ECE 421/599 Electric Energy Systems 7 Optimal Dispatch of Generation. Instructor: Kai Sun Fall 2014

Size: px
Start display at page:

Download "ECE 421/599 Electric Energy Systems 7 Optimal Dispatch of Generation. Instructor: Kai Sun Fall 2014"

Transcription

1 ECE 4/599 Electrc Eergy Systems 7 Optmal Dspatch of Geerato Istructor: Ka Su Fall 04

2 Backgroud I a practcal power system, the costs of geeratg ad delverg electrcty from power plats are dfferet (due to fuel costs ad dstaces to load ceters) Uder ormal codtos, the system geerato capacty s more tha the total load demad ad losses. Thus, there s room to schedule geerato wth capacty lmts Mmzg a cost fucto that represets, e.g. Operatg costs Trasmsso losses System relablty mpacts Ths s called Optmal ower Flow (OF) problem A typcal problem s the Ecoomc Dspatch (ED) of real power geerato

3 Itroducto of Nolear Fucto Optmzato Ucostraed parameter optmzato Costraed parameter optmzato Equalty costrats Iequalty costrats

4 Ucostraed parameter optmzato Mmze cost fucto f x x x (,,, ). Solve all local mma satsfyg two codtos (ecessary & suffcet) Codto-: Gradet vector f f f f (,,, ) 0 x x x Codto-: Hessa matrx H s postve defte Statoary pot (where f s flat all drectos) Local mmum (a pure source f vector feld). Fd the global mmum from all local mma 4

5 f(x,y) (cos x + cos y) 5

6 Mmze f(x, y)x +y y f f f (, ) ( x, y) 0 x y f x 0, y 0 H f f x xy 0 f 0 yx y 0 x 6

7 arameter Optmzato wth Equalty Costrats Mmze Subject to f x x x (,,, ) g ( x, x,, x ) 0 k k,,, K Itroduce Lagrage Multplers λ ~ λ K K L f λ g + Necessary codtos for the local mma of L (also ecessary for the orgal problem) k k L f g + x x x K λk k 0 L λ k g k 0 7

8 Mmze f(x, y)x +y Subject to (x-8) +(y-6) 5 g(x, y)(x-8) +(y-6) -50 y L x + y + λ[( x 8) + ( y 6) 5] f L x + λ(x 6) 0 x L y + λ(y ) 0 y L + λ ( x 8) ( y 6) x Solutos (from the N-R method): λ, x4 ad y (f5) λ, x ad y9 (f5) 8

9 arameter Optmzato wth Iequalty Costrats Mmze Subject to : f x x x (,,, ) g ( x, x,, x ) 0 k k,,, K Itroduce Lagrage Multplers λ ~ λ K ad µ ~ µ m u ( x, x,, x ) 0 j,,, m j K L f + λ g + µ u k k j j k j m Necessary codtos for the local mma of L L f g u + + x x x x K m k j λ k µ j j 0,, Kuh-Tucker (KKT) ecessary codto L λ k g k 0 L u j 0 µ j µ u 0 µ 0 j j j k,,, K j,, m 9

10 Mmze f(x, y)x +y Subject to (x-8) +(y-6) 5 g(x, y)(x-8) +(y-6) -50 x+ y uxy (, ) x y 0 L x y x y x y + + λ [( 8) + ( 6) 5] + µ ( ) L x + λ (x 6) µ 0 x L y + λ ( y ) µ 0 y L ( x 8) + ( y 6) 5 0 λ L L x y < 0 µ 0 0, µ µ L µ ju j 0, µ j 0 or x y 0 µ > 0 µ y f x Solutos: µ0, λ, x ad y9 (f5) µ5.6, λ-0., x5 ad y (f9) µ, λ-.8, x ad y6 (f) 0

11 Operatg Cost of a Thermal lat Fuel-cost curve of a geerator (represeted by a quadratc fucto of real power) C α + β + γ Icremetal fuel-cost curve: dc λ γ + β d

