Weighted Successive Differences

Research Article A Unified Weight Formula for Calculating the Sample Variance from Weighted Successive Differences Discrete Dyamics i Nature ad Society Article ID 210761 4 pages http://dxdoiorg/101155/2014/210761 Research Article A Uified Weight Formula for Calculatig the Sample Variace from Weighted Successive Differeces

More information

DECOMPOSITION METHOD FOR SOLVING A SYSTEM OF THIRD-ORDER BOUNDARY VALUE PROBLEMS. Park Road, Islamabad, Pakistan

DECOMPOSITION METHOD FOR SOLVING A SYSTEM OF THIRD-ORDER BOUNDARY VALUE PROBLEMS. Park Road, Islamabad, Pakistan Mathematical ad Computatioal Applicatios, Vol. 9, No. 3, pp. 30-40, 04 DECOMPOSITION METHOD FOR SOLVING A SYSTEM OF THIRD-ORDER BOUNDARY VALUE PROBLEMS Muhammad Aslam Noor, Khalida Iayat Noor ad Asif Waheed

More information

On forward improvement iteration for stopping problems

On forward improvement iteration for stopping problems O forward improvemet iteratio for stoppig problems Mathematical Istitute, Uiversity of Kiel, Ludewig-Mey-Str. 4, D-24098 Kiel, Germay irle@math.ui-iel.de Albrecht Irle Abstract. We cosider the optimal

More information

lim za n n = z lim a n n.

lim za n n = z lim a n n. Lecture 6 Sequeces ad Series Defiitio 1 By a sequece i a set A, we mea a mappig f : N A. It is customary to deote a sequece f by {s } where, s := f(). A sequece {z } of (complex) umbers is said to be coverget

More information

Bi-criteria Scheduling on Parallel Machines Under Fuzzy Processing Time

Bi-criteria Scheduling on Parallel Machines Under Fuzzy Processing Time 22d Iteratioal Cogress o Modellig ad Simulatio, Hobart, Tasmaia, Australia, 3 to 8 December 207 mssaz.org.au/modsim207 Bi-criteria Schedulig o Parallel Machies Uder Fuzzy Processig Time Sameer Sharma a,

More information

7 Sequences of real numbers

7 Sequences of real numbers 40 7 Sequeces of real umbers 7. Defiitios ad examples Defiitio 7... A sequece of real umbers is a real fuctio whose domai is the set N of atural umbers. Let s : N R be a sequece. The the values of s are

More information

The target reliability and design working life

The target reliability and design working life Safety ad Security Egieerig IV 161 The target reliability ad desig workig life M. Holický Kloker Istitute, CTU i Prague, Czech Republic Abstract Desig workig life ad target reliability levels recommeded

More information

Seed and Sieve of Odd Composite Numbers with Applications in Factorization of Integers

Seed and Sieve of Odd Composite Numbers with Applications in Factorization of Integers IOSR Joural of Mathematics (IOSR-JM) e-issn: 78-578, p-issn: 319-75X. Volume 1, Issue 5 Ver. VIII (Sep. - Oct.01), PP 01-07 www.iosrjourals.org Seed ad Sieve of Odd Composite Numbers with Applicatios i

More information

An Algebraic Elimination Method for the Linear Complementarity Problem

An Algebraic Elimination Method for the Linear Complementarity Problem Volume-3, Issue-5, October-2013 ISSN No: 2250-0758 Iteratioal Joural of Egieerig ad Maagemet Research Available at: wwwijemret Page Number: 51-55 A Algebraic Elimiatio Method for the Liear Complemetarity

More information

Chapter 6 Principles of Data Reduction

Chapter 6 Principles of Data Reduction Chapter 6 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 0 Chapter 6 Priciples of Data Reductio Sectio 6. Itroductio Goal: To summarize or reduce the data X, X,, X to get iformatio about a

More information

Entropy Rates and Asymptotic Equipartition

Entropy Rates and Asymptotic Equipartition Chapter 29 Etropy Rates ad Asymptotic Equipartitio Sectio 29. itroduces the etropy rate the asymptotic etropy per time-step of a stochastic process ad shows that it is well-defied; ad similarly for iformatio,

More information

( ) ( ), (S3) ( ). (S4)

( ) ( ), (S3) ( ). (S4) Ultrasesitivity i phosphorylatio-dephosphorylatio cycles with little substrate: Supportig Iformatio Bruo M.C. Martis, eter S. Swai 1. Derivatio of the equatios associated with the mai model From the differetial

More information

Finite Difference Derivations for Spreadsheet Modeling John C. Walton Modified: November 15, 2007 jcw

Finite Difference Derivations for Spreadsheet Modeling John C. Walton Modified: November 15, 2007 jcw Fiite Differece Derivatios for Spreadsheet Modelig Joh C. Walto Modified: November 15, 2007 jcw Figure 1. Suset with 11 swas o Little Platte Lake, Michiga. Page 1 Modificatio Date: November 15, 2007 Review

