A General Column Generation Algorithm Applied to System Reliability Optimization Problems
|
|
- Oswald Wilkinson
- 6 years ago
- Views:
Transcription
1 A Genera Coumn Generaton Agorthm Apped to System Reabty Optmzaton Probems Lea Za, Davd W. Cot, Department of Industra and Systems Engneerng, Rutgers Unversty, Pscataway, J 08854, USA Abstract A genera coumn generaton approach for system reabty optmzaton s descrbed and demonstrated. In prevous years, a tremendous amount of study has been concentrated on system reabty optmzaton and coumn generaton as a technque to optmze arge scae probems. Ths paper can be consdered as a contnuaton and ntegraton of these two topcs. It presents new deas for formuatng the aocaton probems n reabty systems and opens a new area for appcaton of coumn generaton. We present the reformuaton of probems ncudng the redundancy aocaton probem, reabty aocaton probem and redundancy-reabty aocaton probem. Then, we descrbe how the coumn generaton agorthm can be used to sove these probems. Ths approach obtans the best found soutons for eampe probems whe requrng a sma fracton of computatona efforts requred by other methods. Key Words Coumn generaton agorthm, reabty optmzaton, redundancy aocaton probem, redundancyreabty aocaton probem. Introducton Coumn generaton (CG can be consdered as one of the most successfu approaches for sovng arge-scae nteger programmng probems over the ast decade. The dea was ntroduced by Gmore & Gomory [ 0] when fndng the optma souton for the cuttng-stoc probem. The probem was to cut umber whch were n dfferent engths to meet demands for specfc engths of sheves. They notced that, t s practcay mpossbe to generate a the possbe confguratons of umber cuts. Therefore, they devsed a methodoogy whch started wth a set of feasbe confguratons, and used the optma souton to the smpfed probem to generate a new promsng confguraton. Later, the dea has been used by Mnou [ ] as a powerfu technque to reformuate some mportant combnatora probems. Vance et. a. (994 combned CG and branch-and-bound to present an agorthm for bnary cuttng stoc probems. From the appcaton pont-ofvew, Desrosers et. a. [ 3] apped CG to routng/dstrbuton probems; Ryan [ 4] used CG n arcrew rosterng and Vanderbec [ 5] suggested eact effcent agorthm based on CG for the cuttng stoc probem. There are three varatons of CG accordng to Whem [ 6]. The common dea s to defne a Master Probem (MP whch s the subset of the orgna probem. The MP contans ony a seected number of coumns. In Type I CG, the MP nteracts ony once wth another probem, caed the Auary Probem (AP whch sends the attractve coumns to the MP. Optmzaton of the master probem s based on the coumns sent from AP. In Type II CG, the nteracton of the MP s wth another probem, caed the Sub-Probem (SP or prce-out probem. Type III CG s based on Dantzg-Wofe decomposton (Dantzg et. a. [ 7] n whch the MP teratvey nteracts wth one or more SPs to dentfy promsng coumns. The CG whch s consdered n ths paper s of Type II. The probem has one MP and one (or more SP(s to prce-out the MP usng the smpe optmaty condton. Ths means that the probem starts wth a MP whch s a subset of the orgna probem wth ony a subset of the coumns. The optmzaton of the MP s based on avaabe methods for optmzaton. Then, SP(s are constructed wth the dea of smpe optmaty whch s to consder a non-basc varabes (coumns. The nteracton between the MP and the SP(s can be demonstrated as n Fgure. Ths fgure shows the decomposton of the probem nto two probems: Master Probem and Sub-Probem(s. As t s shown, the dua varabes from the MP are sent to the SP(s, and n return, each SP, wth postve objectve functon, sends ts coumn to the MP.
2 Master Probem Dua Varabes Coumn(s Sub-Probem(s otaton and Abbrevatons otaton r M ( R r, r2,, r Fgure : Decomposton of the Probem nto MP and SP reabty of component number of components/subsystems n the system number of dfferent resource restrctons reabty of the system as a functon of component reabtes ( R, 2,, (,,, g r r2 r b r, r u reabty of the system as a functon of number of each redundant component type consumpton of resource as a functon of component reabtes amount of resource ower bound and upper bound on reabty of component j, u j ower and upper bound for number of components type j n subsystem t number of component choces for subsystem n number of master probem coumns consdered for subsystem (, 2,, t number of component type j used n subsystem j th component seecton vector for subsystem number of component type j used n subsystem for the j vector R ( reabty of subsystem λ ω α th, f component seecton vector s chosen for subsystem 0, otherwse vector of dua varabes reated to cost and weght constrants dua varabe reated to the th convety constrant th component seecton Abbrevatons RAP MP SP CG Redundancy Aocaton Probem Master Probem Sub-Probem Coumn Generaton
