Optimal Reliability Allocation
|
|
- Elwin Owen
- 5 years ago
- Views:
Transcription
1 Optmal Relablty Allocato Yashwat K. Malaya Departmet of Computer Scece Colorado State Uversty
2 Relablty Allocato Problem Allocato the relablty values to subsystems to mmze the total cost whle achevg the relablty target. Wdely applcable Software systems Electrcal systems Mechacal systems Implemetato choces Dscrete Cotuous
3 Relablty Allocato Software A software system cossts of may fuctoal modules Some reused, probably wth lower defect destes Some are ew, wth hgher defect destes Some are voked more ofte To crease relablty Addtoal testg Replcated usg -verso programmg? What s the best strategy?
4 Optmal Relablty Allocato System composed of subsystems: Subsystem cost a fucto of relablty System relablty depeds o subsystems Falure rate as a relablty measure Commos systems: seres ad parallel Software system relablty Fractoal executo tme Lagrage multpler: closed form optmal soluto Parameter depedece: sze, defect desty Apportomet & geeral approach
5 Problem Formulato System S has subsystems Ss, =,... Each subsystem SS has a specfc fuctoalty Several choces wth same fuctoalty, but dfferetly relablty levels. C f ( R ) Mmze system cost C s C f ( R ) Subject to
6 Cost mmzato problem thus system R For a seres Subject to S ST s ST R R R R R s R f C C ) ( Mmze
7 Subsystem mplemetato choces Subsystem ca be made more relable by extedg a cotuous attrbute dameter of a colum buldg tme spet for software testg. Dfferet veders mplemetatos of SS at dfferet costs. Multple copes of SS to acheve hgher relablty. double wheels of a truck Number of copes s costraed betwee oe ad a practcal umber because of mplemetato ssues.
8 The Cost fucto Cost fucto f should satsfy these three codtos: f s a postve fucto f s o-decreasg, thus hgher relablty wll come at a hgher cost. f creases at a hgher rate for hgher values of R Relablty vs Cost Steep cost crease Mettas A, Relablty allocato ad optmzato for complex systems. Pro A Relablty ad Mataablty Symp, Jauary 2000, 26-22
9 I terms of falure rate Takg log of both sdes, ad sce R (t) = e -λt Statg cost as a fucto of falure rate R ST R ) l( ) l( S f C C ) ( ST
10 I terms of falure rate: SRGM expoetal software relablty growth model ( d) 0 exp( d) λ 0 depeds o tal defect desty β depeds versely o program sze Restatg t as Cost fucto d( l 0 ) Assumes costat developmet cost, thus eglected
11 Seres ad Parallel Systems: learlzato Costrat Learzato smplfes the calculatos. Seres system l( R ST ) l( R ) Parallel system: log of urelabltes R ST ( R ) Elegbede: If cost fucto satsfes 3 propertes gve above, the cost s optmal f all parallel compoets have the same cost. l( R ST ) (l( R )
12 Relablty Allocato for Software Systems a block s uder executo for a fracto x of the tme where x = Relablty allocato problem Mmze C 0 l
13 Soluto usg Lagrage multpler solutos for the optmal falure rates optmal values of test tmes d ad d, ST x x x x x ST x d 0 l 0 l x x d
14 Observatos: Software relablty allocato A reused subsystem have a hgher relablty because of past testg causg λ λ 0 ad hece egatve d. Soluto: apply allocato problem oly to modules wth postve d. If x s proportoal to the subsystem code sze, the optmal values of the post-test falure rates λ, λ are equal.
15 Ex: Optmal: Software wth 5 blocks λ ST 0.04 Block B B 2 B 3 B 4 B 5 Sze KSLOC I Defect desty β λ x Optmal λ Optmal d Optmal whe all modules have the same falure rate!
16 Ex: Equal testg λ ST 0.04 Block B B 2 B 3 B 4 B 5 Sze KSLOC I Defect desty β λ x λ Equal d If Total test tme s equally dstrbuted for all 5 blocks, system wll have sgfcatly hgher falure rate of per ut tme
17 Ex: Testg oly B5 λ ST 0.04 Block B B 2 B 3 B 4 B 5 Sze KSLOC I Defect desty β λ x λ Equal d If Total test tme s allowed oly for block B5, system wll have hgher falure rate of per ut tme
18 Illustrato usg excel See Excel sheet relallocatoexamples.xls Try chagg etres. 8
19 Commo Apportomet rules Equal relablty apportomet: At ed they all dvdually have falure rate equal to target falure rate for the system Complexty based apportomet test tme apportoed proporto to the software sze Impact based apportomet: A compoet executed more frequetly, or more crtcal, should be assged more resources
20 Relablty Allocato for Complex Systems A teratve approach Desg the system usg fuctoal subsystems. Perform a tal apportomet of cost or relablty attrbutes based o sutable apportomet rules or prelmary computato. Predct system relablty. Is reallocato feasble ad wll ehace the objectve fucto. If so, perform reallocato. Repeat utl optmalty s acheved. Does ths meets objectves? If ot, retur to step ad revsg the desg at a hgher level..
