COMPLEJIDAD COMPUTACIONAL DEL AUTOMATA DE MAYORÍA CON SIGNOS
|
|
- Brian Doyle
- 5 years ago
- Views:
Transcription
1 COMPLEJIDAD COMPUTACIONAL DEL AUTOMATA DE MAYORÍA CON SIGNOS Pedro MONTEALEGRE Univ. Orléans, INSA Cenre Val de Loire, LIFO EA 40, Orléans, France Desafíos maemáicos e informáicos para la consrucción y análisis de redes de regulación biológica CONCEPCIÓN DE ABRIL, 016
2 MAYORÍA BIDIMENSIONAL CON SIGNOS x { 1, 1} n N[v] ={v, v n,v s,v e,v w } (vecindad von Neuman) A =(a u,v ) u,vz, a u,v { 1, 1} (F A (x)) v = 1 P if un(v) a u,vx u > 0, 1 oherwise
3 COMPLEJIDAD COMPUTACIONAL A =(a u,v ) u,vz, a u,v { 1, 1} Predicion( ) A Inpu: A periodic configuraion x and a sie v. Quesion: Does here exiss T>0 such ha (FA T (x)) v =1 Capacidad de simulación de circuios Circuios monóonos planos Circuios planos Circuios monóonos planos reuilizables Coa inferior de la complejidad NC 1 Hard PHard (TuringUniversal) PSPACEHard (InrínsecameneUniversal) 3
4 (a) A wire is consanly blinking (lef op) and ransmis a signal by being consumed (lef boom). A urn (cener lef). An obsrucor prevens a signal (cener). A muliplier (cener righ). These gadges can sraighforwardly be composed o simulae a wiring oolki W. Plus a diode (righ). (b) OR (lef), AND (cener) and FUSIBLE (righ, wih a disinguished cell) gaes. These gaes have a paricular feaure: he four sides are undi ereniaed inpus/oupus. For example, he AND gaes wais for any wo signals o arrive, and hen sends a signal o he wo remaining sides. One can easily ransform hem ino classical gaes using diodes. Mayoría esá en P y es NC 1 Hard 4
5 CASO NO UNIFORME (MOTIVACIÓN?) Exise una mariz simérica (i.e ), al que Predicion( A F A a u,v = a v,u ) es PCompleo (TuringUniversal) (b) AND gae, OR gae and ( wes)and(norh) gadges. Inpus are from norh and wes, and oupus o eas. A F A Exise una mariz para la cual Predicion( ) es PSPACECompleo (InrínsecameneUniversal) 5
6 SI ES UN AUTOMATA A! W A =(C, N, S, E, O) { 1, 1} 5 a b a b W A =(1, 1, 1, 1, 1) 6
7 ( 1, 1, 1, 1, 1) (1, 1, 1, 1, 1) ( 1, 1, 1, 1, 1) (1, 1, 1, 1, 1) ( 1, 1, 1, 1, 1) (1, 1, 1, 1, 1) ( 1, 1, 1, 1, 1) (1, 1, 1, 1, 1) ( 1, 1, 1, 1, 1) F F3 F3 F4 F4 F5 F5 F6 F6 Symmeric Symmeric Symmeric Anisymmeric Anisymmeric Asymmeric Asymmeric Asymmeric Asymmeric 4 SI ES UN AUTOMATA Hay 3 reglas, que se reducen a 1 salvo roación. six Symmeric usmca second line. Figure 7 The six Symmeric usmca are called, from lef o righ in he firs line F1, F, F3 and F 1, F, F 3 in he second line. 4 On he complexiy of planar hreshold dynamics On he complexiy of planar hreshold dynamics Figure 9 The four Asymmeric usmca are called F5, F6 (in he boom line). is equivalen o F1 (resp. F4, resp. F5 ): hey can simulae circu are called, from lef o righ in he firs line F1, F, F3 and gaes he same Figure 8 The Anisymmeric usmca are under called from lef omode. righ: F4 and F 4. Siméricas Anisiméricas Asiméricas Proof. Le : { 1, 1}Z æ { 1, 1}Z bedefined as (x) = x I Lemma. Le F be a symmeric (resp. anisymmeric, usmcahen F 1}Z and for every i œ {1,, 3, 4,resp. 5, 6},asymmeric) any configuraion x œ { 1, NC1Hard, en P,? (en PSPACE) PHard, en PSPACE, Fi = F i. Indeed, noice ha if W is he sign marix of rule Fi Ciclos de largo a lo más Puede ener ciclos Puede ener ciclos of rule F i,9hen W. This implies for every Figure The W four=asymmeric usmca ha are called F5, Fv6 œ (inzhe ÿ ÿ boom line). de largo n superpoly wuv xu = ( wuv )xu Fi (x) = uœn (v) uœn (v) is equivalen o F1 (resp. F4, resp. F5 ): hey can simulae circu and inducively, gaes under he same mode. q q Z Z w (F (x)) = [( 1) = (Fi x (x uv u 1} iæ { 1, (v) 1} uœn (v) wuv as Proof.F Le uœn : { 1, be defined (x) F be a symmeric (resp. anisymmeric, resp. asymmeric) usmcahen q 1 7 Z) (F = ( 1) ( w for every i œ {1,, 3, 4, 5, 6}, any configuraion xuœn œ (v) { 1, 1}uv and Anisymmeric usmca are called from lef o righ: F4 and F 4.