3 TS GA ACO Tabu Search Genetc Agorthm Ant Coony Optmzaton 2. Reabty Optmzaton Modes and Coumn Generaton The goa n any reabty optmzaton mode s to mamze the reabty of a gven system, often by consderng enhancement of component reabty, use of redundant components, or both. Three dfferent reabty optmzaton probems can be defned, dependng on the opton n hand to ncrease the system reabty. The frst probem s caed reabty optmzaton probem. In ths case, the ony varabe s the reabty of each component whch s a contnuous varabe. Ths probem can be represented as: ( ma R r, r2,, r { r, r2,, r u g r, r,, r b : p,2,, m ; r r r : j,2,, ( { { p 2 p j j j The second s caed redundancy aocaton probem (RAP. Here, to ncrease the reabty of the system one can add redundant components to the nta components. The varabes are the number of components to be assgned to each subsystem. The genera form of ths probem s: ( ma R, 2,, {,,, 2 + (,,, : {,2,, ; : {,2,,, {,2,, g p 2 bp p m j j t The thrd s the reabty-redundancy probem. Here, the decson has to be made on how many components are necessary from each component choce and wth what reabty. The genera form s as foows: ( ma R r, r2,, r;, 2,, { r,, r ;,, g r, r,, r ;,,, b : p,2,, m ; u :,2,,, j,2,, t ( 2 2 { { { u + : {, 2,, ; : {, 2,,, {, 2,, p p j j j rj rj rj j j j t Each of the three types of probems descrbed above has a nonnear objectve functon. The constrant(s can be near or nonnear, dependng on the probem nstance. The reabty optmzaton probem s a nonnear programmng probem wth contnuous varabes. There are aready dfferent souton technques for nonnear optmzaton whch offer very good resuts for the precson desred. The ast two probems are more compe. ot ony are the probems nonnear, but aso, the compety of the probems hghy ncreased wth nteger varabes. That s the reason we consder the ast two probems for the appcaton of CG. To appy CG to each mode, we defne a MP and one (or mutpe SP(s, dependng on the probem. 2.. Redundancy Aocaton Probem The frst nstance to be consdered s RAP. We show the appcaton of CG to a seres-parae system wth subsystems n seres because, the objectve functon can not be demonstrated n genera for a networ wth unspecfed structure. The mode of such a probem can be demonstrated as:
4 t ma ( ( r,..., j = j= j + (,,, : {,2,, ; Z : {,2,,, {,2,, g b p m j t p 2 p j In the above mode, t s the number of dfferent component types that can be assgned to subsystem. Tang the natura ogarthm of the objectve functon, convertng the mamzaton probem to a mnmzaton one, and assumng separabty for g p,.e. g (,,, g ( MP to: Master Probem n t mn λn ( rj = = j = n λ g b : p,2,, m ; λ = : {,2,, ; p p = = = { λ 0, : {, 2,,, {, 2,, n j n ( { p 2 p = =, transforms the n whch, λ =, f the th component seecton vector s chosen for subsystem, 0 otherwse and n s the number of the MP coumns consdered for subsystem. ow, by ntazng the startng coumns, the probem s a near reaed probem n whch λ s are the varabes. Consderng the smpe optmaty condton, the SPs can be defned for each subsystem as: Subprobem m t ma j ωpgp( + α + n( ( rj p= j= + Z :,2,,, j,2,, t j { { To sove the SP(s, we use the dea presented by Barnhart et. a. [ 8] whch s, a good souton of the SP s suffcent to be sent to the MP. Therefore, we appy a heurstc to fnd a good souton for ths nonnear nteger programmng probem. Then, for each subprobem wth nonnegatve objectve functon, the vector s sent to the MP and the MP s reoptmzed. The process contnues unt there s no subprobem wth nonnegatve objectve functon. As the fna step, the MP s optmzed usng one of the avaabe technques for nteger programmng, for nstance branch-and-bound. The vaue for system reabty and component choces can be found from ths method Reabty-Redundancy Probem The same dea can be apped to reabty-redundancy probem. The resuts can be obtaned usng an anaogous procedure. The ony dfference s that n ths case, we have n subprobems, one for each component (nstead of each subsystem and each SP has ony two varabes: number of redundant components and the reabty of each component type.
5 As an eampe, consder a seres system wth components. For each component, there are choces to change the reabty and the number of redundant component. Assumng separabty for each resource consumpton constrant, (.e. g p(, 2,, ; r, r2,, r = gp(, r, the MP s: Master Probem n m ma λn ( r = = j = n λ λ { p p = = = = g, r b : p,2,, m ; λ = : {,2,, ; 0, : {, 2,,, {, 2,, n n ( { The subprobem for each component s: Subprobem m t mn ωpgp( + α + n( ( r p= j= + u Z :,2,, ; r r r : j,2,, { { j j j otce that each SP n ths case contans ony two varabes, whch shows the th component seecton for component, and r whch s the reabty of component. 3. Eampe As an eampe for our approach, we appy CG to the famous probem n RAP descrbed by Fyffe et. a (968. For ths probem, there are 4 subsystems for whch we can choose from three or four component choces, wth specfed reabty, cost and weght. There are weght and cost constrants. The component cost, weght and reabty data were orgnay presented by Fyffe et. a. [ 9]. The nstance consdered n ths eampe s one of the 33 varatons of the probem, as devsed by aagawa and Myaza [ 9]. The probem s demonstrated as: Master Probem 4 n m ma λn ( rj = = j = 4 n t 4 n t n j λ λ λ = λ { c C; w W;, S; 0,, S, {,2,, n j j j j = = j= = = j= = Subprobem(s t t t ma ω c 2 ( ( j j j + ω wj j + α + n rj j= j= j= + Z :,2,,, j,2,, t j { {