21 Coclusos Relablty allocato: cosder how cost vares wth relablty. Software testg: cost log(/falure rate) sze Relablty allocato systems wth replcated subsystems ca ecouter correlated falures ad thus would eed a more careful modelg. 2
The Firm. The Firm. Maximizing Profits. Decisions. ECON 370: Microeconomic Theory Summer 2004 Rice University Stanley Gilbert
The Frm The Frm ECON 370: Mcroecoomc Theory Summer 004 Rce Uversty Staley Glbert A Frm s a mechasm for covertg labor, captal ad raw materals to desrable goods A frm s owed by cosumers ad operated for the
More informationA Test of Normality. Textbook Reference: Chapter 14.2 (eighth edition, pages 591 3; seventh edition, pages 624 6).
A Test of Normalty Textbook Referece: Chapter 4. (eghth edto, pages 59 ; seveth edto, pages 64 6). The calculato of p-values for hypothess testg typcally s based o the assumpto that the populato dstrbuto
More informationProbability and Statistical Methods. Chapter 8 Fundamental Sampling Distributions
Math 3 Probablty ad Statstcal Methods Chapter 8 Fudametal Samplg Dstrbutos Samplg Dstrbutos I the process of makg a ferece from a sample to a populato we usually calculate oe or more statstcs, such as
More informationProbability and Statistical Methods. Chapter 8 Fundamental Sampling Distributions
Math 3 Probablty ad Statstcal Methods Chapter 8 Fudametal Samplg Dstrbutos Samplg Dstrbutos I the process of makg a ferece from a sample to a populato we usually calculate oe or more statstcs, such as
More informationSorting. Data Structures LECTURE 4. Comparison-based sorting. Sorting algorithms. Quick-Sort. Example (1) Pivot
Data Structures, Sprg 004. Joskowcz Data Structures ECUE 4 Comparso-based sortg Why sortg? Formal aalyss of Quck-Sort Comparso sortg: lower boud Summary of comparso-sortg algorthms Sortg Defto Iput: A
More informationConsult the following resources to familiarize yourself with the issues involved in conducting surveys:
Cofdece Itervals Learg Objectves: After completo of ths module, the studet wll be able to costruct ad terpret cofdece tervals crtcally evaluate the outcomes of surveys terpret the marg of error the cotext
More informationCHAPTER - IV STANDARDIZED CUSUM MEDIAN CONTROL CHART
A Study o Process Varablty usg CUSUM ad Fuzzy Cotrol Charts Ph.D Thess CHAPTER - IV STANDARDIZED CUSUM MEDIAN CONTROL CHART. Itroducto: I motorg e process mea, e Mea ( X ) cotrol charts, ad cumulatve sum
More informationValuation of Asian Option
Mälardales Uversty västerås 202-0-22 Mathematcs ad physcs departmet Project aalytcal face I Valuato of Asa Opto Q A 90402-T077 Jgjg Guo89003-T07 Cotet. Asa opto------------------------------------------------------------------3
More informationScheduling of a Paper Mill Process Considering Environment and Cost
Schedulg of a Paper Mll Process Cosderg Evromet ad Cost M Park, Dogwoo Km, yog Km ad l Moo Departmet of Chemcal Egeerg, Yose Uversty, 34 Shchodog Seodaemooku, Seoul, 0-749, Korea Phoe: +8--363-9375 Emal:
More informationRandom Variables. Discrete Random Variables. Example of a random variable. We will look at: Nitrous Oxide Example. Nitrous Oxide Example
Radom Varables Dscrete Radom Varables Dr. Tom Ilveto BUAD 8 Radom Varables varables that assume umercal values assocated wth radom outcomes from a expermet Radom varables ca be: Dscrete Cotuous We wll
More informationOverview. Linear Models Connectionist and Statistical Language Processing. Numeric Prediction. Example
Overvew Lear Models Coectost ad Statstcal Laguage Processg Frak Keller keller@col.u-sb.de Computerlgustk Uverstät des Saarlades classfcato vs. umerc predcto lear regresso least square estmato evaluatg
More informationSample Survey Design
Sample Survey Desg A Hypotetcal Exposure Scearo () Assume we kow te parameters of a worker s exposure dstrbuto of 8-our TWAs to a cemcal. As t appes, te worker as four dfferet types of days wt regard to
More informationIEOR 130 Methods of Manufacturing Improvement Fall, 2017 Prof. Leachman Solutions to First Homework Assignment
IEOR 130 Methods of Maufacturg Improvemet Fall, 2017 Prof. Leachma Solutos to Frst Homework Assgmet 1. The scheduled output of a fab a partcular week was as follows: Product 1 1,000 uts Product 2 2,000
More informationGene Expression Data Analysis (II) statistical issues in spotted arrays
STATC4 Sprg 005 Lecture Data ad fgures are from Wg Wog s computatoal bology course at Harvard Gee Expresso Data Aalyss (II) statstcal ssues spotted arrays Below shows part of a result fle from mage aalyss
More informationApplication of Portfolio Theory to Support Resource Allocation Decisions for Biosecurity
Applcato of Portfolo Theory to Support Resource Allocato Decsos for Bosecurty Paul Mwebaze Ecoomst 11 September 2013 CES/BIOSECURITY FLAGSHIP Presetato outle The resource allocato problem What ca ecoomcs
More informationMATHEMATICAL MODELLING OF RISK IN PORTFOLIO OPTIMIZATION WITH MEAN- EXTENDED GINI APPROACH
SCIREA Joural of Mathematcs http://www.screa.org/joural/mathematcs December 21, 2016 Volume 1, Issue 2, December 2016 MATHEMATICAL MODELLING OF RISK IN PORTFOLIO OPTIMIZATION WITH MEAN- EXTENDED GINI APPROACH
More informationCHAPTER 8. r E( r ) m e. Reduces the number of inputs for diversification. Easier for security analysts to specialize
CHATE 8 Idex odels cgra-hll/ir Copyrght 0 by The cgra-hll Compaes, Ic. All rghts reserved. 8- Advatages of the Sgle Idex odel educes the umber of puts for dversfcato Easer for securty aalysts to specalze
More informationMonetary fee for renting or loaning money.