12 A real case FUELCO MAX MIN HEMIN X Y X Y X Y Ge ID RIOR ($/MBtu) (MW) (WM) (MBtu/hr) (MW) (Btu/kWh) (MW) (Btu/kWh) (Btu/kWh) (Btu/kWh) A B Btu/h X Y 000 $/h Btu/h $/MBtu / 000,000 $/MWh Y /000 $/MBtu Cost ($/h) (MW) Lambda ($/MWh) (MW)

13 ED Neglectg Losses ad No Geerator Lmts If trasmsso le losses are eglected, mmze the total producto cost: C t g C α + β + γ subject to g Apply the Lagrage multpler method ( g + ukows to solve): g L Ct + λ( D ) L 0 L 0 λ Ct g D λ 0 D g Ct All plats must operate at equal cremetal cost dc β + γ λ d λ β γ D λ D + g β g γ γ λ β γ,, g Solve

14 Example 7.4 The fuel-cost fuctos for three thermal plats are C ~C $/h., ad are MW. D 800MW. Neglectg le losses ad geerator lmts, fd the optmal dspatch ad the total cost $/h λ D + g λ β γ β g γ γ $ / MWh dc λ d C C C dc λ d (0.004) (0.006) (0.009) dc λ d Equal cremetal cost λ C t $ / h 4

15 Solvg λ by the N-R Method g λ β γ g D λ g ( ) For a geeral case: g f( λ) ( k) df ( λ) ( k) ( k) f( λ) + ( ) λ dλ D D λ λ + λ ( k+ ) ( k) ( k) λ df ( λ) df ( λ) d ( ) ( ) ( ) dλ dλ dλ D D f( λ) ( k) ( k) g utl ( k+ ) ( k) λ λ ε 5

16 Apply the R-N Method Example 7.4 λ () λ 6.0 () () () γ β (0.004) (0.006) (0.009) 800 ( ) () () λ + + (0.004) (0.006) (0.009) ( k) ( k) λ d ( ) dλ γ $/MWh (MW) () λ + () () () (0.004) (0.006) (0.009) () + + C t ( ) $ / h 6

17 ED Neglectg Losses but Icludg Geerator Lmts Cosderg the maxmum (by ratg) ad mmum (for stablty) geerato lmts, Mmze Subject to C t g C g α + β + γ D (m) (max),,,, g g g L C + λ ( ) + [ µ ( ) + γ ( )] t D (max) (m) L 0 L 0 λ L 0 µ L γ 0 C 0 λ + µ γ g µ ( ) 0, µ 0 D (max) γ ( ) 0 γ 0 (m) (m) (max),,,, g (m) (max) ( µ γ 0) ( 0) (max) γ ( 0) (m) µ 4 dc d dc d dc d λ λ µ λ λ+ γ λ Excludg the plats that reach ther lmts, the other plats stll operate at equal cremetal cost 7

18 Example 7.6 Cosder geerator lmts ( MW) for Example 7.4, let D 975MW dc d λ D 975MW dc d λ D 906MW D 800MW dc d λ Soluto: 450MW 5MW 00MW λ9.4$/mwh C t 86.5 $/h 8

19 λ γ β ( k) ( k) λ d ( ) dλ γ () λ 6.0 () () () (0.004) (0.006) (0.009) () ( ) 84.7 () λ (0.004) (0.006) (0.009) () λ + () () () (0.004) (0.006) (0.009) () + + >, so let 450 (max) 975 ( ) () λ (0.006) (0.009) () λ + () () () (0.006) (0.009) () ( )

20 Solve the optmal dspatch for D 550MW C C C dc d λ or 0 or 0 or 0 Ut Commtmet roblem (mxed-teger optmzato) 0 dc d λ Soluto : 80MW Soluto : 40MW 0 dc d λ 70MW 00MW λ7.54$/mwh C t 4676 $/h 0MW 0MW λ8.0$/mwh C t 4584 $/h 0 Soluto : Soluto 4: 0MW 400MW 40MW 0MW 0MW 50MW λ9.58$/mwh λ8.5$/mwh C t $/h C t 45 $/h 0

21 Trasmsso Loss Whe trasmsso dstaces are very small ad load desty the system s very hgh Trasmsso losses may be eglected All plats operate at equal cremetal producto cost to acheve optmal dspatch of geerato However, a large tercoected etwork ower s trasmtted over log dstaces wth low load desty areas Trasmsso losses are a major factor affectg the optmal dspatch.