6 For the case of W=90 and C=30, the resuts can be summarzed n the foowng tabe. In ths tabe, the resut of CG s aso compared wth the resuts of prevous approaches for ths probem. As t s shown n the tabe, CG and TS gve best soutons for ths nstance. It s mportant to notce that CG resut s as good as TS, athough t ony consders around seventy coumns whe TS searches appromatey 20,000 soutons. Tabe : Comparson of Coumn Generaton resut wth Best Avaabe Ones GA TS ACO [ ] [ 2] [ 3] CG Reabty Reabty Reabty Reabty References. Gmore, P. C., and Gomory, R. E., 96, a near programmng approach to the cuttng stoc probem, Operatons Research, vo. 9, pp Mnou, M. 987, A cass of combnatora optmzaton probems wth poynomay sovabe arge scae set-coverng/ parttonng reaatons, RAIRO 2, Desrosers, J., Dumas, Y., Soomon, M. M., and Soums F., 990, Tme constraned routng and schedung. In: M.E. Ba, T.L. Magnant, C. Monna and G.L. emhauser, Edtors, Handboos n Operatons Research and Management Scence: etwors, orth-hoand, Amsterdam, Ch.. 4. Ryan, D. M. 992, The souton of massve generazed set parttonng probems n arcrew rosterng, vo. 43, no. 5, pp Vanderbec, F., and Wosey, L. A., 996, An eact agorthm for IP coumn generaton, Operatons Research Letters, vo. 9, pp Whem, W. E., 200, A technca revew of coumn generaton n nteger programmng, Optmzaton and Engneerng, vo. 2, pp Dantzg, G. B., and Wofe, P., 960, Decomposton prncpe for near programs, Operatons Research, vo. 8, pp. 0, Barnhart, C., Johnson, E. L., emhauser, G. L., Savesbergh, M.W. P., and Vance, P. H., 998, Branch and prce: Coumn generaton for sovng huge nteger programs, Operatons Research, vo. 46, no. 3, pp Fyffe, D. E., Hnes, W. W., and Lee,. K., 968, System reabty aocaton and a computatona agorthm, IEEE Transactons on Reabty, vo. R-7, no. 2, pp aagawa, Y., and Myaza, S.,98, Surrogate constrants agorthm for reabty, optmzaton probems wth two constrants, IEEE Transactons on Reabty, vo. R-30, no. 2, pp Cot, D. W., and Smth, A. E., 996, Reabty optmzaton of seres-parae systems usng a genetc agorthm, IEEE Transactons on Reabty, vo. 45, no. 2, pp Kuture-Kona, S., Smth, A. E., and Cot, D. W., 2003, Effcenty Sovng the Redundancy Aocaton Probem Usng Tabu Search, IIE Transactons, vo. 35, no. 6, pp Lang, Y. C. and Smth, A. E., 2004, An Ant Coony Optmzaton Agorthm for the Redundancy Aocaton Probem (RAP, IEEE Transactons on Reabty, vo. 53, o. 3, pp
Lower Bounding Procedures for the Single Allocation Hub Location Problem
Lower Boundng Procedures for the Snge Aocaton Hub Locaton Probem Borzou Rostam 1,2 Chrstoph Buchhem 1,4 Fautät für Mathemat, TU Dortmund, Germany J. Faban Meer 1,3 Uwe Causen 1 Insttute of Transport Logstcs,
More informationMARKOV CHAIN AND HIDDEN MARKOV MODEL
MARKOV CHAIN AND HIDDEN MARKOV MODEL JIAN ZHANG JIANZHAN@STAT.PURDUE.EDU Markov chan and hdden Markov mode are probaby the smpest modes whch can be used to mode sequenta data,.e. data sampes whch are not
More informationResearch on Complex Networks Control Based on Fuzzy Integral Sliding Theory
Advanced Scence and Technoogy Letters Vo.83 (ISA 205), pp.60-65 http://dx.do.org/0.4257/ast.205.83.2 Research on Compex etworks Contro Based on Fuzzy Integra Sdng Theory Dongsheng Yang, Bngqng L, 2, He
More informationDeriving the Dual. Prof. Bennett Math of Data Science 1/13/06
Dervng the Dua Prof. Bennett Math of Data Scence /3/06 Outne Ntty Grtty for SVM Revew Rdge Regresson LS-SVM=KRR Dua Dervaton Bas Issue Summary Ntty Grtty Need Dua of w, b, z w 2 2 mn st. ( x w ) = C z
More informationApplication of Particle Swarm Optimization to Economic Dispatch Problem: Advantages and Disadvantages
Appcaton of Partce Swarm Optmzaton to Economc Dspatch Probem: Advantages and Dsadvantages Kwang Y. Lee, Feow, IEEE, and Jong-Bae Par, Member, IEEE Abstract--Ths paper summarzes the state-of-art partce
More informationA MIN-MAX REGRET ROBUST OPTIMIZATION APPROACH FOR LARGE SCALE FULL FACTORIAL SCENARIO DESIGN OF DATA UNCERTAINTY
A MIN-MAX REGRET ROBST OPTIMIZATION APPROACH FOR ARGE SCAE F FACTORIA SCENARIO DESIGN OF DATA NCERTAINTY Travat Assavapokee Department of Industra Engneerng, nversty of Houston, Houston, Texas 7704-4008,
More informationNeural network-based athletics performance prediction optimization model applied research
Avaabe onne www.jocpr.com Journa of Chemca and Pharmaceutca Research, 04, 6(6):8-5 Research Artce ISSN : 0975-784 CODEN(USA) : JCPRC5 Neura networ-based athetcs performance predcton optmzaton mode apped
More informationApproximate Circle Packing in a Rectangular Container: Integer Programming Formulations and Valid Inequalities
Appromate Crce Pacng n a Rectanguar Contaner: Integer Programmng Formuatons and Vad Inequates Igor Ltvnchev, Lus Infante, and Edth Lucero Ozuna Espnosa Department of Mechanca and Eectrca Engneerng Nuevo
More informationBoundary Value Problems. Lecture Objectives. Ch. 27
Boundar Vaue Probes Ch. 7 Lecture Obectves o understand the dfference between an nta vaue and boundar vaue ODE o be abe to understand when and how to app the shootng ethod and FD ethod. o understand what
More informationStructure and Drive Paul A. Jensen Copyright July 20, 2003
Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.