Ecoomcs Notes The follow otes are used for the ecoomcs porto of Seor Des. The materal ad examples are extracted from Eeer Ecoomc alyss 6 th Edto by Doald. Newa, Eeer ress. Notato Iterest rate per perod.
More informationChapter 4. More Interest Formulas
Chapter 4 More Iterest ormulas Uform Seres Compoud Iterest ormulas Why? May paymets are based o a uform paymet seres. e.g. automoble loas, house paymets, ad may other loas. 2 The Uform aymet Seres s 0
More informationChapter 4. More Interest Formulas
Chapter 4 More Iterest ormulas Uform Seres Compoud Iterest ormulas Why? May paymets are based o a uform paymet seres. e.g. automoble loas, house paymets, ad may other loas. 2 The Uform aymet Seres s 0
More informationBy choosing to view this document, you agree to all provisions of the copyright laws protecting it.
Copyrght 203 IEEE. Reprted, wth permsso, from Dgzhou Cao, Yu Su ad Huaru Guo, Optmzg Mateace Polces based o Dscrete Evet Smulato ad the OCBA Mechasm, 203 Relablty ad Mataablty Symposum, Jauary, 203. Ths
More informationForecasting the Movement of Share Market Price using Fuzzy Time Series
Iteratoal Joural of Fuzzy Mathematcs ad Systems. Volume 1, Number 1 (2011), pp. 73-79 Research Ida Publcatos http://www.rpublcato.com Forecastg the Movemet of Share Market Prce usg Fuzzy Tme Seres B.P.
More informationSupplemental notes for topic 9: April 4, 6
Sta-30: Probablty Sprg 017 Supplemetal otes for topc 9: Aprl 4, 6 9.1 Polyomal equaltes Theorem (Jese. If φ s a covex fucto the φ(ex Eφ(x. Theorem (Beaymé-Chebyshev. For ay radom varable x, ɛ > 0 P( x
More informationMathematics 1307 Sample Placement Examination
Mathematcs 1307 Sample Placemet Examato 1. The two les descrbed the followg equatos tersect at a pot. What s the value of x+y at ths pot of tersecto? 5x y = 9 x 2y = 4 A) 1/6 B) 1/3 C) 0 D) 1/3 E) 1/6
More information? Economical statistics
Probablty calculato ad statstcs Probablty calculato Mathematcal statstcs Appled statstcs? Ecoomcal statstcs populato statstcs medcal statstcs etc. Example: blood type Dstrbuto A AB B Elemetary evets: A,
More informationThe Complexity of General Equilibrium
Prof. Ja Bhattachara Eco --Sprg 200 Welfare Propertes of Market Outcomes Last tme, we covered equlbrum oe market partal equlbrum. We foud that uder perfect competto, the equlbrum prce ad quatt mamzed the
More information- Inferential: methods using sample results to infer conclusions about a larger pop n.
Chapter 6 Def : Statstcs: are commoly kow as umercal facts. s a feld of dscple or study. I ths class, statstcs s the scece of collectg, aalyzg, ad drawg coclusos from data. The methods help descrbe ad
More informationThe Application of Asset Pricing to Portfolio Management
Clemso Ecoomcs The Applcato of Asset Prcg to Portfolo Maagemet The Nature of the Problem Portfolo maagers have two basc problems. Frst they must determe whch assets to hold a portfolo, ad secod, they must
More informationFINANCIAL MATHEMATICS GRADE 11
FINANCIAL MATHEMATICS GRADE P Prcpal aout. Ths s the orgal aout borrowed or vested. A Accuulated aout. Ths s the total aout of oey pad after a perod of years. It cludes the orgal aout P plus the terest.