22 Calculato of Trasmsso Losses I V cosφ L I R R V cosφ R V cos φ V R+jX I D L B V V I I I R + I R + I + I R L R + V cos cos φ V φ cosα cos β + + V cos φ V cosφ R R R +jx R +jx I R +jx D I β L I + + α B B B I

23 More geeral loss formulas: Quadratc form Kro s loss formula L L g j g g g g B j j B B B j j 0 00 j B j, B 0 ad B 00 are Loss coeffcets or B-coeffcets. See Chapter 7.7 for detals of calculatg B-coeffcets Chages wth power flows but usually assumed costat ad estmated for a power-flow base case

24 Ecoomc Dspatch Icludg Losses Cost fucto g g t C C dc d β + γ Icremetal fuel cost Subject to g L D L g g g B B B j j 0 00 j g L B B j j j 0 Icremetal trasmsso loss,, (m) (max) g 4

25 Usg Lagrage Multplers g g g g L C ( ) ( ) ( ) where D L (max) (max) (m) (m) g g g B B B L j j 0 00 j g g t C C L 0 λ L 0 dc d g g g g D L D j j 0 j L dc + λ(0 + ) 0 d B B B L + λ λ 00 L dc d ealty factor: L L,, g dc d β + γ g L B B j j j 0 g BB,, j j j 0 g 5

26 BB j j 0 j g g ( B) B ( B ) j j 0 j j B B B B g 0 B B B B0 g g g B g Bg B B g g 0g Solve λ ad (~ g ) for the optmal dspatch: g g g g B B B D L D j j 0 00 j Check equalty costrats: If < (m), let (m) If > (max), let (max) Remove that from the equatos Use the N-R Method: f( ) g D L ( B ) ( B ) 0 j j j B df ( ) f( ) ( ) d ( k) ( k) ( k) ( k) D L f( ) ( k) ( k) ( k) D L df ( ) ( ) d g ( k) ( k) df ( ) g ( ) d 6

27 Ital ad (0) (0) ( B ) ( B ) 0 j j j B ( B ) ( k) ( k) ( k) 0 j j j ( k) ( B ) B f( ) ( k) ( k) ( k) D L g g g g ( k) ( k) ( k) ( k) ( k) D Bjj B0 B00 j g ( B ) B B 0 j j ( k) ( B ) j ( k) ( k) df ( ) g ( ) d g ( k) ( k) Utl (k) <ε 7

28 Example 7.7 Solve the optmal dspatch of three thermal plats a power system. The total system load s 50MW. The base for per ut values s 00MVA. C $ / h C $ / h C $ / h 0 MW 85 MW 0 MW 80 MW 0 MW 70 MW L(pu) (pu) (pu) (pu) (oly B 0) L MW 0.08( ) 0.08( ) 0.079( ) 00 MW 8

29 ( k) ( k) ( B ) (0) λ 8.0 g g B ( k) ( B ) g 0 MW 85 MW 0 MW 80 MW 0 MW 70 MW () () () MW ( ) MW ( ) MW ( ) (5.6) (78.59) () L (7.575).886 () ( ) 48.9 () (4) (4) (4) MW ( ) MW ( ) MW ( ) (5.0907) (64.7) (4) L (4) (5.4767) () ( ) () Ct $/h 9

7.0 Equality Contraints: Lagrange Multipliers

7.0 Equality Contraints: Lagrange Multipliers Systes Optzato 7.0 Equalty Cotrats: Lagrage Multplers Cosder the zato of a o-lear fucto subject to equalty costrats: g f() R ( ) 0 ( ) (7.) where the g ( ) are possbly also olear fuctos, ad < otherwse