More informationIntegrating advanced demand models within the framework of mixed integer linear problems: A Lagrangian relaxation method for the uncapacitated
Integratng advanced demand modes wthn the framework of mxed nteger near probems: A Lagrangan reaxaton method for the uncapactated case Mertxe Pacheco Paneque Shad Sharf Azadeh Mche Berare Bernard Gendron
More informationLOW BIAS INTEGRATED PATH ESTIMATORS. James M. Calvin
Proceedngs of the 007 Wnter Smulaton Conference S G Henderson, B Bller, M-H Hseh, J Shortle, J D Tew, and R R Barton, eds LOW BIAS INTEGRATED PATH ESTIMATORS James M Calvn Department of Computer Scence
More informationNumerical integration in more dimensions part 2. Remo Minero
Numerca ntegraton n more dmensons part Remo Mnero Outne The roe of a mappng functon n mutdmensona ntegraton Gauss approach n more dmensons and quadrature rues Crtca anass of acceptabt of a gven quadrature
More informationInteractive Bi-Level Multi-Objective Integer. Non-linear Programming Problem
Appled Mathematcal Scences Vol 5 0 no 65 3 33 Interactve B-Level Mult-Objectve Integer Non-lnear Programmng Problem O E Emam Department of Informaton Systems aculty of Computer Scence and nformaton Helwan
More informationOptimization of JK Flip Flop Layout with Minimal Average Power of Consumption based on ACOR, Fuzzy-ACOR, GA, and Fuzzy-GA
Journa of mathematcs and computer Scence 4 (05) - 5 Optmzaton of JK Fp Fop Layout wth Mnma Average Power of Consumpton based on ACOR, Fuzzy-ACOR, GA, and Fuzzy-GA Farshd Kevanan *,, A Yekta *,, Nasser
More informationA parametric Linear Programming Model Describing Bandwidth Sharing Policies for ABR Traffic
parametrc Lnear Programmng Mode Descrbng Bandwdth Sharng Poces for BR Traffc I. Moschoos, M. Logothets and G. Kokknaks Wre ommuncatons Laboratory, Dept. of Eectrca & omputer Engneerng, Unversty of Patras,
More informationA finite difference method for heat equation in the unbounded domain
Internatona Conerence on Advanced ectronc Scence and Technoogy (AST 6) A nte derence method or heat equaton n the unbounded doman a Quan Zheng and Xn Zhao Coege o Scence North Chna nversty o Technoogy
More informationThe Minimum Universal Cost Flow in an Infeasible Flow Network
Journal of Scences, Islamc Republc of Iran 17(2): 175-180 (2006) Unversty of Tehran, ISSN 1016-1104 http://jscencesutacr The Mnmum Unversal Cost Flow n an Infeasble Flow Network H Saleh Fathabad * M Bagheran
More informationAssociative Memories
Assocatve Memores We consder now modes for unsupervsed earnng probems, caed auto-assocaton probems. Assocaton s the task of mappng patterns to patterns. In an assocatve memory the stmuus of an ncompete
More informationNODAL PRICES IN THE DAY-AHEAD MARKET
NODAL PRICES IN THE DAY-AHEAD MARET Fred Murphy Tempe Unversty AEG Meetng, Washngton, DC Sept. 7, 8 What we cover Two-stage stochastc program for contngency anayss n the day-ahead aucton. Fnd the LMPs
More informationON AUTOMATIC CONTINUITY OF DERIVATIONS FOR BANACH ALGEBRAS WITH INVOLUTION
European Journa of Mathematcs and Computer Scence Vo. No. 1, 2017 ON AUTOMATC CONTNUTY OF DERVATONS FOR BANACH ALGEBRAS WTH NVOLUTON Mohamed BELAM & Youssef T DL MATC Laboratory Hassan Unversty MORO CCO
More informationNP-Completeness : Proofs
NP-Completeness : Proofs Proof Methods A method to show a decson problem Π NP-complete s as follows. (1) Show Π NP. (2) Choose an NP-complete problem Π. (3) Show Π Π. A method to show an optmzaton problem
More information3. Stress-strain relationships of a composite layer
OM PO I O U P U N I V I Y O F W N ompostes ourse 8-9 Unversty of wente ng. &ech... tress-stran reatonshps of a composte ayer - Laurent Warnet & emo Aerman.. tress-stran reatonshps of a composte ayer Introducton
More informationA Variable Neighbourhood Descent Algorithm for the Redundancy Allocation Problem
EMS Vol. 4, No., pp. 94-0, June 2005. A Varable Neghbourhood Descent Algorthm for the Redundancy Allocaton Problem Yun-Cha Lang Cha-Chuan u Department of ndustral Engneerng and Management, Yuan Ze Unversty
More informationn-step cycle inequalities: facets for continuous n-mixing set and strong cuts for multi-module capacitated lot-sizing problem
n-step cyce nequates: facets for contnuous n-mxng set and strong cuts for mut-modue capactated ot-szng probem Mansh Bansa and Kavash Kanfar Department of Industra and Systems Engneerng, Texas A&M Unversty,
More informationMultispectral Remote Sensing Image Classification Algorithm Based on Rough Set Theory
Proceedngs of the 2009 IEEE Internatona Conference on Systems Man and Cybernetcs San Antono TX USA - October 2009 Mutspectra Remote Sensng Image Cassfcaton Agorthm Based on Rough Set Theory Yng Wang Xaoyun
More informationAssortment Optimization under MNL
Assortment Optmzaton under MNL Haotan Song Aprl 30, 2017 1 Introducton The assortment optmzaton problem ams to fnd the revenue-maxmzng assortment of products to offer when the prces of products are fxed.