8 Y SI NO ES MAYORÍA Las equivalencias enre casos uniformes siméricos, anisiméricos y asiméricos son válidas. Caso ANDNOT : (F A (x)) v = 1 P if un(v) a u,vx u =5 1 oherwise Exise una mariz Predicion( F A A anisimérica (no uniforme) para la cual ) es PSPACECompleo (InrínsecameneUniversal) 8
Chapter 2. First Order Scalar Equations
Chaper. Firs Order Scalar Equaions We sar our sudy of differenial equaions in he same way he pioneers in his field did. We show paricular echniques o solve paricular ypes of firs order differenial equaions.
More informationLaplace transfom: t-translation rule , Haynes Miller and Jeremy Orloff
Laplace ransfom: -ranslaion rule 8.03, Haynes Miller and Jeremy Orloff Inroducory example Consider he sysem ẋ + 3x = f(, where f is he inpu and x he response. We know is uni impulse response is 0 for
More informationChapter 3 Boundary Value Problem
Chaper 3 Boundary Value Problem A boundary value problem (BVP) is a problem, ypically an ODE or a PDE, which has values assigned on he physical boundary of he domain in which he problem is specified. Le
More informationPENALIZED LEAST SQUARES AND PENALIZED LIKELIHOOD
PENALIZED LEAST SQUARES AND PENALIZED LIKELIHOOD HAN XIAO 1. Penalized Leas Squares Lasso solves he following opimizaion problem, ˆβ lasso = arg max β R p+1 1 N y i β 0 N x ij β j β j (1.1) for some 0.
More informationChallenge Problems. DIS 203 and 210. March 6, (e 2) k. k(k + 2). k=1. f(x) = k(k + 2) = 1 x k
Challenge Problems DIS 03 and 0 March 6, 05 Choose one of he following problems, and work on i in your group. Your goal is o convince me ha your answer is correc. Even if your answer isn compleely correc,
More informationES 250 Practice Final Exam
ES 50 Pracice Final Exam. Given ha v 8 V, a Deermine he values of v o : 0 Ω, v o. V 0 Firs, v o 8. V 0 + 0 Nex, 8 40 40 0 40 0 400 400 ib i 0 40 + 40 + 40 40 40 + + ( ) 480 + 5 + 40 + 8 400 400( 0) 000
More informationImpedance Matching and Tuning
Impedance Maching and Tuning Impedance Maching and Tuning Impedance maching or uning is imporan for he following reasons: Maximum power is delivered Improve he SN of he sysem educe ampliude and phase errors
More informationNotes 04 largely plagiarized by %khc
Noes 04 largely plagiarized by %khc Convoluion Recap Some ricks: x() () =x() x() (, 0 )=x(, 0 ) R ț x() u() = x( )d x() () =ẋ() This hen ells us ha an inegraor has impulse response h() =u(), and ha a differeniaor
More informationHomework 6 AERE331 Spring 2019 Due 4/24(W) Name Sec. 1 / 2
Homework 6 AERE33 Spring 9 Due 4/4(W) Name Sec / PROBLEM (5p In PROBLEM 4 of HW4 we used he frequency domain o design a yaw/rudder feedback conrol sysem for a plan wih ransfer funcion 46 Gp () s The conroller
More informationMath 315: Linear Algebra Solutions to Assignment 6
Mah 35: Linear Algebra s o Assignmen 6 # Which of he following ses of vecors are bases for R 2? {2,, 3, }, {4,, 7, 8}, {,,, 3}, {3, 9, 4, 2}. Explain your answer. To generae he whole R 2, wo linearly independen
More informationAnnouncements: Warm-up Exercise:
Fri Apr 13 7.1 Sysems of differenial equaions - o model muli-componen sysems via comparmenal analysis hp//en.wikipedia.org/wiki/muli-comparmen_model Announcemens Warm-up Exercise Here's a relaively simple
More informationRandom Walk on Circle Imagine a Markov process governing the random motion of a particle on a circular
Random Walk on Circle Imagine a Markov process governing he random moion of a paricle on a circular laice: 1 2 γ γ γ The paricle moves o he righ or lef wih probabiliy γ and says where i is wih probabiliy
More information4. Electric field lines with respect to equipotential surfaces are
Pre-es Quasi-saic elecromagneism. The field produced by primary charge Q and by an uncharged conducing plane disanced from Q by disance d is equal o he field produced wihou conducing plane by wo following
More informationIntroduction to Probability and Statistics Slides 4 Chapter 4
Inroducion o Probabiliy and Saisics Slides 4 Chaper 4 Ammar M. Sarhan, asarhan@mahsa.dal.ca Deparmen of Mahemaics and Saisics, Dalhousie Universiy Fall Semeser 8 Dr. Ammar Sarhan Chaper 4 Coninuous Random
More informationMath Week 14 April 16-20: sections first order systems of linear differential equations; 7.4 mass-spring systems.