More information

G S Power Flow Solution

G S Power Flow Solution G S Power Flow Soluto P Q I y y * 0 1, Y y Y 0 y Y Y 1, P Q ( k) ( k) * ( k 1) 1, Y Y PQ buses * 1 P Q Y ( k1) *( k) ( k) Q Im[ Y ] 1 P buses & Slack bus ( k 1) *( k) ( k) Y 1 P Re[ ] Slack bus 17 Calculato

More information

Module E3 Economic Dispatch Calculation

Module E3 Economic Dispatch Calculation E3 System otrol Overvew ad Ecoomc Dspatch alculato 5 Module E3 Ecoomc Dspatch alculato rmary Author: Gerald B. Sheble Iowa State Uversty Emal Address: gsheble@astate.edu o-author: James D. Mcalley Iowa

More information

1. Introduction. Keywords: Dynamic programming, Economic power dispatch, Optimization, Prohibited operating zones, Ramp-rate constraints.

1. Introduction. Keywords: Dynamic programming, Economic power dispatch, Optimization, Prohibited operating zones, Ramp-rate constraints. A Novel TANAN s Algorthm to solve Ecoomc ower Dspatch wth Geerator Costrats ad Trasmsso Losses Subramaa R 1, Thaushkod K ad Neelakata N 3 1 Assocate rofessor/eee, Akshaya College of Egeerg ad Techology,

More information

Special Instructions / Useful Data

Special Instructions / Useful Data JAM 6 Set of all real umbers P A..d. B, p Posso Specal Istructos / Useful Data x,, :,,, x x Probablty of a evet A Idepedetly ad detcally dstrbuted Bomal dstrbuto wth parameters ad p Posso dstrbuto wth

More information

Support vector machines

Support vector machines CS 75 Mache Learg Lecture Support vector maches Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square CS 75 Mache Learg Outle Outle: Algorthms for lear decso boudary Support vector maches Mamum marg hyperplae.

More information

Kernel-based Methods and Support Vector Machines

Kernel-based Methods and Support Vector Machines Kerel-based Methods ad Support Vector Maches Larr Holder CptS 570 Mache Learg School of Electrcal Egeerg ad Computer Scece Washgto State Uverst Refereces Muller et al. A Itroducto to Kerel-Based Learg

More information

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits Block-Based Compact hermal Modelg of Semcoductor Itegrated Crcuts Master s hess Defese Caddate: Jg Ba Commttee Members: Dr. Mg-Cheg Cheg Dr. Daqg Hou Dr. Robert Schllg July 27, 2009 Outle Itroducto Backgroud

More information

LECTURE 24 LECTURE OUTLINE

LECTURE 24 LECTURE OUTLINE LECTURE 24 LECTURE OUTLINE Gradet proxmal mmzato method Noquadratc proxmal algorthms Etropy mmzato algorthm Expoetal augmeted Lagraga mehod Etropc descet algorthm **************************************

More information

Log1 Contest Round 2 Theta Complex Numbers. 4 points each. 5 points each

Log1 Contest Round 2 Theta Complex Numbers. 4 points each. 5 points each 01 Log1 Cotest Roud Theta Complex Numbers 1 Wrte a b Wrte a b form: 1 5 form: 1 5 4 pots each Wrte a b form: 65 4 4 Evaluate: 65 5 Determe f the followg statemet s always, sometmes, or ever true (you may

More information

CS 1675 Introduction to Machine Learning Lecture 12 Support vector machines

CS 1675 Introduction to Machine Learning Lecture 12 Support vector machines CS 675 Itroducto to Mache Learg Lecture Support vector maches Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square Mdterm eam October 9, 7 I-class eam Closed book Stud materal: Lecture otes Correspodg chapters

More information

Chapter 5 Properties of a Random Sample

Chapter 5 Properties of a Random Sample Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample

