, t 1. Transitions - this one was easy, but in general the hardest part is choosing the which variables are state and control variables
|
|
- Peter Bates
- 6 years ago
- Views:
Transcription
1 Opmal Conrol Why Use I - verss calcls of varaons, opmal conrol More generaly More convenen wh consrans (e.g., can p consrans on he dervaves More nsghs no problem (a leas more apparen han hrogh calcls of varaons General Problem - ma f (,, ( d s.. '(,, (,,, ( fed; ( free Sae Varables - governed by frs order dfferenal eqaons (sae eqaons Conrol Varables - decson varables; assmed o be pecewse connos (connos ecep for fne nmber of pons ha aren'; affec objecve drecly and hrogh sae varables Sae Eqaon - dfferenal eqaon ha governs change n a sae varable; also called ranson eqaon; shold have one sae eqaon for each sae varable Fncons - f and g are connosly dfferenable fncons of hree ndependen argmens, none of whch s a dervave; n fac, here are no dervaves n opmal conrol ecep for he lef sde of he sae eqaons Smples Problem - ypcal calcls of varaon problem looks lke: ma f (,, '( d s.. ( Ths can be ransformed no opmal conrol problem by sng ma '( : f (,, ( d s.. '(, ( Transons - hs one was easy, b n general he hardes par s choosng he whch varables are sae and conrol varables Solvng - Hamlonan - H (,, (, ( f (,,,, (smlar o a lagrangan Properes of Opmal Solon - hese are necessary condons ( f g Opmaly Condon ( '( ( f g Mlpler Eqaon (3 ' ( Sae Eqaon (4 ( Bondary Condon (5 ( Bondary Condon (n hs case s free (6 H for ma ( for mn Second Order Condon Economc Inerpreaon - ( s margnal valaon of he assocaed sae varable a me ( becase s free; n mamzaon problem, we'd choose so we can' gan by changng so margnal benef of s zero ( of 9
2 General Procedre - [ Use ( o fnd ha mamzes H (same condon for mnmzaon problem [ Solve from [ as a fncon of,, and λ: (,, [3 Sbse o of ( and (3 whch gves s a sysem of wo dfferenal eqaons for and λ (,, (,, g(,, (,, ' '(,, (,, Eqvalen o Eler and Transversaly - Proof: look a ma f (,, '( d s.. ( Already showed eqvalen opmal conrol problem (se '( H f (,, From (: f λ d Dfferenae boh sdes wr : λ '( d From (: '( ( f g (becase d d Combne hese o ge Eler Eqaon: d d ' From (5: (... ha's he ransversaly condon: f ' From (6: H... same as he Legendre condon f 5 Eample - ma ( d s.. ', ( (,, f g (,, H f (,,,, ( Opmal solon properes: ( ( '( ( f g ( (3 ' ( (4 ( and (5 (5 (6 H (reqred for ma... good Solve ( for : of 9 ' '
3 Sb no ( and (3 for sysem of dfferenal eqaons: 3 '( 3 3 '( From here we se Trck 4 from he DffEq hando: r r General Solon: Ae Ae r r ( r a A ( r a A ( Be Be (7 B and (8 B b 3 b a b Coeffcen Mar: 3 3 a b 3 r 3 Characersc Eqaon: ( 3 ( r r (egenvales r 3 r r 3 r 3 and r Eqaons & 4 Unknowns - now se (4, (5, (7, (8 o solve for A, A, B, B Several Varables Each sae varable ( needs a sae eqaon: '( g ( Need one mlpler for each sae eqaon: '( Each conrol varable yelds one opmaly condon: # conrol varables cold be >,, or < # of sae varables General Problem - sae varables ( and conrol varables (j f (, (, (, (, ( d j '( g (,, (, (, ( (, ( (, ( ma s..,, (sae eqaons fed; free (end condons Hamlonan - H (,, (, (, ( f g g Properes of Opmal Solon - ( g g, j, j j j ( H f g g '(,, (3 '( g (, (, (, (, (,, (4 fed end condons from above (5 (,, j j 3 of 9
4 End Pon Condons Fed End Pons - modfy orgnal problem for boh end pons fed: ma Dervng Necessary Condons - Le J * be ma vale J * P f (, *, * d f (,, ( d s.. '(,, ( (, ( ± *', b realze *' g(, *, *,, all fed : J * [ f (, *, *, *, * *' Wha o ge rde of he *' n he las erm so solve ha wh negraon by pars: b a b b a UdV UV VdU... le U λ and dv *' d du λ' d and V * *' d * * a * ' d Plg ha back n: [ f (, *, *, *, * ' * J * d * * Pck a comparson pah [ f,,,, ' Snce J * s ma vale: J J* J Sb formlas: J ( d J {[ f (,,,, ' [ f (, *, *, *, * ' * } d * * Snce J s a feasble pah, ms have he same endpons as J * erms osde negral dsappear: * * Defne: Sb no L(,,, f (,,,, ' J : J [ L(,,, L(, *, *, d Ths s a dfference of he same fncon so we can se Taylor seres o appromae (noe ha and λ are fed: L(,,, L(, *, *, L (, *, *, ( * L (, *, *, ( * h.o.. where L f g ' and L f g d 4 of 9
5 Drop he hgher order erms o appromae J J : [( f g '( * ( f g ( * d Snce hs condon holds for arbrary, le ' ( f g (mlpler eqaon Now we have J ( f g ( * d Snce * can be >,, or <, we ms have f g (opmaly condon So we end p wh he same necessary condons we had on p., ecep condon (5 In general condon (5 wll be ( f s fed ( f s free Salvage Vale - back o orgnal problem and add salvage vale o objecve fncon (.e., vale ha's only based on endng condons and ; noe f boh and are fed, hs salvage vale s rrelevan becase wold be consan ma f (,, ( d φ(, s.. '(,, (,, ( fed, ( free Dervng Necessary Condons - 3 sep solon Sep - arbrarly choose fnal sae (... we js covered how o solve ha n prevos secon Le V ( f (, *, * d be ma vale of negral gven P * ± *', b realze *' g(, *, * : V * [ f (, *, *, *, * *' Wha o ge rde of he *' n he las erm so solve ha wh negraon by pars js lke we dd las me so we end p wh: ('s on he mddle of he prevos page Le [ f (, *, *, *, * ' * V * d * * (, *, * f g so now V * [ H (, *, * ' * d * * H Sep - show ( s margnal vale of ( Look a - look a change n V * wh respec o : d d* d* d d* d ' H H H H ' * d ( d d d d d d λ was chosen arbrarly so 's ndependen of ; also me s ndependen of hose dervaves are zero ( d 5 of 9