More informationSpeeding up Computation of Scalar Multiplication in Elliptic Curve Cryptosystem
H.K. Pathak et. al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng Speedng up Computaton of Scalar Multplcaton n Ellptc Curve Cryptosystem H. K. Pathak Manju Sangh S.o.S n Computer scence
More informationInthem-machine flow shop problem, a set of jobs, each
THE ASYMPTOTIC OPTIMALITY OF THE SPT RULE FOR THE FLOW SHOP MEAN COMPLETION TIME PROBLEM PHILIP KAMINSKY Industra Engneerng and Operatons Research, Unversty of Caforna, Bereey, Caforna 9470, amnsy@eor.bereey.edu
More informationSupplementary Material: Learning Structured Weight Uncertainty in Bayesian Neural Networks
Shengyang Sun, Changyou Chen, Lawrence Carn Suppementary Matera: Learnng Structured Weght Uncertanty n Bayesan Neura Networks Shengyang Sun Changyou Chen Lawrence Carn Tsnghua Unversty Duke Unversty Duke
More informationEEL 6266 Power System Operation and Control. Chapter 3 Economic Dispatch Using Dynamic Programming
EEL 6266 Power System Operaton and Control Chapter 3 Economc Dspatch Usng Dynamc Programmng Pecewse Lnear Cost Functons Common practce many utltes prefer to represent ther generator cost functons as sngle-
More informationSIMULTANEOUS wireless information and power transfer. Joint Optimization of Power and Data Transfer in Multiuser MIMO Systems
Jont Optmzaton of Power and Data ransfer n Mutuser MIMO Systems Javer Rubo, Antono Pascua-Iserte, Dane P. Paomar, and Andrea Godsmth Unverstat Potècnca de Cataunya UPC, Barceona, Span ong Kong Unversty
More informationSimultaneous Optimization of Berth Allocation, Quay Crane Assignment and Quay Crane Scheduling Problems in Container Terminals
Smultaneous Optmzaton of Berth Allocaton, Quay Crane Assgnment and Quay Crane Schedulng Problems n Contaner Termnals Necat Aras, Yavuz Türkoğulları, Z. Caner Taşkın, Kuban Altınel Abstract In ths work,
More informationKernel Methods and SVMs Extension
Kernel Methods and SVMs Extenson The purpose of ths document s to revew materal covered n Machne Learnng 1 Supervsed Learnng regardng support vector machnes (SVMs). Ths document also provdes a general
More informationDesign and Optimization of Fuzzy Controller for Inverse Pendulum System Using Genetic Algorithm
Desgn and Optmzaton of Fuzzy Controller for Inverse Pendulum System Usng Genetc Algorthm H. Mehraban A. Ashoor Unversty of Tehran Unversty of Tehran h.mehraban@ece.ut.ac.r a.ashoor@ece.ut.ac.r Abstract:
More informationPortfolios with Trading Constraints and Payout Restrictions
Portfolos wth Tradng Constrants and Payout Restrctons John R. Brge Northwestern Unversty (ont wor wth Chrs Donohue Xaodong Xu and Gongyun Zhao) 1 General Problem (Very) long-term nvestor (eample: unversty
More informationThe Study of Teaching-learning-based Optimization Algorithm
Advanced Scence and Technology Letters Vol. (AST 06), pp.05- http://dx.do.org/0.57/astl.06. The Study of Teachng-learnng-based Optmzaton Algorthm u Sun, Yan fu, Lele Kong, Haolang Q,, Helongang Insttute
More informationProblem Set 9 Solutions
Desgn and Analyss of Algorthms May 4, 2015 Massachusetts Insttute of Technology 6.046J/18.410J Profs. Erk Demane, Srn Devadas, and Nancy Lynch Problem Set 9 Solutons Problem Set 9 Solutons Ths problem
More informationCS 3710: Visual Recognition Classification and Detection. Adriana Kovashka Department of Computer Science January 13, 2015
CS 3710: Vsual Recognton Classfcaton and Detecton Adrana Kovashka Department of Computer Scence January 13, 2015 Plan for Today Vsual recognton bascs part 2: Classfcaton and detecton Adrana s research
More informationImage Classification Using EM And JE algorithms
Machne earnng project report Fa, 2 Xaojn Sh, jennfer@soe Image Cassfcaton Usng EM And JE agorthms Xaojn Sh Department of Computer Engneerng, Unversty of Caforna, Santa Cruz, CA, 9564 jennfer@soe.ucsc.edu
More informationA PROBABILITY-DRIVEN SEARCH ALGORITHM FOR SOLVING MULTI-OBJECTIVE OPTIMIZATION PROBLEMS
HCMC Unversty of Pedagogy Thong Nguyen Huu et al. A PROBABILITY-DRIVEN SEARCH ALGORITHM FOR SOLVING MULTI-OBJECTIVE OPTIMIZATION PROBLEMS Thong Nguyen Huu and Hao Tran Van Department of mathematcs-nformaton,
More informationSingle-Facility Scheduling over Long Time Horizons by Logic-based Benders Decomposition
Sngle-Faclty Schedulng over Long Tme Horzons by Logc-based Benders Decomposton Elvn Coban and J. N. Hooker Tepper School of Busness, Carnege Mellon Unversty ecoban@andrew.cmu.edu, john@hooker.tepper.cmu.edu
More informationDevelopment of whole CORe Thermal Hydraulic analysis code CORTH Pan JunJie, Tang QiFen, Chai XiaoMing, Lu Wei, Liu Dong
Deveopment of whoe CORe Therma Hydrauc anayss code CORTH Pan JunJe, Tang QFen, Cha XaoMng, Lu We, Lu Dong cence and technoogy on reactor system desgn technoogy, Nucear Power Insttute of Chna, Chengdu,
More informationDynamic Analysis Of An Off-Road Vehicle Frame
Proceedngs of the 8th WSEAS Int. Conf. on NON-LINEAR ANALYSIS, NON-LINEAR SYSTEMS AND CHAOS Dnamc Anass Of An Off-Road Vehce Frame ŞTEFAN TABACU, NICOLAE DORU STĂNESCU, ION TABACU Automotve Department,
More informationInverse Kinematics From Position to Angles
Invere Knemat From Poton to Ange Invere Knemat Gven a dered poton P & orentaton R o the end-eetor Y,, z, O, A, T z q,, n Fnd the jont varabe whh an brng the robot to the dered onguraton. Sovabt 0 Gven
More informationExample: Suppose we want to build a classifier that recognizes WebPages of graduate students.