More information

An Introduction to. Support Vector Machine

An Introduction to. Support Vector Machine A Itroducto to Support Vector Mache Support Vector Mache (SVM) A classfer derved from statstcal learg theory by Vapk, et al. 99 SVM became famous whe, usg mages as put, t gave accuracy comparable to eural-etwork

More information

Binary classification: Support Vector Machines

Binary classification: Support Vector Machines CS 57 Itroducto to AI Lecture 6 Bar classfcato: Support Vector Maches Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square CS 57 Itro to AI Supervsed learg Data: D { D, D,.., D} a set of eamples D, (,,,,,

More information

Point Estimation: definition of estimators

Point Estimation: definition of estimators Pot Estmato: defto of estmators Pot estmator: ay fucto W (X,..., X ) of a data sample. The exercse of pot estmato s to use partcular fuctos of the data order to estmate certa ukow populato parameters.

More information

Decomposition of Hadamard Matrices

Decomposition of Hadamard Matrices Chapter 7 Decomposto of Hadamard Matrces We hae see Chapter that Hadamard s orgal costructo of Hadamard matrces states that the Kroecer product of Hadamard matrces of orders m ad s a Hadamard matrx of

More information

Department of Agricultural Economics. PhD Qualifier Examination. August 2011

Department of Agricultural Economics. PhD Qualifier Examination. August 2011 Departmet of Agrcultural Ecoomcs PhD Qualfer Examato August 0 Istructos: The exam cossts of sx questos You must aswer all questos If you eed a assumpto to complete a questo, state the assumpto clearly

More information

Support vector machines II

Support vector machines II CS 75 Mache Learg Lecture Support vector maches II Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square Learl separable classes Learl separable classes: here s a hperplae that separates trag staces th o error

More information

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation PGE 30: Formulato ad Soluto Geosystems Egeerg Dr. Balhoff Iterpolato Numercal Methods wth MATLAB, Recktewald, Chapter 0 ad Numercal Methods for Egeers, Chapra ad Caale, 5 th Ed., Part Fve, Chapter 8 ad

More information

LINEARLY CONSTRAINED MINIMIZATION BY USING NEWTON S METHOD

LINEARLY CONSTRAINED MINIMIZATION BY USING NEWTON S METHOD Jural Karya Asl Loreka Ahl Matematk Vol 8 o 205 Page 084-088 Jural Karya Asl Loreka Ahl Matematk LIEARLY COSTRAIED MIIMIZATIO BY USIG EWTO S METHOD Yosza B Dasrl, a Ismal B Moh 2 Faculty Electrocs a Computer

More information

Power Flow S + Buses with either or both Generator Load S G1 S G2 S G3 S D3 S D1 S D4 S D5. S Dk. Injection S G1

Power Flow S + Buses with either or both Generator Load S G1 S G2 S G3 S D3 S D1 S D4 S D5. S Dk. Injection S G1 ower Flow uses wth ether or both Geerator Load G G G D D 4 5 D4 D5 ecto G Net Comple ower ecto - D D ecto s egatve sg at load bus = _ G D mlarl Curret ecto = G _ D At each bus coservato of comple power

More information

Multi Objective Fuzzy Inventory Model with. Demand Dependent Unit Cost and Lead Time. Constraints A Karush Kuhn Tucker Conditions.

Multi Objective Fuzzy Inventory Model with. Demand Dependent Unit Cost and Lead Time. Constraints A Karush Kuhn Tucker Conditions. It. Joural of Math. Aalyss, Vol. 8, 204, o. 4, 87-93 HIKARI Ltd, www.m-hkar.com http://dx.do.org/0.2988/jma.204.30252 Mult Objectve Fuzzy Ivetory Model wth Demad Depedet Ut Cost ad Lead Tme Costrats A

More information

Deterministic Constant Demand Models

Deterministic Constant Demand Models Determstc Costat Demad Models George Lberopoulos Ecoomc Order uatty (EO): basc model 3 4 vetory λ λ Parts to customers wth costat rate λ λ λ EO: basc model Assumptos/otato Costat demad rate: λ (parts per