Mah 2250-004 Week 4 April 6-20 secions 7.-7.3 firs order sysems of linear differenial equaions; 7.4 mass-spring sysems. Mon Apr 6 7.-7.2 Sysems of differenial equaions (7.), and he vecor Calculus we need
More informationTurbulence in Fluids. Plumes and Thermals. Benoit Cushman-Roisin Thayer School of Engineering Dartmouth College
Turbulence in Fluids Plumes and Thermals enoi Cushman-Roisin Thayer School of Engineering Darmouh College Why do hese srucures behave he way hey do? How much mixing do hey accomplish? 1 Plumes Plumes are
More informationLecture 20: Riccati Equations and Least Squares Feedback Control
34-5 LINEAR SYSTEMS Lecure : Riccai Equaions and Leas Squares Feedback Conrol 5.6.4 Sae Feedback via Riccai Equaions A recursive approach in generaing he marix-valued funcion W ( ) equaion for i for he
More informatione 2t u(t) e 2t u(t) =?
EE : Signals, Sysems, and Transforms Fall 7. Skech he convoluion of he following wo signals. Tes No noes, closed book. f() Show your work. Simplify your answers. g(). Using he convoluion inegral, find
More informationSOLUTIONS TO ECE 3084
SOLUTIONS TO ECE 384 PROBLEM 2.. For each sysem below, specify wheher or no i is: (i) memoryless; (ii) causal; (iii) inverible; (iv) linear; (v) ime invarian; Explain your reasoning. If he propery is no
More informationFinish reading Chapter 2 of Spivak, rereading earlier sections as necessary. handout and fill in some missing details!
MAT 257, Handou 6: Ocober 7-2, 20. I. Assignmen. Finish reading Chaper 2 of Spiva, rereading earlier secions as necessary. handou and fill in some missing deails! II. Higher derivaives. Also, read his
More informationCSE-4303/CSE-5365 Computer Graphics Fall 1996 Take home Test
Comper Graphics roblem #1) A bi-cbic parameric srface is defined by Hermie geomery in he direcion of parameer. In he direcion, he geomery ecor is defined by a poin @0, a poin @0.5, a angen ecor @1 and
More informationProblem set 6: Solutions Math 207A, Fall x 0 2 x
Problem se 6: Soluions Mah 7A, Fall 14 1 Skech phase planes of he following linear ssems: 4 a = ; 9 4 b = ; 9 1 c = ; 1 d = ; 4 e = ; f = 1 3 In each case, classif he equilibrium, =, as a saddle poin,
More informationTHE GENERALIZED PASCAL MATRIX VIA THE GENERALIZED FIBONACCI MATRIX AND THE GENERALIZED PELL MATRIX
J Korean Mah Soc 45 008, No, pp 479 49 THE GENERALIZED PASCAL MATRIX VIA THE GENERALIZED FIBONACCI MATRIX AND THE GENERALIZED PELL MATRIX Gwang-yeon Lee and Seong-Hoon Cho Reprined from he Journal of he
More informationODEs II, Lecture 1: Homogeneous Linear Systems - I. Mike Raugh 1. March 8, 2004
ODEs II, Lecure : Homogeneous Linear Sysems - I Mike Raugh March 8, 4 Inroducion. In he firs lecure we discussed a sysem of linear ODEs for modeling he excreion of lead from he human body, saw how o ransform
More informationDesigning Information Devices and Systems I Spring 2019 Lecture Notes Note 17
EES 16A Designing Informaion Devices and Sysems I Spring 019 Lecure Noes Noe 17 17.1 apaciive ouchscreen In he las noe, we saw ha a capacior consiss of wo pieces on conducive maerial separaed by a nonconducive
More information6.003 Homework #13 Solutions
6.003 Homework #3 Soluions Problems. Transformaion Consider he following ransformaion from x() o y(): x() w () w () w 3 () + y() p() cos() where p() = δ( k). Deermine an expression for y() when x() = sin(/)/().