Exampe: Suppose we want to bud a cassfer that recognzes WebPages of graduate students. How can we fnd tranng data? We can browse the web and coect a sampe of WebPages of graduate students of varous unverstes.
More informationLecture 12: Discrete Laplacian
Lecture 12: Dscrete Laplacan Scrbe: Tanye Lu Our goal s to come up wth a dscrete verson of Laplacan operator for trangulated surfaces, so that we can use t n practce to solve related problems We are mostly
More informationPhysics 5153 Classical Mechanics. Principle of Virtual Work-1
P. Guterrez 1 Introducton Physcs 5153 Classcal Mechancs Prncple of Vrtual Work The frst varatonal prncple we encounter n mechancs s the prncple of vrtual work. It establshes the equlbrum condton of a mechancal
More informationANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)
Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of
More informationThe Application of BP Neural Network principal component analysis in the Forecasting the Road Traffic Accident
ICTCT Extra Workshop, Bejng Proceedngs The Appcaton of BP Neura Network prncpa component anayss n Forecastng Road Traffc Accdent He Mng, GuoXucheng &LuGuangmng Transportaton Coege of Souast Unversty 07
More informationThe line method combined with spectral chebyshev for space-time fractional diffusion equation
Apped and Computatona Mathematcs 014; 3(6): 330-336 Pubshed onne December 31, 014 (http://www.scencepubshnggroup.com/j/acm) do: 10.1164/j.acm.0140306.17 ISS: 3-5605 (Prnt); ISS: 3-5613 (Onne) The ne method
More informationNote 2. Ling fong Li. 1 Klein Gordon Equation Probablity interpretation Solutions to Klein-Gordon Equation... 2
Note 2 Lng fong L Contents Ken Gordon Equaton. Probabty nterpretaton......................................2 Soutons to Ken-Gordon Equaton............................... 2 2 Drac Equaton 3 2. Probabty nterpretaton.....................................
More informationPhysics 5153 Classical Mechanics. D Alembert s Principle and The Lagrangian-1
P. Guterrez Physcs 5153 Classcal Mechancs D Alembert s Prncple and The Lagrangan 1 Introducton The prncple of vrtual work provdes a method of solvng problems of statc equlbrum wthout havng to consder the
More informationBALANCING OF U-SHAPED ASSEMBLY LINE
BALANCING OF U-SHAPED ASSEMBLY LINE Nuchsara Krengkorakot, Naln Panthong and Rapeepan Ptakaso Industral Engneerng Department, Faculty of Engneerng, Ubon Rajathanee Unversty, Thaland Emal: ennuchkr@ubu.ac.th
More informationNumerical Investigation of Power Tunability in Two-Section QD Superluminescent Diodes
Numerca Investgaton of Power Tunabty n Two-Secton QD Superumnescent Dodes Matta Rossett Paoo Bardea Ivo Montrosset POLITECNICO DI TORINO DELEN Summary 1. A smpfed mode for QD Super Lumnescent Dodes (SLD)
More informationApproximate merging of a pair of BeÂzier curves
COMPUTER-AIDED DESIGN Computer-Aded Desgn 33 (1) 15±136 www.esever.com/ocate/cad Approxmate mergng of a par of BeÂzer curves Sh-Mn Hu a,b, *, Rou-Feng Tong c, Tao Ju a,b, Ja-Guang Sun a,b a Natona CAD
More informationAn Interactive Optimisation Tool for Allocation Problems
An Interactve Optmsaton ool for Allocaton Problems Fredr Bonäs, Joam Westerlund and apo Westerlund Process Desgn Laboratory, Faculty of echnology, Åbo Aadem Unversty, uru 20500, Fnland hs paper presents
More informationSection 8.3 Polar Form of Complex Numbers
80 Chapter 8 Secton 8 Polar Form of Complex Numbers From prevous classes, you may have encountered magnary numbers the square roots of negatve numbers and, more generally, complex numbers whch are the
More informationLine Drawing and Clipping Week 1, Lecture 2
CS 43 Computer Graphcs I Lne Drawng and Clppng Week, Lecture 2 Davd Breen, Wllam Regl and Maxm Peysakhov Geometrc and Intellgent Computng Laboratory Department of Computer Scence Drexel Unversty http://gcl.mcs.drexel.edu
More informationHMMT February 2016 February 20, 2016
HMMT February 016 February 0, 016 Combnatorcs 1. For postve ntegers n, let S n be the set of ntegers x such that n dstnct lnes, no three concurrent, can dvde a plane nto x regons (for example, S = {3,
More informationNUMERICAL DIFFERENTIATION
NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the
More informationA Derivative-Free Algorithm for Bound Constrained Optimization
Computatona Optmzaton and Appcatons, 21, 119 142, 2002 c 2002 Kuwer Academc Pubshers. Manufactured n The Netherands. A Dervatve-Free Agorthm for Bound Constraned Optmzaton STEFANO LUCIDI ucd@ds.unroma.t
More informationA polynomially solvable case of the pooling problem
A poynomay sovabe case of the poong probem Natasha Boand Georga Insttute of Technoogy, Atanta, U.S.A. Thomas Kanowsk Faban Rgternk The Unversty of Newcaste, Austraa arxv:1508.03181v4 [math.oc] 5 Apr 2016
More informationwe have E Y x t ( ( xl)) 1 ( xl), e a in I( Λ ) are as follows:
APPENDICES Aendx : the roof of Equaton (6 For j m n we have Smary from Equaton ( note that j '( ( ( j E Y x t ( ( x ( x a V ( ( x a ( ( x ( x b V ( ( x b V x e d ( abx ( ( x e a a bx ( x xe b a bx By usng