6 ( H d* ' d d* d d H We know from necessary condons ha ( H f g ' and H he negral goes away and we have V * (... whch means he change n he mamm vale ( V * cased by a change n he sarng condon ( s (....e., margnal vale of ( Look a any (, - js break p he nerval; s nal pon for second negral; same as n prevos proof V * ( f (, *, * d f (, *, * d f (, *, * d Look a - look a change n V * wh respec o : d d* d* d d* d ' H H H H ' * d ( d d d d d d λ was chosen arbrarly so 's ndependen of ; also me s ndependen of hose dervaves are zero d* d* ( H ' H d ( d d We know from necessary condons ha H f g ' and H he negral goes away and we have (... whch means he change n he mamm vale ( V * cased by a change n he endng condon ( s ( `....e., margnal vale of ( Sep 3 - consder ( beng free; f we ge o choose ( obvosly we'd wan o mamze V * so we end p wh (... hs gnores he salvage vale hogh To consder he salvage vale le W f,, ( d φ(, ( Now defne W* ( V* φ(, s ma vale for gven ( We wan o mamze W * wr ( : W* φ(, φ(, ( whch means he margnal benef of salvage margnal cos of salvage Noe: f here s no salvage erm φ so hs becomes he same as he condon we had before ( 6 of 9
7 Eample - monopoly bldng prodc over me and sellng a me : r ma e P( [ c c revene (salvage e cos over me Endpon Consran - back o orgnal problem and add consran o endpon ma f (,, ( d s.. '(,, ( and K (,, ( fed, ( free Dervng Necessary Condons - Use same mehodology as prevos dervaon o ge V *( Now search for opmal ( sasfyng consran K ( Lagrangan: L V* ( p( K( KT Condons: L V* ( p( K'(, wh eqaly f ( > We know ( > and ( ( p( K'( L K(, wh eqaly f p >... so we ge cases: p (a f no bndng ( K ( >, hen p ( (b f bndng, hen K ( and ( p( K'( When solvng a problem, we sally have o look a boh cases and pck he one ha works (one probably won' have a solon; f boh have a solon, hen plg he solon no he objecve o see whch s beer r d Margnal vale of consran a Margnal vale of consran Change n consran a Free Horzon - back o orgnal problem and le be free ma f (,, ( d s.. '(,, (, (, ( fed, free Dervng Necessary Condons - V * (, H (, *, * ' * d ( ( Use same mehodology o ge [ Now opmze V * (, wr... need Lebnz Rle becase s n negral d* d* H (, *(, *(, ( λ'( *( ( H ' H d d d Noe erms ha cancel '( 7 of 9
8 Also a opmm we know H ' and H so negral erm goes away ( f g H (, * (, *(, ( Opmal Adversng Eample - a( adversng ependre A( goodwll (accmlaed adversng ependre Deprecaon of goodwll: A' ( a( δa( Sales: q ( f ( p(, A( Revene: p ( q( Revene ne of prodcon cos: R( p(, A( p( q( C( q( Insananeos prof: R( p(, A( a( Problem: ma e r [ R( p(, A( a( a(, p( d s.. A' ( a( δa(, a (, p (, A ( A fed Conrol Varables - a ( and p ( Sae Varable - A ( r H e ( R a ( a δa Observaons - (a prce eners negrand (objecve, b no sae r R (a e eqaon we can se sac opmzaon p p Le ( A R( p*( A, A (b r e p* ( A arg ma R( p, A (.e., p* s vale ha a p ( r R mamzes R for a gven A ' e δ (b a doesn' ener s own frs order condon so we can' solve he radonal way Look a problem wh only conrol varable r H e ( ( A a ( a δa r ( e a r ( A ( ' e δ Sysem of Dfferenal Eqaons: A' a δa ( A ' e r δ... can' go frher wh DffEq who more deals on ( A Can sll solve problem hogh: ( e r lm ( and ' re r Sb e r r ( A r no (: ' e δe r r ( A Se hese eqal: ' re e δe r 8 of 9
9 ( A ( A Cancel e -r : r δ r δ A* s consan over me and we wan o ge o as soon as possble (jmp o mmedaely, no gradally A' a δa a* δa* Solon: If A < A* hen wan o jmp o A * so a* ( A* A If A > A* hen a *( nl A ( A*, hen a* ( δa Smmary - ma f (,, d s.. sae eqaon: '(,, sar me: fed end me: fed (nless saed oherwse sar condon: ( fed end condon: ( free (nless saed oherwse H (,,, f g Necessary Condons - (a ' (,, Sae eqaon g (b ' Mlpler (cosae, alary, adjon eqaon g (c Opmaly condon (d Transversaly condons: ( fed and ( fed ( ( fed and ( free ( ( free and ( fed H ( f g (v Endpon Consran: K ( Cases - solve boh; f here s a conradcon n one, 's no a solon; f here s no conradcon, ha case s a solon (cold have more han one or none Case - Bndng ( K ( ( pk'( Case - No bndng ( K ( > p so ( (v Salvage Vale: add φ (, o objecve wh fed and ( free (, ( φ 9 of 9
Mechanics Physics 151