More informationPosition, Velocity, and Acceleration
rev 06/2017 Posiion, Velociy, and Acceleraion Equipmen Qy Equipmen Par Number 1 Dynamic Track ME-9493 1 Car ME-9454 1 Fan Accessory ME-9491 1 Moion Sensor II CI-6742A 1 Track Barrier Purpose The purpose
More information6.003 Homework #9 Solutions
6.003 Homework #9 Soluions Problems. Fourier varieies a. Deermine he Fourier series coefficiens of he following signal, which is periodic in 0. x () 0 3 0 a 0 5 a k a k 0 πk j3 e 0 e j πk 0 jπk πk e 0
More informationOnline Convex Optimization Example And Follow-The-Leader
CSE599s, Spring 2014, Online Learning Lecure 2-04/03/2014 Online Convex Opimizaion Example And Follow-The-Leader Lecurer: Brendan McMahan Scribe: Sephen Joe Jonany 1 Review of Online Convex Opimizaion
More informationPCP Theorem by Gap Amplification
PCP Theorem by Gap Amplificaion Bernhard Vesenmayer JASS 2006 Absrac The PCP Theorem provides a new classificaion of NP. Since he original proof by [AS98], several new proofs occured. While he firs proof
More informationFeatures / Advantages: Applications: Package: Y4
IGBT (NPT) Module CES = x 1 I C = 9 =. CE(sa) Phase leg Par number MII7-13 1 Backside: isolaed 7 3 Feaures / dvanages: pplicaions: Package: Y NPT IGBT echnology low sauraion volage low swiching losses
More information10. State Space Methods
. Sae Space Mehods. Inroducion Sae space modelling was briefly inroduced in chaper. Here more coverage is provided of sae space mehods before some of heir uses in conrol sysem design are covered in he
More informationDelhi Noida Bhopal Hyderabad Jaipur Lucknow Indore Pune Bhubaneswar Kolkata Patna Web: Ph:
Serial : 0. ND_NW_EE_Signal & Sysems_4068 Delhi Noida Bhopal Hyderabad Jaipur Lucknow Indore Pune Bhubaneswar Kolkaa Pana Web: E-mail: info@madeeasy.in Ph: 0-4546 CLASS TEST 08-9 ELECTRICAL ENGINEERING
More informationSolutions from Chapter 9.1 and 9.2
Soluions from Chaper 9 and 92 Secion 9 Problem # This basically boils down o an exercise in he chain rule from calculus We are looking for soluions of he form: u( x) = f( k x c) where k x R 3 and k is
More informationMath 10B: Mock Mid II. April 13, 2016
Name: Soluions Mah 10B: Mock Mid II April 13, 016 1. ( poins) Sae, wih jusificaion, wheher he following saemens are rue or false. (a) If a 3 3 marix A saisfies A 3 A = 0, hen i canno be inverible. True.
More informationEchocardiography Project and Finite Fourier Series
Echocardiography Projec and Finie Fourier Series 1 U M An echocardiagram is a plo of how a porion of he hear moves as he funcion of ime over he one or more hearbea cycles If he hearbea repeas iself every
More informationSolutions to the Olympiad Cayley Paper
Soluions o he Olympiad Cayley Paper C1. How many hree-digi muliples of 9 consis only of odd digis? Soluion We use he fac ha an ineger is a muliple of 9 when he sum of is digis is a muliple of 9, and no
More informationTHE MATRIX-TREE THEOREM
THE MATRIX-TREE THEOREM 1 The Marix-Tree Theorem. The Marix-Tree Theorem is a formula for he number of spanning rees of a graph in erms of he deerminan of a cerain marix. We begin wih he necessary graph-heoreical
More information6.003 Homework #9 Solutions
6.00 Homework #9 Soluions Problems. Fourier varieies a. Deermine he Fourier series coefficiens of he following signal, which is periodic in 0. x () 0 0 a 0 5 a k sin πk 5 sin πk 5 πk for k 0 a k 0 πk j
More informationChapter 8 The Complete Response of RL and RC Circuits
Chaper 8 The Complee Response of RL and RC Circuis Seoul Naional Universiy Deparmen of Elecrical and Compuer Engineering Wha is Firs Order Circuis? Circuis ha conain only one inducor or only one capacior
More information1. Kinematics I: Position and Velocity
1. Kinemaics I: Posiion and Velociy Inroducion The purpose of his eperimen is o undersand and describe moion. We describe he moion of an objec by specifying is posiion, velociy, and acceleraion. In his
More informationLecture 4 Notes (Little s Theorem)
Lecure 4 Noes (Lile s Theorem) This lecure concerns one of he mos imporan (and simples) heorems in Queuing Theory, Lile s Theorem. More informaion can be found in he course book, Bersekas & Gallagher,
More informationApproximation Algorithms for Unique Games via Orthogonal Separators
Approximaion Algorihms for Unique Games via Orhogonal Separaors Lecure noes by Konsanin Makarychev. Lecure noes are based on he papers [CMM06a, CMM06b, LM4]. Unique Games In hese lecure noes, we define