More informationOn the Multicriteria Integer Network Flow Problem
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 5, No 2 Sofa 2005 On the Multcrtera Integer Network Flow Problem Vassl Vasslev, Marana Nkolova, Maryana Vassleva Insttute of
More informationLower bounds for the Crossing Number of the Cartesian Product of a Vertex-transitive Graph with a Cycle
Lower bounds for the Crossng Number of the Cartesan Product of a Vertex-transtve Graph wth a Cyce Junho Won MIT-PRIMES December 4, 013 Abstract. The mnmum number of crossngs for a drawngs of a gven graph
More informationPolite Water-filling for Weighted Sum-rate Maximization in MIMO B-MAC Networks under. Multiple Linear Constraints
2011 IEEE Internatona Symposum on Informaton Theory Proceedngs Pote Water-fng for Weghted Sum-rate Maxmzaton n MIMO B-MAC Networks under Mutpe near Constrants An u 1, Youjan u 2, Vncent K. N. au 3, Hage
More informationDownlink Power Allocation for CoMP-NOMA in Multi-Cell Networks
Downn Power Aocaton for CoMP-NOMA n Mut-Ce Networs Md Shpon A, Eram Hossan, Arafat A-Dwe, and Dong In Km arxv:80.0498v [eess.sp] 6 Dec 207 Abstract Ths wor consders the probem of dynamc power aocaton n
More informationLecture Notes on Linear Regression
Lecture Notes on Lnear Regresson Feng L fl@sdueducn Shandong Unversty, Chna Lnear Regresson Problem In regresson problem, we am at predct a contnuous target value gven an nput feature vector We assume
More informationMultilayer Perceptron (MLP)
Multlayer Perceptron (MLP) Seungjn Cho Department of Computer Scence and Engneerng Pohang Unversty of Scence and Technology 77 Cheongam-ro, Nam-gu, Pohang 37673, Korea seungjn@postech.ac.kr 1 / 20 Outlne
More informationSampling-based Approach for Design Optimization in the Presence of Interval Variables
0 th Word Congress on Structura and Mutdscpnary Optmzaton May 9-4, 03, Orando, orda, USA Sampng-based Approach for Desgn Optmzaton n the Presence of nterva Varabes Davd Yoo and kn Lee Unversty of Connectcut,
More informationAmiri s Supply Chain Model. System Engineering b Department of Mathematics and Statistics c Odette School of Business
Amr s Supply Chan Model by S. Ashtab a,, R.J. Caron b E. Selvarajah c a Department of Industral Manufacturng System Engneerng b Department of Mathematcs Statstcs c Odette School of Busness Unversty of
More informationCyclic Codes BCH Codes
Cycc Codes BCH Codes Gaos Feds GF m A Gaos fed of m eements can be obtaned usng the symbos 0,, á, and the eements beng 0,, á, á, á 3 m,... so that fed F* s cosed under mutpcaton wth m eements. The operator
More informationL-Edge Chromatic Number Of A Graph
IJISET - Internatona Journa of Innovatve Scence Engneerng & Technoogy Vo. 3 Issue 3 March 06. ISSN 348 7968 L-Edge Chromatc Number Of A Graph Dr.R.B.Gnana Joth Assocate Professor of Mathematcs V.V.Vannaperuma
More informationCOMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS
Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS
More informationA SEPARABLE APPROXIMATION DYNAMIC PROGRAMMING ALGORITHM FOR ECONOMIC DISPATCH WITH TRANSMISSION LOSSES. Pierre HANSEN, Nenad MLADENOVI]
Yugoslav Journal of Operatons Research (00) umber 57-66 A SEPARABLE APPROXIMATIO DYAMIC PROGRAMMIG ALGORITHM FOR ECOOMIC DISPATCH WITH TRASMISSIO LOSSES Perre HASE enad MLADEOVI] GERAD and Ecole des Hautes
More informationA Class of Distributed Optimization Methods with Event-Triggered Communication
A Cass of Dstrbuted Optmzaton Methods wth Event-Trggered Communcaton Martn C. Mene Mchae Ubrch Sebastan Abrecht the date of recept and acceptance shoud be nserted ater Abstract We present a cass of methods
More informationResearch Article H Estimates for Discrete-Time Markovian Jump Linear Systems
Mathematca Probems n Engneerng Voume 213 Artce ID 945342 7 pages http://dxdoorg/11155/213/945342 Research Artce H Estmates for Dscrete-Tme Markovan Jump Lnear Systems Marco H Terra 1 Gdson Jesus 2 and
More informationAn Integrated OR/CP Method for Planning and Scheduling
An Integrated OR/CP Method for Plannng and Schedulng John Hooer Carnege Mellon Unversty IT Unversty of Copenhagen June 2005 The Problem Allocate tass to facltes. Schedule tass assgned to each faclty. Subect
More informationMean Field / Variational Approximations
Mean Feld / Varatonal Appromatons resented by Jose Nuñez 0/24/05 Outlne Introducton Mean Feld Appromaton Structured Mean Feld Weghted Mean Feld Varatonal Methods Introducton roblem: We have dstrbuton but
More informationCurve Fitting with the Least Square Method
WIKI Document Number 5 Interpolaton wth Least Squares Curve Fttng wth the Least Square Method Mattheu Bultelle Department of Bo-Engneerng Imperal College, London Context We wsh to model the postve feedback
More informationModule 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:
More informationPerfect Competition and the Nash Bargaining Solution