Mechancs Physcs 5 Lecure 0 Canoncal Transformaons (Chaper 9) Wha We Dd Las Tme Hamlon s Prncple n he Hamlonan formalsm Dervaon was smple δi δ Addonal end-pon consrans pq H( q, p, ) d 0 δ q ( ) δq ( ) δ
More informationMechanics Physics 151
Mechancs Physcs 5 Lecure 9 Hamlonan Equaons of Moon (Chaper 8) Wha We Dd Las Tme Consruced Hamlonan formalsm H ( q, p, ) = q p L( q, q, ) H p = q H q = p H = L Equvalen o Lagrangan formalsm Smpler, bu
More informationMechanics Physics 151
Mechancs Physcs 5 Lecure 9 Hamlonan Equaons of Moon (Chaper 8) Wha We Dd Las Tme Consruced Hamlonan formalsm Hqp (,,) = qp Lqq (,,) H p = q H q = p H L = Equvalen o Lagrangan formalsm Smpler, bu wce as
More informationStochastic Programming handling CVAR in objective and constraint
Sochasc Programmng handlng CVAR n obecve and consran Leondas Sakalaskas VU Inse of Mahemacs and Informacs Lhana ICSP XIII Jly 8-2 23 Bergamo Ialy Olne Inrodcon Lagrangan & KKT condons Mone-Carlo samplng
More informationby Lauren DeDieu Advisor: George Chen
b Laren DeDe Advsor: George Chen Are one of he mos powerfl mehods o nmercall solve me dependen paral dfferenal eqaons PDE wh some knd of snglar shock waves & blow-p problems. Fed nmber of mesh pons Moves
More informationGraduate Macroeconomics 2 Problem set 5. - Solutions
Graduae Macroeconomcs 2 Problem se. - Soluons Queson 1 To answer hs queson we need he frms frs order condons and he equaon ha deermnes he number of frms n equlbrum. The frms frs order condons are: F K
More information( ) () we define the interaction representation by the unitary transformation () = ()
Hgher Order Perurbaon Theory Mchael Fowler 3/7/6 The neracon Represenaon Recall ha n he frs par of hs course sequence, we dscussed he chrödnger and Hesenberg represenaons of quanum mechancs here n he chrödnger
More informationSolution in semi infinite diffusion couples (error function analysis)
Soluon n sem nfne dffuson couples (error funcon analyss) Le us consder now he sem nfne dffuson couple of wo blocks wh concenraon of and I means ha, n a A- bnary sysem, s bondng beween wo blocks made of
More informationCONSISTENT EARTHQUAKE ACCELERATION AND DISPLACEMENT RECORDS
APPENDX J CONSSTENT EARTHQUAKE ACCEERATON AND DSPACEMENT RECORDS Earhqake Acceleraons can be Measred. However, Srcres are Sbjeced o Earhqake Dsplacemens J. NTRODUCTON { XE "Acceleraon Records" }A he presen
More informationLecture 2 M/G/1 queues. M/G/1-queue
Lecure M/G/ queues M/G/-queue Posson arrval process Arbrary servce me dsrbuon Sngle server To deermne he sae of he sysem a me, we mus now The number of cusomers n he sysems N() Tme ha he cusomer currenly
More informationDifferent kind of oscillation
PhO 98 Theorecal Qeson.Elecrcy Problem (8 pons) Deren knd o oscllaon e s consder he elecrc crc n he gre, or whch mh, mh, nf, nf and kω. The swch K beng closed he crc s copled wh a sorce o alernang crren.
More informationEEL 6266 Power System Operation and Control. Chapter 5 Unit Commitment
EEL 6266 Power Sysem Operaon and Conrol Chaper 5 Un Commmen Dynamc programmng chef advanage over enumeraon schemes s he reducon n he dmensonaly of he problem n a src prory order scheme, here are only N
More informationJ i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes.
umercal negraon of he dffuson equaon (I) Fne dfference mehod. Spaal screaon. Inernal nodes. R L V For hermal conducon le s dscree he spaal doman no small fne spans, =,,: Balance of parcles for an nernal
More informationfrom normal distribution table It is interesting to notice in the above computation that the starting stock level each
Homeork Solon Par A. Ch a b 65 4 5 from normal dsrbon able Ths, order qany s 39-7 b o b5 from normal dsrbon able Ths, order qany s 9-7 I s neresng o noce n he above compaon ha he sarng sock level each
More informationCS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4
CS434a/54a: Paern Recognon Prof. Olga Veksler Lecure 4 Oulne Normal Random Varable Properes Dscrmnan funcons Why Normal Random Varables? Analycally racable Works well when observaon comes form a corruped
More informationLecture 18: The Laplace Transform (See Sections and 14.7 in Boas)
Lecure 8: The Lalace Transform (See Secons 88- and 47 n Boas) Recall ha our bg-cure goal s he analyss of he dfferenal equaon, ax bx cx F, where we emloy varous exansons for he drvng funcon F deendng on
More informationDynamic Team Decision Theory. EECS 558 Project Shrutivandana Sharma and David Shuman December 10, 2005
Dynamc Team Decson Theory EECS 558 Proec Shruvandana Sharma and Davd Shuman December 0, 005 Oulne Inroducon o Team Decson Theory Decomposon of he Dynamc Team Decson Problem Equvalence of Sac and Dynamc
More informationNotes on the stability of dynamic systems and the use of Eigen Values.