More informationLECTURE 5: Quadratic classifiers
LECURE 5: Quadratc classfers Bayes classfers for Normally dstrbuted classes Case : σ I Case : ( daoal) Case : ( o-daoal) Case : σ I Case 5: j eeral case Numercal example Lear ad quadratc classfers: coclusos
More informationAlgorithm Analysis. x is a member of the set P x is not a member of the set P The null or empty set. Cardinality: the number of members
Algorthm Aalyss Mathematcal Prelmares: Sets ad Relatos: A set s a collecto of dstgushable members or elemets. The members are usually draw from some larger collecto called the base type. Each member of
More informationAMS Final Exam Spring 2018
AMS57.1 Fal Exam Sprg 18 Name: ID: Sgature: Istructo: Ths s a close book exam. You are allowed two pages 8x11 formula sheet (-sded. No cellphoe or calculator or computer or smart watch s allowed. Cheatg
More informationABSTRACT 1 INTRODUCTION
Proceedgs of ICAD011 ICAD-011-1 TOLERANCE SYNTHESIS USING AXIOMATIC DESIGN Ga Campatell ga.campatell@uf.t DMTI, Departmet of Mechacal Egeerg ad Idustral Techologes, Uversty of Freze Va d S.Marta, 3 50139
More informationPortfolio Optimization: MAD vs. Markowitz
Rose-Hulma Udergraduate Mathematcs Joural Volume 6 Issue 2 Artcle 3 Portfolo Optmzato: MAD vs. Markowtz Beth Bower College of Wllam ad Mary, bebowe@wm.edu Pamela Wetz Mllersvlle Uversty, pamela037@hotmal.com
More information8.0% E(R) 6.0% Lend. Borrow 4.0% 2.0% rf rf 0.0% 0.0% 1.0% 2.0% 3.0% 4.0% STD(R) E(R) Long A and Short B. Long A and Long B. Short A and Long B
F8000 Valuato of Facal ssets Sprg Semester 00 Dr. Isabel Tkatch ssstat Professor of Face Ivestmet Strateges Ledg vs. orrowg rsk-free asset) Ledg: a postve proporto s vested the rsk-free asset cash outflow
More informationTypes of Sampling Plans. Types of Sampling Plans. Sampling Procedures. Probability Samples -Simple Random sample -Stratified sample -Cluster sample
Samplg Procedures Defe the Populato Idetfy the Samplg Frame Select a Samplg Procedure Determe the Sample Sze Select the Sample Elemets Collect the Data Types of Samplg Plas o-probablty Samples -Coveece
More informationOnline Encoding Algorithm for Infinite Set
Ole Ecodg Algorthm for Ifte Set Natthapo Puthog, Athast Surarers ELITE (Egeerg Laboratory Theoretcal Eumerable System) Departmet of Computer Egeerg Faculty of Egeerg, Chulalogor Uversty, Pathumwa, Bago,
More informationSTATIC GAMES OF INCOMPLETE INFORMATION
ECON 10/410 Decsos, Markets ad Icetves Lecture otes.11.05 Nls-Herk vo der Fehr SAIC GAMES OF INCOMPLEE INFORMAION Itroducto Complete formato: payoff fuctos are commo kowledge Icomplete formato: at least
More informationInferential: methods using sample results to infer conclusions about a larger population.
Chapter 1 Def : Statstcs: 1) are commoly kow as umercal facts ) s a feld of dscple or study Here, statstcs s about varato. 3 ma aspects of statstcs: 1) Desg ( Thk ): Plag how to obta data to aswer questos.
More informationDynamic Economic Load Dispatch of Electric Power System Using Direct Method
Iteratoal Joural of Appled Egeerg Research ISSN 0973-456 Volume 13, Number 6 (018) pp. 34-347 Research Ida ublcatos. http://www.rpublcato.com Dyamc Ecoomc oad Dspatch of Electrc ower System Usg Drect Method
More informationMath 373 Fall 2013 Homework Chapter 4
Math 373 Fall 2013 Hoework Chapter 4 Chapter 4 Secto 5 1. (S09Q3)A 30 year auty edate pays 50 each quarter of the frst year. It pays 100 each quarter of the secod year. The payets cotue to crease aually
More informationSimulation Study on the Influential Effect of Venture Capital Decision-making Behavior s Influencing Factors Wan-li MA and Hao WU
2018 Iteratoal Coferece o Modelg, Smulato ad Optmzato (MSO 2018) ISBN: 978-1-60595-542-1 Smulato Study o the Ifluetal Effect of Veture Captal Decso-mag Behavor s Ifluecg Factors Wa-l MA ad Hao WU College
More information6. Loss systems. ELEC-C7210 Modeling and analysis of communication networks 1
ELEC-C72 Modelg ad aalyss of commucato etwors Cotets Refresher: Smple teletraffc model Posso model customers, servers Applcato to flow level modellg of streamg data traffc Erlag model customers, ; servers
More informationLecture 9 February 21
Math 239: Dscrete Mathematcs for the Lfe Sceces Sprg 2008 Lecture 9 February 21 Lecturer: Lor Pachter Scrbe/ Edtor: Sudeep Juvekar/ Alle Che 9.1 What s a Algmet? I ths lecture, we wll defe dfferet types
More informationAn Entropy Method for Diversified Fuzzy Portfolio Selection
60 Iteratoal Joural of Fuzzy Systems, Vol. 4, No., March 0 A Etropy Method for Dversfed Fuzzy Portfolo Selecto Xaoxa Huag Abstract Ths paper proposes a etropy method for dversfed fuzzy portfolo selecto.