More informationSignals and Systems Profs. Byron Yu and Pulkit Grover Fall Midterm 1 Solutions
8-90 Signals and Sysems Profs. Byron Yu and Pulki Grover Fall 07 Miderm Soluions Name: Andrew ID: Problem Score Max 0 8 4 6 5 0 6 0 7 8 9 0 6 Toal 00 Miderm Soluions. (0 poins) Deermine wheher he following
More informationSection 5: Chain Rule
Chaper The Derivaive Applie Calculus 11 Secion 5: Chain Rule There is one more ype of complicae funcion ha we will wan o know how o iffereniae: composiion. The Chain Rule will le us fin he erivaive of
More informationV The Fourier Transform
V he Fourier ransform Lecure noes by Assaf al 1. Moivaion Imagine playing hree noes on he piano, recording hem (soring hem as a.wav or.mp3 file), and hen ploing he resuling waveform on he compuer: 100Hz
More informationA note on inertial motion
Atmósfera (24) 183-19 A note on inertial motion A. WIIN-NIELSEN The Collstrop Foundation, H. C. Andersens Blvd. 37, 5th, DK 1553, Copenhagen V, Denmark Received January 13, 23; accepted January 1, 24 RESUMEN
More informationSTA 114: Statistics. Notes 2. Statistical Models and the Likelihood Function
STA 114: Saisics Noes 2. Saisical Models and he Likelihood Funcion Describing Daa & Saisical Models A physicis has a heory ha makes a precise predicion of wha s o be observed in daa. If he daa doesn mach
More informationConverter - Brake - Inverter Module (CBI2)
Converer - Brake - Inverer Module (CBI2) 21 22 D11 D13 D15 1 2 3 7 D7 16 15 T1 D1 T3 D3 T5 D5 18 6 17 5 19 4 D12 D14 D16 T7 T2 D2 T4 D4 T6 D6 23 14 24 11 12 13 NTC 8 9 Three Phase Brake Chopper Three Phase
More informationLecture Outline. Introduction Transmission Line Equations Transmission Line Wave Equations 8/10/2018. EE 4347 Applied Electromagnetics.
8/10/018 Course Insrucor Dr. Raymond C. Rumpf Office: A 337 Phone: (915) 747 6958 E Mail: rcrumpf@uep.edu EE 4347 Applied Elecromagneics Topic 4a Transmission Line Equaions Transmission These Line noes
More informationPI74STX1G126. SOTiny Gate STX Buffer with 3-State Output. Features. Descriptio n. Block Diagram. Pin Configuration
PI74STXG6 4567890456789045678904567890456789045678904567890456789045678904567890456789045678904567890 4567890456789045678904567890456789045678904567890456789045678904567890456789045678904567890 SOTiny
More informationBasic Principles of Sinusoidal Oscillators
Basic Principles of Sinusoidal Oscillaors Linear oscillaor Linear region of circui : linear oscillaion Nonlinear region of circui : ampliudes sabilizaion Barkhausen crierion X S Amplifier A X O X f Frequency-selecive
More informationLecture 15: Differential Pairs (Part 2)
Lecure 5: ifferenial Pairs (Par ) Gu-Yeon Wei ivision of Enineerin and Applied Sciences Harvard Universiy uyeon@eecs.harvard.edu Wei Overview eadin S&S: Chaper 6.6 Suppleenal eadin S&S: Chaper 6.9 azavi,
More informationm j 2 m j t + +D t u j t
Lemma : Le m,, m be posiive, pairwise disinc, real numbers, and le j,, j Z, j < j 2
More informationConverter - Brake - Inverter Module (CBI2)
Converer - Brake - Inverer Module (CBI2) 21 22 D11 D13 D1 1 2 3 7 D7 16 1 T1 D1 T3 D3 T D 18 2 6 17 19 4 D12 D14 D16 T7 T2 D2 T4 D4 T6 D6 23 14 24 11 1 12 13 NTC 8 9 Three Phase Brake Chopper Three Phase
More informationp h a s e - o u t Dual Power MOSFET Module VMM X2 V DSS = 75 V I D25 = 1560 A R DS(on) = 0.38 mω Phaseleg Configuration
MM 5-75X Dual Power MOSFET Module S = 75 5 = 5 =. mω Phaseleg Configuraion Gae Conrol Pi 9 Power Screw Terminals 9 MOSFET T + T Symbol Condiio Maximum Raings S = 5 C o 5 C 75 ± 5 T C = 5 C j 5 T C = C
More informationConverter - Brake - Inverter Module (CBI2)
MUBW 5-6 7 Converer - Brake - Inverer Module (CBI2) 2 22 D D3 D5 2 3 7 D7 6 5 T D T3 D3 T5 D5 8 2 6 7 5 9 4 D2 D4 D6 T7 T2 D2 T4 D4 T6 D6 23 4 24 2 3 NTC 8 9 Three Phase Brake Chopper Three Phase Recifier
More informationTopic Astable Circuits. Recall that an astable circuit has two unstable states;
Topic 2.2. Asable Circuis. Learning Objecives: A he end o his opic you will be able o; Recall ha an asable circui has wo unsable saes; Explain he operaion o a circui based on a Schmi inverer, and esimae
More information28. Narrowband Noise Representation
Narrowband Noise Represenaion on Mac 8. Narrowband Noise Represenaion In mos communicaion sysems, we are ofen dealing wih band-pass filering of signals. Wideband noise will be shaped ino bandlimied noise.