Noes on he sabl of dnamc ssems and he use of Egen Values. Source: Macro II course noes, Dr. Davd Bessler s Tme Seres course noes, zarads (999) Ineremporal Macroeconomcs chaper 4 & Techncal ppend, and Hamlon
More informationIs it necessary to seasonally adjust business and consumer surveys. Emmanuelle Guidetti
Is necessar o seasonall adjs bsness and consmer srves Emmanelle Gde Olne 1 BTS feares 2 Smlaon eercse 3 Seasonal ARIMA modellng 4 Conclsons Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01
More informationLet s treat the problem of the response of a system to an applied external force. Again,
Page 33 QUANTUM LNEAR RESPONSE FUNCTON Le s rea he problem of he response of a sysem o an appled exernal force. Agan, H() H f () A H + V () Exernal agen acng on nernal varable Hamlonan for equlbrum sysem
More informationIn the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!") i+1,q - [(!
ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL The frs hng o es n wo-way ANOVA: Is here neracon? "No neracon" means: The man effecs model would f. Ths n urn means: In he neracon plo (wh A on he horzonal
More informationIncluding the ordinary differential of distance with time as velocity makes a system of ordinary differential equations.
Soluons o Ordnary Derenal Equaons An ordnary derenal equaon has only one ndependen varable. A sysem o ordnary derenal equaons consss o several derenal equaons each wh he same ndependen varable. An eample
More information[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5
TPG460 Reservor Smulaon 08 page of 5 DISCRETIZATIO OF THE FOW EQUATIOS As we already have seen, fne dfference appromaons of he paral dervaves appearng n he flow equaons may be obaned from Taylor seres
More informationSolving Parabolic Partial Delay Differential. Equations Using The Explicit Method And Higher. Order Differences
Jornal of Kfa for Maemacs and Compe Vol. No.7 Dec pp 77-5 Solvng Parabolc Paral Delay Dfferenal Eqaons Usng e Eplc Meod And Hger Order Dfferences Asss. Prof. Amal Kalaf Haydar Kfa Unversy College of Edcaon
More informationPHYS 705: Classical Mechanics. Canonical Transformation
PHYS 705: Classcal Mechancs Canoncal Transformaon Canoncal Varables and Hamlonan Formalsm As we have seen, n he Hamlonan Formulaon of Mechancs,, are ndeenden varables n hase sace on eual foong The Hamlon
More informationDensity Matrix Description of NMR BCMB/CHEM 8190
Densy Marx Descrpon of NMR BCMBCHEM 89 Operaors n Marx Noaon If we say wh one bass se, properes vary only because of changes n he coeffcens weghng each bass se funcon x = h< Ix > - hs s how we calculae
More informationObserver Design for Nonlinear Systems using Linear Approximations
Observer Desgn for Nonlnear Ssems sng Lnear Appromaons C. Navarro Hernandez, S.P. Banks and M. Aldeen Deparmen of Aomac Conrol and Ssems Engneerng, Unvers of Sheffeld, Mappn Sree, Sheffeld S 3JD. e-mal:
More informationOrdinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s
Ordnary Dfferenal Equaons n Neuroscence wh Malab eamples. Am - Gan undersandng of how o se up and solve ODE s Am Undersand how o se up an solve a smple eample of he Hebb rule n D Our goal a end of class
More informationCS286.2 Lecture 14: Quantum de Finetti Theorems II
CS286.2 Lecure 14: Quanum de Fne Theorems II Scrbe: Mara Okounkova 1 Saemen of he heorem Recall he las saemen of he quanum de Fne heorem from he prevous lecure. Theorem 1 Quanum de Fne). Le ρ Dens C 2
More informationVariants of Pegasos. December 11, 2009
Inroducon Varans of Pegasos SooWoong Ryu bshboy@sanford.edu December, 009 Youngsoo Cho yc344@sanford.edu Developng a new SVM algorhm s ongong research opc. Among many exng SVM algorhms, we wll focus on
More informationPart II CONTINUOUS TIME STOCHASTIC PROCESSES
Par II CONTINUOUS TIME STOCHASTIC PROCESSES 4 Chaper 4 For an advanced analyss of he properes of he Wener process, see: Revus D and Yor M: Connuous marngales and Brownan Moon Karazas I and Shreve S E:
More informationMethod of Characteristics for Pure Advection By Gilberto E. Urroz, September 2004
Mehod of Charaerss for Pre Adveon By Glbero E Urroz Sepember 004 Noe: The followng noes are based on lass noes for he lass COMPUTATIONAL HYDAULICS as agh by Dr Forres Holly n he Sprng Semeser 985 a he
More informationUNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION
INTERNATIONAL TRADE T. J. KEHOE UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 27 EXAMINATION Please answer wo of he hree quesons. You can consul class noes, workng papers, and arcles whle you are workng on he
More informationDensity Matrix Description of NMR BCMB/CHEM 8190
Densy Marx Descrpon of NMR BCMBCHEM 89 Operaors n Marx Noaon Alernae approach o second order specra: ask abou x magnezaon nsead of energes and ranson probables. If we say wh one bass se, properes vary
More informationAdvanced Macroeconomics II: Exchange economy
Advanced Macroeconomcs II: Exchange economy Krzyszof Makarsk 1 Smple deermnsc dynamc model. 1.1 Inroducon Inroducon Smple deermnsc dynamc model. Defnons of equlbrum: Arrow-Debreu Sequenal Recursve Equvalence
More informationLecture VI Regression
Lecure VI Regresson (Lnear Mehods for Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure VI: MLSC - Dr. Sehu Vjayakumar Lnear Regresson Model M
More informationFTCS Solution to the Heat Equation
FTCS Soluon o he Hea Equaon ME 448/548 Noes Gerald Reckenwald Porland Sae Unversy Deparmen of Mechancal Engneerng gerry@pdxedu ME 448/548: FTCS Soluon o he Hea Equaon Overvew Use he forward fne d erence
More informationLecture 6: Learning for Control (Generalised Linear Regression)
Lecure 6: Learnng for Conrol (Generalsed Lnear Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure 6: RLSC - Prof. Sehu Vjayakumar Lnear Regresson
More informationRelative controllability of nonlinear systems with delays in control
Relave conrollably o nonlnear sysems wh delays n conrol Jerzy Klamka Insue o Conrol Engneerng, Slesan Techncal Unversy, 44- Glwce, Poland. phone/ax : 48 32 37227, {jklamka}@a.polsl.glwce.pl Keywor: Conrollably.