More information

Convergence of Random SP Iterative Scheme

Convergence of Random SP Iterative Scheme Applied Mathematical Scieces, Vol. 7, 2013, o. 46, 2283-2293 HIKARI Ltd, www.m-hikari.com Covergece of Radom SP Iterative Scheme 1 Reu Chugh, 2 Satish Narwal ad 3 Vivek Kumar 1,2,3 Departmet of Mathematics,

More information

Some New Iterative Methods for Solving Nonlinear Equations

Some New Iterative Methods for Solving Nonlinear Equations World Applied Scieces Joural 0 (6): 870-874, 01 ISSN 1818-495 IDOSI Publicatios, 01 DOI: 10.589/idosi.wasj.01.0.06.830 Some New Iterative Methods for Solvig Noliear Equatios Muhammad Aslam Noor, Khalida

More information

(3) If you replace row i of A by its sum with a multiple of another row, then the determinant is unchanged! Expand across the i th row:

(3) If you replace row i of A by its sum with a multiple of another row, then the determinant is unchanged! Expand across the i th row: Math 5-4 Tue Feb 4 Cotiue with sectio 36 Determiats The effective way to compute determiats for larger-sized matrices without lots of zeroes is to ot use the defiitio, but rather to use the followig facts,

More information

GUIDELINES ON REPRESENTATIVE SAMPLING

GUIDELINES ON REPRESENTATIVE SAMPLING DRUGS WORKING GROUP VALIDATION OF THE GUIDELINES ON REPRESENTATIVE SAMPLING DOCUMENT TYPE : REF. CODE: ISSUE NO: ISSUE DATE: VALIDATION REPORT DWG-SGL-001 002 08 DECEMBER 2012 Ref code: DWG-SGL-001 Issue

More information

FIR Filter Design: Part II

FIR Filter Design: Part II EEL335: Discrete-Time Sigals ad Systems. Itroductio I this set of otes, we cosider how we might go about desigig FIR filters with arbitrary frequecy resposes, through compositio of multiple sigle-peak

More information

A Fixed Point Result Using a Function of 5-Variables

A Fixed Point Result Using a Function of 5-Variables Joural of Physical Scieces, Vol., 2007, 57-6 Fixed Poit Result Usig a Fuctio of 5-Variables P. N. Dutta ad Biayak S. Choudhury Departmet of Mathematics Begal Egieerig ad Sciece Uiversity, Shibpur P.O.:

More information

Math 257: Finite difference methods

Math 257: Finite difference methods Math 257: Fiite differece methods 1 Fiite Differeces Remember the defiitio of a derivative f f(x + ) f(x) (x) = lim 0 Also recall Taylor s formula: (1) f(x + ) = f(x) + f (x) + 2 f (x) + 3 f (3) (x) +...

More information

Optimally Sparse SVMs

Optimally Sparse SVMs A. Proof of Lemma 3. We here prove a lower boud o the umber of support vectors to achieve geeralizatio bouds of the form which we cosider. Importatly, this result holds ot oly for liear classifiers, but

More information

The Ratio Test. THEOREM 9.17 Ratio Test Let a n be a series with nonzero terms. 1. a. n converges absolutely if lim. n 1

The Ratio Test. THEOREM 9.17 Ratio Test Let a n be a series with nonzero terms. 1. a. n converges absolutely if lim. n 1 460_0906.qxd //04 :8 PM Page 69 SECTION 9.6 The Ratio ad Root Tests 69 Sectio 9.6 EXPLORATION Writig a Series Oe of the followig coditios guaratees that a series will diverge, two coditios guaratee that

More information

LECTURE 11: POSTNIKOV AND WHITEHEAD TOWERS

LECTURE 11: POSTNIKOV AND WHITEHEAD TOWERS LECTURE 11: POSTNIKOV AND WHITEHEAD TOWERS I the previous sectio we used the techique of adjoiig cells i order to costruct CW approximatios for arbitrary spaces Here we will see that the same techique

More information

4.1 Data processing inequality

4.1 Data processing inequality ECE598: Iformatio-theoretic methods i high-dimesioal statistics Sprig 206 Lecture 4: Total variatio/iequalities betwee f-divergeces Lecturer: Yihog Wu Scribe: Matthew Tsao, Feb 8, 206 [Ed. Mar 22] Recall

More information

MOST PEOPLE WOULD RATHER LIVE WITH A PROBLEM THEY CAN'T SOLVE, THAN ACCEPT A SOLUTION THEY CAN'T UNDERSTAND.