More informationk 1 k 2 x (1) x 2 = k 1 x 1 = k 2 k 1 +k 2 x (2) x k series x (3) k 2 x 2 = k 1 k 2 = k 1+k 2 = 1 k k 2 k series
Final Review A Puzzle... Consider wo massless springs wih spring consans k 1 and k and he same equilibrium lengh. 1. If hese springs ac on a mass m in parallel, hey would be equivalen o a single spring
More informationLectures 29 and 30 BIQUADRATICS AND STATE SPACE OP AMP REALIZATIONS. I. Introduction
EE-202/445, 3/18/18 9-1 R. A. DeCarlo Lecures 29 and 30 BIQUADRATICS AND STATE SPACE OP AMP REALIZATIONS I. Inroducion 1. The biquadraic ransfer funcion has boh a 2nd order numeraor and a 2nd order denominaor:
More informationLars Nesheim. 17 January Last lecture solved the consumer choice problem.
Lecure 4 Locaional Equilibrium Coninued Lars Nesheim 17 January 28 1 Inroducory remarks Las lecure solved he consumer choice problem. Compued condiional demand funcions: C (I x; p; r (x)) and x; p; r (x))
More informationL1, L2, N1 N2. + Vout. C out. Figure 2.1.1: Flyback converter
page 11 Flyback converer The Flyback converer belongs o he primary swiched converer family, which means here is isolaion beween in and oupu. Flyback converers are used in nearly all mains supplied elecronic
More informationMath 334 Fall 2011 Homework 11 Solutions
Dec. 2, 2 Mah 334 Fall 2 Homework Soluions Basic Problem. Transform he following iniial value problem ino an iniial value problem for a sysem: u + p()u + q() u g(), u() u, u () v. () Soluion. Le v u. Then
More informationMathcad Lecture #8 In-class Worksheet Curve Fitting and Interpolation
Mahcad Lecure #8 In-class Workshee Curve Fiing and Inerpolaion A he end of his lecure, you will be able o: explain he difference beween curve fiing and inerpolaion decide wheher curve fiing or inerpolaion
More informationKINEMATICS IN ONE DIMENSION
KINEMATICS IN ONE DIMENSION PREVIEW Kinemaics is he sudy of how hings move how far (disance and displacemen), how fas (speed and velociy), and how fas ha how fas changes (acceleraion). We say ha an objec
More informationt is a basis for the solution space to this system, then the matrix having these solutions as columns, t x 1 t, x 2 t,... x n t x 2 t...
Mah 228- Fri Mar 24 5.6 Marix exponenials and linear sysems: The analogy beween firs order sysems of linear differenial equaions (Chaper 5) and scalar linear differenial equaions (Chaper ) is much sronger
More informationES.1803 Topic 22 Notes Jeremy Orloff
ES.83 Topic Noes Jeremy Orloff Fourier series inroducion: coninued. Goals. Be able o compue he Fourier coefficiens of even or odd periodic funcion using he simplified formulas.. Be able o wrie and graph
More informationSection 3.5 Nonhomogeneous Equations; Method of Undetermined Coefficients
Secion 3.5 Nonhomogeneous Equaions; Mehod of Undeermined Coefficiens Key Terms/Ideas: Linear Differenial operaor Nonlinear operaor Second order homogeneous DE Second order nonhomogeneous DE Soluion o homogeneous
More informationMachine Learning 4771
ony Jebara, Columbia Universiy achine Learning 4771 Insrucor: ony Jebara ony Jebara, Columbia Universiy opic 20 Hs wih Evidence H Collec H Evaluae H Disribue H Decode H Parameer Learning via JA & E ony
More informationCSE 5365 Computer Graphics. Take Home Test #1
CSE 5365 Comper Graphics Take Home Tes #1 Fall/1996 Tae-Hoon Kim roblem #1) A bi-cbic parameric srface is defined by Hermie geomery in he direcion of parameer. In he direcion, he geomery ecor is defined
More informationLecture 10: The Poincaré Inequality in Euclidean space
Deparmens of Mahemaics Monana Sae Universiy Fall 215 Prof. Kevin Wildrick n inroducion o non-smooh analysis and geomery Lecure 1: The Poincaré Inequaliy in Euclidean space 1. Wha is he Poincaré inequaliy?