More informationMay 2005 Exam Solutions
May 005 Exam Soluto 1 E Chapter 6, Level Autes The preset value of a auty-mmedate s: a s (1 ) v s By specto, the expresso above s ot equal to the expresso Choce E. Soluto C Chapter 1, Skg Fud The terest
More informationCOMPARISON OF APPROACHES TO TESTING EQUALITY OF EXPECTATIONS AMONG SAMPLES FROM POISSON AND NEGATIVE BINOMIAL DISTRIBUTION
ACTA UNIVERSITATIS AGRICULTURAE ET SILVICULTURAE MENDELIANAE BRUNENSIS Volume 66 0 Number 4, 08 https://do.org/0.8/actau08660405 COMPARISON OF APPROACHES TO TESTING EQUALITY OF EXPECTATIONS AMONG SAMPLES
More informationVariable weight combined forecast of China s energy demand based on grey model and BP neural network
Avalable ole www.jocpr.com Joural of Chemcal ad Pharmaceutcal Research, 2014, 6(4):303-308 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 Varable weght combed forecast of Cha s eergy demad based
More informationFINANCIAL MATHEMATICS : GRADE 12
FINANCIAL MATHEMATICS : GRADE 12 Topcs: 1 Smple Iterest/decay 2 Compoud Iterest/decay 3 Covertg betwee omal ad effectve 4 Autes 4.1 Future Value 4.2 Preset Value 5 Skg Fuds 6 Loa Repaymets: 6.1 Repaymets
More information0.07 (12) i 1 1 (12) 12n. *Note that N is always the number of payments, not necessarily the number of years. Also, for
Chapter 3, Secto 2 1. (S13HW) Calculate the preset value for a auty that pays 500 at the ed of each year for 20 years. You are gve that the aual terest rate s 7%. 20 1 v 1 1.07 PV Qa Q 500 5297.01 0.07
More informationCS 1675 Intro to Machine Learning Lecture 9. Linear regression. Supervised learning. a set of n examples
CS 675 Itro to Mache Learg Lecture 9 Lear regresso Mlos Hauskrecht mlos@cs.ptt.eu 59 Seott Square Supervse learg Data: D { D D.. D} a set of eamples D s a put vector of sze s the esre output gve b a teacher
More informationThe Constrained Mean-Semivariance Portfolio Optimization Problem with the Support of a Novel Multiobjective Evolutionary Algorithm
Joural of Software Egeerg ad Applcatos, 013, 6, -9 do:10.436/jsea.013.67b005 Publshed Ole July 013 (http://www.scrp.org/joural/jsea) The Costraed Mea-Semvarace Portfolo Optmzato Problem wth the Support
More informationON MAXIMAL IDEAL OF SKEW POLYNOMIAL RINGS OVER A DEDEKIND DOMAIN
Far East Joural of Mathematcal Sceces (FJMS) Volume, Number, 013, Pages Avalable ole at http://pphmj.com/jourals/fjms.htm Publshed by Pushpa Publshg House, Allahabad, INDIA ON MAXIMAL IDEAL OF SKEW POLYNOMIAL
More informationPortfolio Optimization. Application of the Markowitz Model Using Lagrange and Profitability Forecast
Epert Joural of Ecoomcs. Volume 6, Issue, pp. 6-34, 8 8 The Author. Publshed by Sprt Ivestfy. ISSN 359-774 Ecoomcs.EpertJourals.com Portfolo Optmzato. Applcato of the Markowtz Model Usg Lagrage ad Proftablty
More informationA cooperative game theory approach for the equal profit and risk allocation
Recet Researces Crcuts, Systems, Commucatos ad Computers A cooperatve game teory approac for te equal proft ad rsk allocato Ataasos C. Karmpers, Aastasos Sotrcos, Kostatos Aravosss, ad Ilas P. Tatsopoulos
More informationLinear regression II
CS 75 Mache Learg Lecture 9 Lear regresso II Mlos Hauskrecht mlos@cs.ptt.eu 539 Seott Square Lear regresso Fucto f : X Y Y s a lear combato of put compoets f ( w w w w w w, w, w k - parameters (weghts
More informationMaking Even Swaps Even Easier
Mauscrpt (Jue 18, 2004) Makg Eve Swaps Eve Easer Jyr Mustaok * ad Ramo P. Hämäläe Helsk Uversty of Techology Systems Aalyss Laboratory P.O. Box 1100, FIN-02015 HUT, Flad E-mals: yr.mustaok@hut.f, ramo@hut.f
More informationCS 2750 Machine Learning. Lecture 7. Linear regression. CS 2750 Machine Learning. Linear regression. is a linear combination of input components x
CS 75 Mache Learg Lecture 7 Lear regresso Mlos Hauskrecht mlos@cs.ptt.eu 539 Seott Square CS 75 Mache Learg Lear regresso Fucto f : X Y s a lear combato of put compoets f k - parameters eghts Bas term
More informationUpon an Integer Allocation Problem