More informationTSS = SST + SSE An orthogonal partition of the total SS
ANOVA: Topc 4. Orhogonal conrass [ST&D p. 183] H 0 : µ 1 = µ =... = µ H 1 : The mean of a leas one reamen group s dfferen To es hs hypohess, a basc ANOVA allocaes he varaon among reamen means (SST) equally
More information2/20/2013. EE 101 Midterm 2 Review
//3 EE Mderm eew //3 Volage-mplfer Model The npu ressance s he equalen ressance see when lookng no he npu ermnals of he amplfer. o s he oupu ressance. I causes he oupu olage o decrease as he load ressance
More informationAdvanced time-series analysis (University of Lund, Economic History Department)
Advanced me-seres analss (Unvers of Lund, Economc Hsor Dearmen) 3 Jan-3 Februar and 6-3 March Lecure 4 Economerc echnues for saonar seres : Unvarae sochasc models wh Box- Jenns mehodolog, smle forecasng
More informationVariational method to the second-order impulsive partial differential equations with inconstant coefficients (I)
Avalable onlne a www.scencedrec.com Proceda Engneerng 6 ( 5 4 Inernaonal Worksho on Aomoble, Power and Energy Engneerng Varaonal mehod o he second-order mlsve aral dfferenal eqaons wh nconsan coeffcens
More informationAE/ME 339. K. M. Isaac. 8/31/2004 topic4: Implicit method, Stability, ADI method. Computational Fluid Dynamics (AE/ME 339) MAEEM Dept.
AE/ME 339 Comptatonal Fld Dynamcs (CFD) Comptatonal Fld Dynamcs (AE/ME 339) Implct form of dfference eqaton In the prevos explct method, the solton at tme level n,,n, depended only on the known vales of,
More informationHEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD
Journal of Appled Mahemacs and Compuaonal Mechancs 3, (), 45-5 HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Sansław Kukla, Urszula Sedlecka Insue of Mahemacs,
More informationDepartment of Economics University of Toronto
Deparmen of Economcs Unversy of Torono ECO408F M.A. Economercs Lecure Noes on Heeroskedascy Heeroskedascy o Ths lecure nvolves lookng a modfcaons we need o make o deal wh he regresson model when some of
More informationChapter 6 DETECTION AND ESTIMATION: Model of digital communication system. Fundamental issues in digital communications are
Chaper 6 DCIO AD IMAIO: Fndaenal sses n dgal concaons are. Deecon and. saon Deecon heory: I deals wh he desgn and evalaon of decson ang processor ha observes he receved sgnal and gesses whch parclar sybol
More informationHow about the more general "linear" scalar functions of scalars (i.e., a 1st degree polynomial of the following form with a constant term )?
lmcd Lnear ransformaon of a vecor he deas presened here are que general hey go beyond he radonal mar-vecor ype seen n lnear algebra Furhermore, hey do no deal wh bass and are equally vald for any se of
More information. The geometric multiplicity is dim[ker( λi. number of linearly independent eigenvectors associated with this eigenvalue.
Lnear Algebra Lecure # Noes We connue wh he dscusson of egenvalues, egenvecors, and dagonalzably of marces We wan o know, n parcular wha condons wll assure ha a marx can be dagonalzed and wha he obsrucons
More informationCH.3. COMPATIBILITY EQUATIONS. Continuum Mechanics Course (MMC) - ETSECCPB - UPC
CH.3. COMPATIBILITY EQUATIONS Connuum Mechancs Course (MMC) - ETSECCPB - UPC Overvew Compably Condons Compably Equaons of a Poenal Vecor Feld Compably Condons for Infnesmal Srans Inegraon of he Infnesmal
More information. The geometric multiplicity is dim[ker( λi. A )], i.e. the number of linearly independent eigenvectors associated with this eigenvalue.