More informationExample on p. 157
Example 2.5.3. Le where BV [, 1] = Example 2.5.3. on p. 157 { g : [, 1] C g() =, g() = g( + ) [, 1), var (g) = sup g( j+1 ) g( j ) he supremum is aken over all he pariions of [, 1] (1) : = < 1 < < n =
More informationGuest Lectures for Dr. MacFarlane s EE3350 Part Deux
Gues Lecures for Dr. MacFarlane s EE3350 Par Deux Michael Plane Mon., 08-30-2010 Wrie name in corner. Poin ou his is a review, so I will go faser. Remind hem o go lisen o online lecure abou geing an A
More informationNon-parametric techniques. Instance Based Learning. NN Decision Boundaries. Nearest Neighbor Algorithm. Distance metric important
on-parameric echniques Insance Based Learning AKA: neares neighbor mehods, non-parameric, lazy, memorybased, or case-based learning Copyrigh 2005 by David Helmbold 1 Do no fi a model (as do LDA, logisic
More informationCoolMOS 1) Power MOSFET with Series Schottky Diode and Ultra Fast Antiparallel Diode
IXKF 4N6SCD1 CoolMOS 1) Power MOSFET wih Series Schoky Diode and Ulra Fas niparallel Diode in High olage ISOPLUS i4-pc S = 6 2 = 41 yp. = 6 mω rr = 7 ISOPLUS i4-pc Preliminary daa 1 D S T D F 1 2 E72873
More informationApplication of a Stochastic-Fuzzy Approach to Modeling Optimal Discrete Time Dynamical Systems by Using Large Scale Data Processing
Applicaion of a Sochasic-Fuzzy Approach o Modeling Opimal Discree Time Dynamical Sysems by Using Large Scale Daa Processing AA WALASZE-BABISZEWSA Deparmen of Compuer Engineering Opole Universiy of Technology
More informationModel Reduction for Dynamical Systems Lecture 6
Oo-von-Guericke Universiä Magdeburg Faculy of Mahemaics Summer erm 07 Model Reducion for Dynamical Sysems ecure 6 v eer enner and ihong Feng Max lanck Insiue for Dynamics of Complex echnical Sysems Compuaional
More information3.1 More on model selection
3. More on Model selecion 3. Comparing models AIC, BIC, Adjused R squared. 3. Over Fiing problem. 3.3 Sample spliing. 3. More on model selecion crieria Ofen afer model fiing you are lef wih a handful of
More informationLearning Objectives: Practice designing and simulating digital circuits including flip flops Experience state machine design procedure
Lab 4: Synchronous Sae Machine Design Summary: Design and implemen synchronous sae machine circuis and es hem wih simulaions in Cadence Viruoso. Learning Objecives: Pracice designing and simulaing digial
More information( ) ( ) if t = t. It must satisfy the identity. So, bulkiness of the unit impulse (hyper)function is equal to 1. The defining characteristic is
UNIT IMPULSE RESPONSE, UNIT STEP RESPONSE, STABILITY. Uni impulse funcion (Dirac dela funcion, dela funcion) rigorously defined is no sricly a funcion, bu disribuion (or measure), precise reamen requires
More information2.160 System Identification, Estimation, and Learning. Lecture Notes No. 8. March 6, 2006
2.160 Sysem Idenificaion, Esimaion, and Learning Lecure Noes No. 8 March 6, 2006 4.9 Eended Kalman Filer In many pracical problems, he process dynamics are nonlinear. w Process Dynamics v y u Model (Linearized)
More informationOnline Appendix for "Customer Recognition in. Experience versus Inspection Good Markets"
Online Appendix for "Cusomer Recogniion in Experience versus Inspecion Good Markes" Bing Jing Cheong Kong Graduae School of Business Beijing, 0078, People s Republic of China, bjing@ckgsbeducn November
More informationMASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Physics Spring Experiment 9: Faraday s Law of Induction
MASSACHUSETTS INSTITUTE OF TECHNOLOY Deparmen of Physics 8.02 Spring 2005 OBJECTIVES Experimen 9: Faraday s Law of Inducion 1. To become familiar wih he conceps of changing magneic flux and induced curren
More informationPHYSICS Solving Equations
Sepember 20, 2013 PHYSIS Solving Equaion Sepember 2013 www.njcl.org Solving for a Variable Our goal i o be able o olve any equaion for any variable ha appear in i. Le' look a a imple equaion fir. The variable
More informationThe average rate of change between two points on a function is d t
SM Dae: Secion: Objecive: The average rae of change beween wo poins on a funcion is d. For example, if he funcion ( ) represens he disance in miles ha a car has raveled afer hours, hen finding he slope
More informationIntroduction to AC Power, RMS RMS. ECE 2210 AC Power p1. Use RMS in power calculations. AC Power P =? DC Power P =. V I = R =. I 2 R. V p.