44 Ecoomy Iformatcs, o 1/2001 Uo a Iteger llocato Problem Claudu VINTE Goldma Sachs Ltd Tokyo, Jaa bstract: class of heurstc algorthms for tradg executo allocatos o vestors accouts Keywords: allocato,
More informationDetermination of Optimal Portfolio by Using Tangency Portfolio and Sharpe Ratio
Research Joural of Face ad Accoutg ISSN 2222-697 (Paper) ISSN 2222-2847 (Ole) Vol.7, No.5, 206 Determato of Optmal Portfolo by Usg Tagecy Portfolo ad Sharpe Rato Dr. Haka Blr Bahçeşehr Uversty, Turkey
More informationMulti-Resource Allocation: Fairness-Efficiency Tradeoffs in a Unifying Framework
Mult-Resource Allocato: Faress-Effcecy Tradeoffs a Ufyg Framework Carlee Joe-Wog, Soumya Se, Ta La, Mug Chag Departmet of Electrcal Egeerg, Prceto Uversty, Prceto, NJ 08544 Departmet of Electrcal ad Computer
More informationCOSC 6385 Computer Architecture. Performance Measurement
COSC 6385 Computer Archtecture Performace Measuremet Edgar Gabrel Sprg 204 Measurg performace (I) Respose tme: how log does t take to execute a certa applcato/a certa amout of work Gve two platforms X
More informationEffects of Distributed Generation penetration on system power losses and voltage profiles
Iteratoal Joural of cetfc ad Research ublcatos, olume 3, Issue, December 03 I 50-353 Effects of Dstrbuted Geerato peetrato o system power losses ad voltage profles Julus Kloz Charles*, codemus Abugu Odero**
More informationTwo Approaches for Log-Compression Parameter Estimation: Comparative Study*
SERBAN JOURNAL OF ELECTRCAL ENGNEERNG Vol. 6, No. 3, December 009, 419-45 UDK: 61.391:61.386 Two Approaches for Log-Compresso Parameter Estmato: Comparatve Study* Mlorad Paskaš 1 Abstract: Stadard ultrasoud
More informationTOPIC 7 ANALYSING WEIGHTED DATA
TOPIC 7 ANALYSING WEIGHTED DATA You do t have to eat the whole ox to kow that the meat s tough. Samuel Johso Itroducto dfferet aalyss for sample data Up utl ow, all of the aalyss techques have oly dealt
More informationAPPENDIX M: NOTES ON MOMENTS
APPENDIX M: NOTES ON MOMENTS Every stats textbook covers the propertes of the mea ad varace great detal, but the hgher momets are ofte eglected. Ths s ufortuate, because they are ofte of mportat real-world
More informationMathematical Background and Algorithms
(Scherhet ud Zuverlässgket egebetteter Systeme) Fault Tree Aalyss Mathematcal Backgroud ad Algorthms Prof. Dr. Lggesmeyer, 0 Deftos of Terms Falure s ay behavor of a compoet or system that devates from
More informationPoverty indices. P(k;z; α ) = P(k;z; α ) /(z) α. If you wish to compute the FGT index of poverty, follow these steps:
Poverty dces DAD offers four possbltes for fxg the poverty le: - A determstc poverty le set by the user. 2- A poverty le equal to a proporto l of the mea. 3- A poverty le equal to a proporto m of a quatle
More informationMODULE 1 LECTURE NOTES 3
Water Resources Systems Plag ad Maagemet: Itroducto ad Basc Cocepts: Optmzato ad Smulato MODULE LECTURE NOTES 3 OPTIMIZATION AND SIMULATION INTRODUCTION I the prevous lecture we studed the bascs of a optmzato
More informationSolutions to Problems
Solutos to Problems ( Pt Pt + Ct) P5-. LG : Rate of retur: rt Pt Basc ($,000 $0,000 + $,500) a. Ivestmet X: Retur.50% $0,000 Ivestmet Y: Retur ($55,000 $55,000 + $6,800).36% $55,000 b. Ivestmet X should
More information= 1. UCLA STAT 13 Introduction to Statistical Methods for the Life and Health Sciences. Parameters and Statistics. Measures of Centrality
UCLA STAT Itroducto to Statstcal Methods for the Lfe ad Health Sceces Istructor: Ivo Dov, Asst. Prof. of Statstcs ad Neurolog Teachg Assstats: Brad Shaata & Tffa Head Uverst of Calfora, Los Ageles, Fall
More informationDiscrete time-cost tradeoff model for optimizing multi-mode construction project resource allocation
Dscrete tme-cost tradeoff model for optmzg mult-mode costructo project resource allocato The project schedulg ad resource allocato problems have bee studed usg dfferet optmzato methods. The resource levelg
More informationKeywords: financial risk management, tractable risk measures, portfolio selection, efficient frontiers, linear programming problem.