Mah E-b Lecure #0 Noes We connue wh he dscusson of egenvalues, egenvecors, and dagonalzably of marces We wan o know, n parcular wha condons wll assure ha a marx can be dagonalzed and wha he obsrucons are
More informationV.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS
R&RATA # Vol.) 8, March FURTHER AALYSIS OF COFIDECE ITERVALS FOR LARGE CLIET/SERVER COMPUTER ETWORKS Vyacheslav Abramov School of Mahemacal Scences, Monash Unversy, Buldng 8, Level 4, Clayon Campus, Wellngon
More informationRobustness Experiments with Two Variance Components
Naonal Insue of Sandards and Technology (NIST) Informaon Technology Laboraory (ITL) Sascal Engneerng Dvson (SED) Robusness Expermens wh Two Varance Componens by Ana Ivelsse Avlés avles@ns.gov Conference
More informationNATIONAL UNIVERSITY OF SINGAPORE PC5202 ADVANCED STATISTICAL MECHANICS. (Semester II: AY ) Time Allowed: 2 Hours
NATONAL UNVERSTY OF SNGAPORE PC5 ADVANCED STATSTCAL MECHANCS (Semeser : AY 1-13) Tme Allowed: Hours NSTRUCTONS TO CANDDATES 1. Ths examnaon paper conans 5 quesons and comprses 4 prned pages.. Answer all
More informationAdvanced Machine Learning & Perception
Advanced Machne Learnng & Percepon Insrucor: Tony Jebara SVM Feaure & Kernel Selecon SVM Eensons Feaure Selecon (Flerng and Wrappng) SVM Feaure Selecon SVM Kernel Selecon SVM Eensons Classfcaon Feaure/Kernel
More informationVolatility Interpolation
Volaly Inerpolaon Prelmnary Verson March 00 Jesper Andreasen and Bran Huge Danse Mares, Copenhagen wan.daddy@danseban.com brno@danseban.com Elecronc copy avalable a: hp://ssrn.com/absrac=69497 Inro Local
More informationTHE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS
THE PREICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS INTROUCTION The wo dmensonal paral dfferenal equaons of second order can be used for he smulaon of compeve envronmen n busness The arcle presens he
More informationCS 3750 Machine Learning Lecture 6. Monte Carlo methods. CS 3750 Advanced Machine Learning. Markov chain Monte Carlo
CS 3750 Machne Learnng Lectre 6 Monte Carlo methods Mlos Haskrecht mlos@cs.ptt.ed 5329 Sennott Sqare Markov chan Monte Carlo Importance samplng: samples are generated accordng to Q and every sample from
More informationT q (heat generation) Figure 0-1: Slab heated with constant source 2 = q k
IFFERETIL EQUTIOS, PROBLE BOURY VLUE 5. ITROUCTIO s as been noed n e prevous caper, boundary value problems BVP for ordnary dfferenal equaons ave boundary condons specfed a more an one pon of e ndependen
More informationDerivatives of Inverse Trig Functions
Derivaives of Inverse Trig Fncions Ne we will look a he erivaives of he inverse rig fncions. The formlas may look complicae, b I hink yo will fin ha hey are no oo har o se. Yo will js have o be carefl
More informationSolution of a diffusion problem in a non-homogeneous flow and diffusion field by the integral representation method (IRM)
Appled and ompaonal Mahemacs 4; 3: 5-6 Pblshed onlne Febrary 4 hp://www.scencepblshnggrop.com//acm do:.648/.acm.43.3 olon of a dffson problem n a non-homogeneos flow and dffson feld by he negral represenaon
More informationTesting a new idea to solve the P = NP problem with mathematical induction
Tesng a new dea o solve he P = NP problem wh mahemacal nducon Bacground P and NP are wo classes (ses) of languages n Compuer Scence An open problem s wheher P = NP Ths paper ess a new dea o compare he
More informationBlock 5 Transport of solutes in rivers
Nmeral Hydrals Blok 5 Transpor of soles n rvers Marks Holzner Conens of he orse Blok 1 The eqaons Blok Compaon of pressre srges Blok 3 Open hannel flow flow n rvers Blok 4 Nmeral solon of open hannel flow
More informationReal-Time Trajectory Generation and Tracking for Cooperative Control Systems
Real-Tme Trajecor Generaon and Trackng for Cooperave Conrol Ssems Rchard Mrra Jason Hcke Calforna Inse of Technolog MURI Kckoff Meeng 14 Ma 2001 Olne I. Revew of prevos work n rajecor generaon and rackng
More informationExistence and Uniqueness Results for Random Impulsive Integro-Differential Equation
Global Journal of Pure and Appled Mahemacs. ISSN 973-768 Volume 4, Number 6 (8), pp. 89-87 Research Inda Publcaons hp://www.rpublcaon.com Exsence and Unqueness Resuls for Random Impulsve Inegro-Dfferenal
More informationGENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim
Korean J. Mah. 19 (2011), No. 3, pp. 263 272 GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS Youngwoo Ahn and Kae Km Absrac. In he paper [1], an explc correspondence beween ceran
More informationOnline Appendix for. Strategic safety stocks in supply chains with evolving forecasts
Onlne Appendx for Sraegc safey socs n supply chans wh evolvng forecass Tor Schoenmeyr Sephen C. Graves Opsolar, Inc. 332 Hunwood Avenue Hayward, CA 94544 A. P. Sloan School of Managemen Massachuses Insue
More informationCHAPTER 10: LINEAR DISCRIMINATION
CHAPER : LINEAR DISCRIMINAION Dscrmnan-based Classfcaon 3 In classfcaon h K classes (C,C,, C k ) We defned dscrmnan funcon g j (), j=,,,k hen gven an es eample, e chose (predced) s class label as C f g
More informationFI 3103 Quantum Physics
/9/4 FI 33 Quanum Physcs Aleander A. Iskandar Physcs of Magnesm and Phooncs Research Grou Insu Teknolog Bandung Basc Conces n Quanum Physcs Probably and Eecaon Value Hesenberg Uncerany Prncle Wave Funcon
More informationOn One Analytic Method of. Constructing Program Controls
Appled Mahemacal Scences, Vol. 9, 05, no. 8, 409-407 HIKARI Ld, www.m-hkar.com hp://dx.do.org/0.988/ams.05.54349 On One Analyc Mehod of Consrucng Program Conrols A. N. Kvko, S. V. Chsyakov and Yu. E. Balyna
More informationBundling with Customer Self-Selection: A Simple Approach to Bundling Low Marginal Cost Goods On-Line Appendix
Bundlng wh Cusomer Self-Selecon: A Smple Approach o Bundlng Low Margnal Cos Goods On-Lne Appendx Lorn M. H Unversy of Pennsylvana, Wharon School 57 Jon M. Hunsman Hall Phladelpha, PA 94 lh@wharon.upenn.edu
More informationCubic Bezier Homotopy Function for Solving Exponential Equations
Penerb Journal of Advanced Research n Compung and Applcaons ISSN (onlne: 46-97 Vol. 4, No.. Pages -8, 6 omoopy Funcon for Solvng Eponenal Equaons S. S. Raml *,,. Mohamad Nor,a, N. S. Saharzan,b and M.