MOST PEOPLE WOULD RATHER LIVE WITH A PROBLEM THEY CAN'T SOLVE, THAN ACCEPT A SOLUTION THEY CAN'T UNDERSTAND. XI-1 (1074) MOST PEOPLE WOULD RATHER LIVE WITH A PROBLEM THEY CAN'T SOLVE, THAN ACCEPT A SOLUTION THEY CAN'T UNDERSTAND. R. E. D. WOOLSEY AND H. S. SWANSON XI-2 (1075) STATISTICAL DECISION MAKING Advaced

More information

Orthogonal Gaussian Filters for Signal Processing

Orthogonal Gaussian Filters for Signal Processing Orthogoal Gaussia Filters for Sigal Processig Mark Mackezie ad Kiet Tieu Mechaical Egieerig Uiversity of Wollogog.S.W. Australia Abstract A Gaussia filter usig the Hermite orthoormal series of fuctios

More information

Stochastic Matrices in a Finite Field

Stochastic Matrices in a Finite Field Stochastic Matrices i a Fiite Field Abstract: I this project we will explore the properties of stochastic matrices i both the real ad the fiite fields. We first explore what properties 2 2 stochastic matrices

More information

Chapter 10: Power Series

Chapter 10: Power Series Chapter : Power Series 57 Chapter Overview: Power Series The reaso series are part of a Calculus course is that there are fuctios which caot be itegrated. All power series, though, ca be itegrated because

More information

The Mathematical Model and the Simulation Modelling Algoritm of the Multitiered Mechanical System

The Mathematical Model and the Simulation Modelling Algoritm of the Multitiered Mechanical System The Mathematical Model ad the Simulatio Modellig Algoritm of the Multitiered Mechaical System Demi Aatoliy, Kovalev Iva Dept. of Optical Digital Systems ad Techologies, The St. Petersburg Natioal Research

More information

Structural Functionality as a Fundamental Property of Boolean Algebra and Base for Its Real-Valued Realizations

Structural Functionality as a Fundamental Property of Boolean Algebra and Base for Its Real-Valued Realizations Structural Fuctioality as a Fudametal Property of Boolea Algebra ad Base for Its Real-Valued Realizatios Draga G. Radojević Uiversity of Belgrade, Istitute Mihajlo Pupi, Belgrade draga.radojevic@pupi.rs

More information

Machine Learning Theory Tübingen University, WS 2016/2017 Lecture 11

Machine Learning Theory Tübingen University, WS 2016/2017 Lecture 11 Machie Learig Theory Tübige Uiversity, WS 06/07 Lecture Tolstikhi Ilya Abstract We will itroduce the otio of reproducig kerels ad associated Reproducig Kerel Hilbert Spaces (RKHS). We will cosider couple

More information

Research Article Robust Linear Programming with Norm Uncertainty

Research Article Robust Linear Programming with Norm Uncertainty Joural of Applied Mathematics Article ID 209239 7 pages http://dx.doi.org/0.55/204/209239 Research Article Robust Liear Programmig with Norm Ucertaity Lei Wag ad Hog Luo School of Ecoomic Mathematics Southwester

More information

ECE-S352 Introduction to Digital Signal Processing Lecture 3A Direct Solution of Difference Equations

ECE-S352 Introduction to Digital Signal Processing Lecture 3A Direct Solution of Difference Equations ECE-S352 Itroductio to Digital Sigal Processig Lecture 3A Direct Solutio of Differece Equatios Discrete Time Systems Described by Differece Equatios Uit impulse (sample) respose h() of a DT system allows

More information

Chapter 4. Fourier Series

Chapter 4. Fourier Series Chapter 4. Fourier Series At this poit we are ready to ow cosider the caoical equatios. Cosider, for eample the heat equatio u t = u, < (4.) subject to u(, ) = si, u(, t) = u(, t) =. (4.) Here,

More information

There is no straightforward approach for choosing the warmup period l.

There is no straightforward approach for choosing the warmup period l. B. Maddah INDE 504 Discrete-Evet Simulatio Output Aalysis () Statistical Aalysis for Steady-State Parameters I a otermiatig simulatio, the iterest is i estimatig the log ru steady state measures of performace.

More information

Pb ( a ) = measure of the plausibility of proposition b conditional on the information stated in proposition a. & then using P2

Pb ( a ) = measure of the plausibility of proposition b conditional on the information stated in proposition a. & then using P2 Axioms for Probability Logic Pb ( a ) = measure of the plausibility of propositio b coditioal o the iformatio stated i propositio a For propositios a, b ad c: P: Pb ( a) 0 P2: Pb ( a& b ) = P3: Pb ( a)

More information

A statistical method to determine sample size to estimate characteristic value of soil parameters

A statistical method to determine sample size to estimate characteristic value of soil parameters A statistical method to determie sample size to estimate characteristic value of soil parameters Y. Hojo, B. Setiawa 2 ad M. Suzuki 3 Abstract Sample size is a importat factor to be cosidered i determiig

More information

Mathematical Induction

Mathematical Induction Mathematical Iductio Itroductio Mathematical iductio, or just iductio, is a proof techique. Suppose that for every atural umber, P() is a statemet. We wish to show that all statemets P() are true. I a

More information