THE EMPIRICAL VALUE-AT-RISK/EXPECTED RETURN FRONTIER: A USEFUL TOOL OF MARKET RISK MANAGING Aalsa D Clemete Abstract I addto to measurg ad motorg facal rs, t s mportat for rs maagers to uderstad how facal
More informationAn Efficient Estimator Improving the Searls Normal Mean Estimator for Known Coefficient of Variation
ISSN: 2454-2377, A Effcet Estmator Improvg the Searls Normal Mea Estmator for Kow Coeffcet of Varato Ashok Saha Departmet of Mathematcs & Statstcs, Faculty of Scece & Techology, St. Auguste Campus The
More informationMinimization of Value at Risk of Financial Assets Portfolio using Genetic Algorithms and Neural Networks
Joural of Appled Face & Bakg, vol. 6, o. 2, 2016, 39-52 ISSN: 1792-6580 (prt verso), 1792-6599 (ole) Scepress Ltd, 2016 Mmzato of Value at Rsk of Facal Assets Portfolo usg Geetc Algorthms ad Neural Networks
More informationA Theory of Rate-Based Execution *
A Theor of Rate-Based Executo * Kev Jeffa Departmet of Computer Scece Uverst of North Carola at Chapel Hll Chapel Hll, NC 27599-375 jeffa@cs.uc.edu Steve Goddard Computer Scece & Egeerg Uverst of Nebraska
More informationRisk-based Loan Pricing: Portfolio Optimization Approach With Marginal Risk Contribution
Rsk-based Loa rcg: ortfolo Optmzato Approach Wth Margal Rsk Cotrbuto So Yeo Chu McDoough School of Busess, Georgetow Uversty, Washgto D.C. 20057, soyeo.chu@georgetow.edu Mguel A. Lejeue Departmet of Decso
More informationA polyphase sequences with low autocorrelations
oural o Physcs: Coerece Seres PAPER OPE ACCESS A polyphase sequeces wth low autocorrelatos To cte ths artcle: A Leuh 07. Phys.: Co. Ser. 859 00 Vew the artcle ole or updates ad ehacemets. Related cotet
More information0.07. i PV Qa Q Q i n. Chapter 3, Section 2
Chapter 3, Secto 2 1. (S13HW) Calculate the preset value for a auty that pays 500 at the ed of each year for 20 years. You are gve that the aual terest rate s 7%. 20 1 v 1 1.07 PV Qa Q 500 5297.01 0.07
More informationDEGRESSIVE PROPORTIONALITY IN THE EUROPEAN PARLIAMENT
M A T H E M A T I C A L E C O N O M I C S No. 7(4) 20 DEGRESSIVE PROPORTIONALITY IN THE EUROPEAN PARLIAMENT Katarzya Cegełka Abstract. The dvso of madates to the Europea Parlamet has posed dffcultes sce
More information901 Notes: 16.doc Department of Economics Clemson University PRODUCTION THEORY 1
90 Notes: 6.doc Departmet of Ecoomcs Clemso Uversty PRODUCTION THEORY The eoclasscal theory of the frm s ot a theory about dustral orgazato but rather a theory about the relato betwee put ad output prces.
More informationPortfolio Optimization of Pension Fund Contribution in Nigeria
Mathematcal Theory ad Modelg ISSN 2224-5804 (Paper) ISSN 2225-0522 (Ole) www.ste.org Portfolo Optmzato of Peso Fud Cotrbuto Ngera 2 3 Egbe, G. A. Awogbem, C. A. Osu, B.O. Jot Degree Programme, Natoal Mathematcal
More informationCategories in Digital Images
Amerca Joural of Mathematcs ad Statstcs 3, 3(): 6-66 DOI:.593/j.ajms.33.9 Cateores Dtal Imaes Sme Öztuç *, Al Mutlu Celal Bayar Uversty, Faculty of Scece ad Arts, Departmet of Mathematcs, Muradye Campus,
More informationAccounting 303 Exam 2, Chapters 5, 6, 7 Fall 2015
Accoutg 303 Exam 2, Chapters 5, 6, 7 Fall 2015 Name Row I. Multple Choce Questos. (2 pots each, 30 pots total) Read each questo carefully ad dcate your aswer by crclg the letter precedg the oe best aswer.