More information( t) Outline of program: BGC1: Survival and event history analysis Oslo, March-May Recapitulation. The additive regression model
BGC1: Survval and even hsory analyss Oslo, March-May 212 Monday May 7h and Tuesday May 8h The addve regresson model Ørnulf Borgan Deparmen of Mahemacs Unversy of Oslo Oulne of program: Recapulaon Counng
More informationLinear Response Theory: The connection between QFT and experiments
Phys540.nb 39 3 Lnear Response Theory: The connecon beween QFT and expermens 3.1. Basc conceps and deas Q: ow do we measure he conducvy of a meal? A: we frs nroduce a weak elecrc feld E, and hen measure
More informationMotion in Two Dimensions
Phys 1 Chaper 4 Moon n Two Dmensons adzyubenko@csub.edu hp://www.csub.edu/~adzyubenko 005, 014 A. Dzyubenko 004 Brooks/Cole 1 Dsplacemen as a Vecor The poson of an objec s descrbed by s poson ecor, r The
More informationOptimal environmental charges under imperfect compliance
ISSN 1 746-7233, England, UK World Journal of Modellng and Smulaon Vol. 4 (28) No. 2, pp. 131-139 Opmal envronmenal charges under mperfec complance Dajn Lu 1, Ya Wang 2 Tazhou Insue of Scence and Technology,
More informationDual Approximate Dynamic Programming for Large Scale Hydro Valleys
Dual Approxmae Dynamc Programmng for Large Scale Hydro Valleys Perre Carpener and Jean-Phlppe Chanceler 1 ENSTA ParsTech and ENPC ParsTech CMM Workshop, January 2016 1 Jon work wh J.-C. Alas, suppored
More informationDEEP UNFOLDING FOR MULTICHANNEL SOURCE SEPARATION SUPPLEMENTARY MATERIAL
DEEP UNFOLDING FOR MULTICHANNEL SOURCE SEPARATION SUPPLEMENTARY MATERIAL Sco Wsdom, John Hershey 2, Jonahan Le Roux 2, and Shnj Waanabe 2 Deparmen o Elecrcal Engneerng, Unversy o Washngon, Seale, WA, USA
More informationTHERMODYNAMICS 1. The First Law and Other Basic Concepts (part 2)
Company LOGO THERMODYNAMICS The Frs Law and Oher Basc Conceps (par ) Deparmen of Chemcal Engneerng, Semarang Sae Unversy Dhon Harano S.T., M.T., M.Sc. Have you ever cooked? Equlbrum Equlbrum (con.) Equlbrum
More informationAn Optimal Control Approach to the Multi-agent Persistent Monitoring Problem
An Opmal Conrol Approach o he Mul-agen Perssen Monorng Problem Chrsos.G. Cassandras, Xuchao Ln and Xu Chu Dng Dvson of Sysems Engneerng and Cener for Informaon and Sysems Engneerng Boson Unversy, cgc@bu.edu,
More informationMachine Learning Linear Regression
Machne Learnng Lnear Regresson Lesson 3 Lnear Regresson Bascs of Regresson Leas Squares esmaon Polynomal Regresson Bass funcons Regresson model Regularzed Regresson Sascal Regresson Mamum Lkelhood (ML)
More informationApproximate Analytic Solution of (2+1) - Dimensional Zakharov-Kuznetsov(Zk) Equations Using Homotopy
Arcle Inernaonal Journal of Modern Mahemacal Scences, 4, (): - Inernaonal Journal of Modern Mahemacal Scences Journal homepage: www.modernscenfcpress.com/journals/jmms.aspx ISSN: 66-86X Florda, USA Approxmae
More informationEpistemic Game Theory: Online Appendix
Epsemc Game Theory: Onlne Appendx Edde Dekel Lucano Pomao Marcano Snscalch July 18, 2014 Prelmnares Fx a fne ype srucure T I, S, T, β I and a probably µ S T. Le T µ I, S, T µ, βµ I be a ype srucure ha
More informationNormal Random Variable and its discriminant functions
Noral Rando Varable and s dscrnan funcons Oulne Noral Rando Varable Properes Dscrnan funcons Why Noral Rando Varables? Analycally racable Works well when observaon coes for a corruped snle prooype 3 The
More informationPanel Data Regression Models
Panel Daa Regresson Models Wha s Panel Daa? () Mulple dmensoned Dmensons, e.g., cross-secon and me node-o-node (c) Pongsa Pornchawseskul, Faculy of Economcs, Chulalongkorn Unversy (c) Pongsa Pornchawseskul,
More information4.2 Continuous-Time Systems and Processes Problem Definition Let the state variable representation of a linear system be
4 COVARIANCE ROAGAION 41 Inrodcion Now ha we have compleed or review of linear sysems and random processes, we wan o eamine he performance of linear sysems ecied by random processes he sandard approach
More informationComb Filters. Comb Filters