Perfect Competton and the Nash Barganng Soluton Renhard John Department of Economcs Unversty of Bonn Adenauerallee 24-42 53113 Bonn, Germany emal: rohn@un-bonn.de May 2005 Abstract For a lnear exchange
More informationMaximizing the number of nonnegative subsets
Maxmzng the number of nonnegatve subsets Noga Alon Hao Huang December 1, 213 Abstract Gven a set of n real numbers, f the sum of elements of every subset of sze larger than k s negatve, what s the maxmum
More informationOptimization Models for Heterogeneous Protocols
Optmzaton Modes for Heterogeneous Protocos Steven Low CS, EE netab.caltech.edu wth J. Doye, S. Hegde, L. L, A. Tang, J. Wang, Catech M. Chang, Prnceton Outne Internet protocos Horzonta decomposton TCP-AQM
More informationNested case-control and case-cohort studies
Outne: Nested case-contro and case-cohort studes Ørnuf Borgan Department of Mathematcs Unversty of Oso NORBIS course Unversty of Oso 4-8 December 217 1 Radaton and breast cancer data Nested case contro
More information2.3 Nilpotent endomorphisms
s a block dagonal matrx, wth A Mat dm U (C) In fact, we can assume that B = B 1 B k, wth B an ordered bass of U, and that A = [f U ] B, where f U : U U s the restrcton of f to U 40 23 Nlpotent endomorphsms
More informationSingular Value Decomposition: Theory and Applications
Sngular Value Decomposton: Theory and Applcatons Danel Khashab Sprng 2015 Last Update: March 2, 2015 1 Introducton A = UDV where columns of U and V are orthonormal and matrx D s dagonal wth postve real
More informationOptimal Solution to the Problem of Balanced Academic Curriculum Problem Using Tabu Search
Optmal Soluton to the Problem of Balanced Academc Currculum Problem Usng Tabu Search Lorna V. Rosas-Téllez 1, José L. Martínez-Flores 2, and Vttoro Zanella-Palacos 1 1 Engneerng Department,Unversdad Popular
More informationChapter 6 Hidden Markov Models. Chaochun Wei Spring 2018
896 920 987 2006 Chapter 6 Hdden Markov Modes Chaochun We Sprng 208 Contents Readng materas Introducton to Hdden Markov Mode Markov chans Hdden Markov Modes Parameter estmaton for HMMs 2 Readng Rabner,
More informationarxiv: v1 [math.ho] 18 May 2008
Recurrence Formulas for Fbonacc Sums Adlson J. V. Brandão, João L. Martns 2 arxv:0805.2707v [math.ho] 8 May 2008 Abstract. In ths artcle we present a new recurrence formula for a fnte sum nvolvng the Fbonacc
More informationDecentralized Adaptive Control for a Class of Large-Scale Nonlinear Systems with Unknown Interactions
Decentrazed Adaptve Contro for a Cass of Large-Scae onnear Systems wth Unknown Interactons Bahram Karm 1, Fatemeh Jahangr, Mohammad B. Menhaj 3, Iman Saboor 4 1. Center of Advanced Computatona Integence,
More informationLossy Compression. Compromise accuracy of reconstruction for increased compression.
Lossy Compresson Compromse accuracy of reconstructon for ncreased compresson. The reconstructon s usually vsbly ndstngushable from the orgnal mage. Typcally, one can get up to 0:1 compresson wth almost
More informationLogistic Regression. CAP 5610: Machine Learning Instructor: Guo-Jun QI
Logstc Regresson CAP 561: achne Learnng Instructor: Guo-Jun QI Bayes Classfer: A Generatve model odel the posteror dstrbuton P(Y X) Estmate class-condtonal dstrbuton P(X Y) for each Y Estmate pror dstrbuton
More information[WAVES] 1. Waves and wave forces. Definition of waves
1. Waves and forces Defnton of s In the smuatons on ong-crested s are consdered. The drecton of these s (μ) s defned as sketched beow n the goba co-ordnate sstem: North West East South The eevaton can
More informationComparison of the Population Variance Estimators. of 2-Parameter Exponential Distribution Based on. Multiple Criteria Decision Making Method
Appled Mathematcal Scences, Vol. 7, 0, no. 47, 07-0 HIARI Ltd, www.m-hkar.com Comparson of the Populaton Varance Estmators of -Parameter Exponental Dstrbuton Based on Multple Crtera Decson Makng Method
More informationA new Approach for Solving Linear Ordinary Differential Equations
, ISSN 974-57X (Onlne), ISSN 974-5718 (Prnt), Vol. ; Issue No. 1; Year 14, Copyrght 13-14 by CESER PUBLICATIONS A new Approach for Solvng Lnear Ordnary Dfferental Equatons Fawz Abdelwahd Department of
More information2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification
E395 - Pattern Recognton Solutons to Introducton to Pattern Recognton, Chapter : Bayesan pattern classfcaton Preface Ths document s a soluton manual for selected exercses from Introducton to Pattern Recognton
More informationNONLINEAR SYSTEM IDENTIFICATION BASE ON FW-LSSVM
Journa of heoretca and Apped Informaton echnoogy th February 3. Vo. 48 No. 5-3 JAI & LLS. A rghts reserved. ISSN: 99-8645 www.jatt.org E-ISSN: 87-395 NONLINEAR SYSEM IDENIFICAION BASE ON FW-LSSVM, XIANFANG
More informationChapter Newton s Method
Chapter 9. Newton s Method After readng ths chapter, you should be able to:. Understand how Newton s method s dfferent from the Golden Secton Search method. Understand how Newton s method works 3. Solve
More information