More informationA Hierarchical Multistage Interconnection Network
A Herarchcal Multstage Itercoecto Networ Mohtar Aboelaze Dept. of Computer Scece Yor Uversty Toroto, ON. CANADA M3J P3 aboelaze@cs.yoru.ca Kashf Al Dept. of Computer Scece Yor Uversty Toroto, ON. CANADA
More informationAn Empirical Based Path Loss model with Tree Density Effects for 1.8 GHz Mobile Communications Using Fuzzy Regression
Proceedgs of the 5th WSEAS It Cof o Electrocs, Hardware, Wreless ad Optcal Commucatos, Madrd, Spa, February 15-17, 26 (pp5-57) A Emprcal Based Path Loss model wth Tree Desty Effects for 18 GHz Moble Commucatos
More informationNaïve diversification, risk parity type I, minimum variance and risk parity type II (ERC) 1
Naïve dversfcato, rs arty tye I, mmum varace ad rs arty tye II (ERC) Harald Boger harald.boger@lve.de Frst Verso: Setember 9 th 05 Ths Verso: October 8 th 05 Cosder a mea-varace-otmzg vestor ad assume
More informationPrediction Of Compressive Strength Of Concrete With Different Aggregate Binder Ratio Using ANN Model
Predcto Of Compressve Stregth Of Cocrete Wth Dfferet Aggregate Bder Rato Usg ANN Model Rama Moha Rao.P *, H.Sudarsaa Rao # * Assstat Professor (SG), Cetre for Dsaster Mtgato ad Maagemet, VIT Uversty, Vellore,
More informationSTOCK PRICE PREDICTION BY USING A COMBINATION OF NEURAL NETWORKS AND GENETIC ALGORITHMS
Arth Prabadh: A Joural of Ecoomcs ad Maagemet STOCK PRICE PREDICTION BY USING A COMBINATION OF NEURAL NETWORKS AND GENETIC ALGORITHMS MAHMOUD MOUSAVISHIRI*; FATEMEH SAEIDI** *Departmet of Maagemet, Ecoomcs
More informationDeriving & Understanding the Variance Formulas
Dervg & Uderstadg the Varace Formulas Ma H. Farrell BUS 400 August 28, 205 The purpose of ths hadout s to derve the varace formulas that we dscussed class ad show why take the form they do. I class we
More informationDiscrete Time NHPP Models for Software Reliability Growth Phenomenon
124 The Iteratoal Ara Joural o Iormato Techology, Vol. 6, No. 2, Aprl 29 Dscrete Tme NHPP Models or Sotware Relalty Growth Pheomeo Omar Shataw Prce Husse Adullah Iormato Techology College, al-bayt Uversty,
More informationTrade as transfers, GATT and the core
Ecoomcs Letters 66 () 163 169 www.elsever.com/ locate/ ecobase Trade as trasfers, GATT ad the core Carste Kowalczyk *, Tomas Sostrom a, b a The Fletcher School of Law ad Dplomacy, Tufts Uversty, Medford,
More informationMEASURING THE FOREIGN EXCHANGE RISK LOSS OF THE BANK
Gabrel Bstrceau, It.J.Eco. es., 04, v53, 7 ISSN: 9658 MEASUING THE FOEIGN EXCHANGE ISK LOSS OF THE BANK Gabrel Bstrceau Ecoomst, Ph.D. Face Natoal Bak of omaa Bucharest, Moetary Polcy Departmet, 5 Lpsca
More informationDensity estimation II.
Lecture 5 esty estmato II. Mlos Hausrecht mlos@cs.tt.eu 539 Seott Square Outle Outle: esty estmato: Bomal strbuto Multomal strbuto ormal strbuto oetal famly ata: esty estmato {.. } a vector of attrbute
More informationA nonlinear multiobjective approach for the supplier selection, integrating transportation policies
Author mauscrpt, publshed "N/P" A olear multobjectve approach for the suppler selecto, tegratg trasportato polces Abstract Acha Aguezzoul 1, Perre Ladet 2 1 UFR ESM-IAE 3, place Edouard BRANLY, Techopôle
More informationThe Prediction Error of Bornhuetter-Ferguson
The Predcto Error of Borhuetter-Ferguso Thomas Mac Abstract: Together wth the Cha Ladder (CL method, the Borhuetter-Ferguso ( method s oe of the most popular clams reservg methods. Whereas a formula for
More informationVariance Covariance (Delta Normal) Approach of VaR Models: An Example From Istanbul Stock Exchange
ISSN 2222-697 (Paper) ISSN 2222-2847 (Ole) Vol.7, No.3, 206 Varace Covarace (Delta Normal) Approach of VaR Models: A Example From Istabul Stock Exchage Dr. Ihsa Kulal Iformato ad Commucato Techologes Authorty,
More informationUncertain Supply Chain Management
Ucerta Supply Cha Maagemet (0) 07 Cotets lsts avalable at GrowgScece Ucerta Supply Cha Maagemet homepage: www.growgscece.com/uscm A methodology for outsourcg resources reverse logstcs usg fuzzy TOPSIS
More information4. Monopolistic Competition and Price Rigidities Slides based on G. Illing, University of Munich
4. ooolst ometto ad re Rgdtes Sldes based o G. Illg, Uversty of uh 4. ooolst ometto ad Aggregate Demad Exteraltes Idea: mooolst ometto gves rse to effet alloato ad to (short-ru) real effets of moetary
More informationManagement Science Letters
Maagemet Scece Letters (0) 355 36 Cotets lsts avalable at GrowgScece Maagemet Scece Letters homepage: www.growgscece.com/msl A tellget techcal aalyss usg eural etwork Reza Rae a Shapour Mohammad a ad Mohammad
More information