The smple flers dscussed so far are characered eher by a sngle passband and/or a sngle sopband There are applcaons where flers wh mulple passbands and sopbands are requred Thecomb fler s an example of
More informationJohn Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany
Herarchcal Markov Normal Mxure models wh Applcaons o Fnancal Asse Reurns Appendx: Proofs of Theorems and Condonal Poseror Dsrbuons John Geweke a and Gann Amsano b a Deparmens of Economcs and Sascs, Unversy
More informationWronskian Determinant Solutions for the (3 + 1)-Dimensional Boiti-Leon-Manna-Pempinelli Equation
Jornal of Appled Mahemacs and Physcs 0 8-4 Pblshed Onlne ovember 0 (hp://www.scrp.org/jornal/jamp) hp://d.do.org/0.46/jamp.0.5004 Wronskan Deermnan Solons for he ( + )-Dmensonal Bo-Leon-Manna-Pempnell
More informationMath 128b Project. Jude Yuen
Mah 8b Proec Jude Yuen . Inroducon Le { Z } be a sequence of observed ndependen vecor varables. If he elemens of Z have a on normal dsrbuon hen { Z } has a mean vecor Z and a varancecovarance marx z. Geomercally
More informationF-Tests and Analysis of Variance (ANOVA) in the Simple Linear Regression Model. 1. Introduction
ECOOMICS 35* -- OTE 9 ECO 35* -- OTE 9 F-Tess and Analyss of Varance (AOVA n he Smple Lnear Regresson Model Inroducon The smple lnear regresson model s gven by he followng populaon regresson equaon, or
More informationChapter Lagrangian Interpolation
Chaper 5.4 agrangan Inerpolaon Afer readng hs chaper you should be able o:. dere agrangan mehod of nerpolaon. sole problems usng agrangan mehod of nerpolaon and. use agrangan nerpolans o fnd deraes and
More informationResearch Article Cubic B-spline for the Numerical Solution of Parabolic Integro-differential Equation with a Weakly Singular Kernel
Researc Jornal of Appled Scences, Engneerng and Tecnology 7(): 65-7, 4 DOI:.96/afs.7.5 ISS: 4-7459; e-iss: 4-7467 4 Mawell Scenfc Pblcaon Corp. Sbmed: Jne 8, Acceped: Jly 9, Pblsed: Marc 5, 4 Researc Arcle
More informationTechnical Appendix to The Equivalence of Wage and Price Staggering in Monetary Business Cycle Models
Techncal Appendx o The Equvalence of Wage and Prce Saggerng n Moneary Busness Cycle Models Rochelle M. Edge Dvson of Research and Sascs Federal Reserve Board Sepember 24, 2 Absrac Ths appendx deals he
More informationOnline Supplement for Dynamic Multi-Technology. Production-Inventory Problem with Emissions Trading
Onlne Supplemen for Dynamc Mul-Technology Producon-Invenory Problem wh Emssons Tradng by We Zhang Zhongsheng Hua Yu Xa and Baofeng Huo Proof of Lemma For any ( qr ) Θ s easy o verfy ha he lnear programmng
More informationOptimal Production Control of Hybrid Manufacturing/Remanufacturing Failure-Prone Systems under Diffusion-Type Demand
Appled Mahemacs, 3, 4, 55-559 hp://dx.do.org/.436/am.3.4379 Pblshed Onlne March 3 (hp://www.scrp.org/jornal/am) Opmal Prodcon Conrol of Hybrd Manfacrng/Remanfacrng Falre-Prone Sysems nder ffson-type emand
More informationFAIPA_SAND: An Interior Point Algorithm for Simultaneous ANalysis and Design Optimization
FAIPA_AN: An Ineror Pon Algorhm for mlaneos ANalyss an esgn Opmzaon osé Hersos*, Palo Mappa* an onel llen** *COPPE / Feeral Unersy of Ro e anero, Mechancal Engneerng Program, Caa Posal 6853, 945 97 Ro
More informationOutline. Probabilistic Model Learning. Probabilistic Model Learning. Probabilistic Model for Time-series Data: Hidden Markov Model
Probablsc Model for Tme-seres Daa: Hdden Markov Model Hrosh Mamsuka Bonformacs Cener Kyoo Unversy Oulne Three Problems for probablsc models n machne learnng. Compung lkelhood 2. Learnng 3. Parsng (predcon
More informationComparison of Differences between Power Means 1
In. Journal of Mah. Analyss, Vol. 7, 203, no., 5-55 Comparson of Dfferences beween Power Means Chang-An Tan, Guanghua Sh and Fe Zuo College of Mahemacs and Informaon Scence Henan Normal Unversy, 453007,
More informationLecture 11 SVM cont
Lecure SVM con. 0 008 Wha we have done so far We have esalshed ha we wan o fnd a lnear decson oundary whose margn s he larges We know how o measure he margn of a lnear decson oundary Tha s: he mnmum geomerc
More information