ECE MS I DC Power P I = Inroducion o AC Power, MS I AC Power P =? A Solp //9, // // correced p4 '4 v( ) = p cos( ω ) v( ) p( ) Couldn' we define an "effecive" volage ha would allow us o use he same relaionships
More informationMatrix Versions of Some Refinements of the Arithmetic-Geometric Mean Inequality
Marix Versions of Some Refinemens of he Arihmeic-Geomeric Mean Inequaliy Bao Qi Feng and Andrew Tonge Absrac. We esablish marix versions of refinemens due o Alzer ], Carwrigh and Field 4], and Mercer 5]
More informationF This leads to an unstable mode which is not observable at the output thus cannot be controlled by feeding back.
Lecure 8 Las ime: Semi-free configuraion design This is equivalen o: Noe ns, ener he sysem a he same place. is fixed. We design C (and perhaps B. We mus sabilize if i is given as unsable. Cs ( H( s = +
More informationNon-parametric techniques. Instance Based Learning. NN Decision Boundaries. Nearest Neighbor Algorithm. Distance metric important
on-parameric echniques Insance Based Learning AKA: neares neighbor mehods, non-parameric, lazy, memorybased, or case-based learning Copyrigh 2005 by David Helmbold 1 Do no fi a model (as do LTU, decision
More informationGeneralized Snell envelope and BSDE With Two general Reflecting Barriers
1/22 Generalized Snell envelope and BSDE Wih Two general Reflecing Barriers EL HASSAN ESSAKY Cadi ayyad Universiy Poly-disciplinary Faculy Safi Work in progress wih : M. Hassani and Y. Ouknine Iasi, July
More informationNetwork Flows: Introduction & Maximum Flow
CSC 373 - lgorihm Deign, nalyi, and Complexiy Summer 2016 Lalla Mouaadid Nework Flow: Inroducion & Maximum Flow We now urn our aenion o anoher powerful algorihmic echnique: Local Search. In a local earch
More informationLecture 10: Wave equation, solution by spherical means
Lecure : Wave equaion, soluion by spherical means Physical modeling eample: Elasodynamics u (; ) displacemen vecor in elasic body occupying a domain U R n, U, The posiion of he maerial poin siing a U in
More informationd 1 = c 1 b 2 - b 1 c 2 d 2 = c 1 b 3 - b 1 c 3
and d = c b - b c c d = c b - b c c This process is coninued unil he nh row has been compleed. The complee array of coefficiens is riangular. Noe ha in developing he array an enire row may be divided or
More informationAnalysis and design of a high-efficiency zero-voltage-switching step-up DC DC converter
Sādhanā Vol. 38, Par 4, Augus 2013, pp. 653 665. c Indian Academy of Sciences Analysis and design of a high-efficiency zero-volage-swiching sep-up DC DC converer JAE-WON YANG and HYUN-LARK DO Deparmen
More informationThe problem with linear regulators
he problem wih linear regulaors i in P in = i in V REF R a i ref i q i C v CE P o = i o i B ie P = v i o o in R 1 R 2 i o i f η = P o P in iref is small ( 0). iq (quiescen curren) is small (probably).
More informationMath Week 15: Section 7.4, mass-spring systems. These are notes for Monday. There will also be course review notes for Tuesday, posted later.
Mah 50-004 Week 5: Secion 7.4, mass-spring sysems. These are noes for Monday. There will also be course review noes for Tuesday, posed laer. Mon Apr 3 7.4 mass-spring sysems. Announcemens: Warm up exercise:
More informationTwo Coupled Oscillators / Normal Modes
Lecure 3 Phys 3750 Two Coupled Oscillaors / Normal Modes Overview and Moivaion: Today we ake a small, bu significan, sep owards wave moion. We will no ye observe waves, bu his sep is imporan in is own
More informationChapter Three Systems of Linear Differential Equations
Chaper Three Sysems of Linear Differenial Equaions In his chaper we are going o consier sysems of firs orer orinary ifferenial equaions. These are sysems of he form x a x a x a n x n x a x a x a n x n
More information