More information

X ε ) = 0, or equivalently, lim

X ε ) = 0, or equivalently, lim Revew for the prevous lecture Cocepts: order statstcs Theorems: Dstrbutos of order statstcs Examples: How to get the dstrbuto of order statstcs Chapter 5 Propertes of a Radom Sample Secto 55 Covergece

More information

Solution of Large Scale Economic Load Dispatch Problem using Quadratic Programming and GAMS: A Comparative Analysis

Solution of Large Scale Economic Load Dispatch Problem using Quadratic Programming and GAMS: A Comparative Analysis ISSN 1746-7659 Eglad UK Joural of Iformato ad Computg Scece Vol. 7 No. 3 2012 pp. 200-211 Soluto of Large Scale Ecoomc Load Dspatch Problem usg Quadratc Programmg ad GAMS: A Comparatve Aalyss Devedra Bse

More information

CH E 374 Computational Methods in Engineering Fall 2007

CH E 374 Computational Methods in Engineering Fall 2007 CH E 7 Computatoal Methods Egeerg Fall 007 Sample Soluto 5. The data o the varato of the rato of stagato pressure to statc pressure (r ) wth Mach umber ( M ) for the flow through a duct are as follows:

More information

Regression and the LMS Algorithm

Regression and the LMS Algorithm CSE 556: Itroducto to Neural Netorks Regresso ad the LMS Algorthm CSE 556: Regresso 1 Problem statemet CSE 556: Regresso Lear regresso th oe varable Gve a set of N pars of data {, d }, appromate d b a

More information

Economic drivers. Input and output prices Adjustment under ITQs

Economic drivers. Input and output prices Adjustment under ITQs Ecoomc drvers Iput ad output prces Adjustmet uder ITQs Outle Questo beg examed How are fshers lely to adjust ther fshg operatos uder ITQs? Methodologes to loo at the ssue Cost fuctos Proft fuctos Case

More information

ECON 5360 Class Notes GMM

ECON 5360 Class Notes GMM ECON 560 Class Notes GMM Geeralzed Method of Momets (GMM) I beg by outlg the classcal method of momets techque (Fsher, 95) ad the proceed to geeralzed method of momets (Hase, 98).. radtoal Method of Momets

More information

Feature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture)

Feature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture) CSE 546: Mache Learg Lecture 6 Feature Selecto: Part 2 Istructor: Sham Kakade Greedy Algorthms (cotued from the last lecture) There are varety of greedy algorthms ad umerous amg covetos for these algorthms.

More information

A COMPARATIVE STUDY OF THE METHODS OF SOLVING NON-LINEAR PROGRAMMING PROBLEM

A COMPARATIVE STUDY OF THE METHODS OF SOLVING NON-LINEAR PROGRAMMING PROBLEM DAODIL INTERNATIONAL UNIVERSITY JOURNAL O SCIENCE AND TECHNOLOGY, VOLUME, ISSUE, JANUARY 9 A COMPARATIVE STUDY O THE METHODS O SOLVING NON-LINEAR PROGRAMMING PROBLEM Bmal Chadra Das Departmet of Tetle

More information

CSE 5526: Introduction to Neural Networks Linear Regression

CSE 5526: Introduction to Neural Networks Linear Regression CSE 556: Itroducto to Neural Netorks Lear Regresso Part II 1 Problem statemet Part II Problem statemet Part II 3 Lear regresso th oe varable Gve a set of N pars of data , appromate d by a lear fucto

More information

Available Transfer Capability Calculation with Transfer based Static Security -Constrained Optimal Power Flow

Available Transfer Capability Calculation with Transfer based Static Security -Constrained Optimal Power Flow Proceedgs of the 5th WSEAS Iteratoal Coferece o Applcatos of Electrcal Egeerg, Prague, Czech Republc, March 12-14, 26 (pp3-35) Avalable Trasfer Capablty Calculato wth Trasfer based Statc Securty -Costraed

More information

Objectives of Multiple Regression

Objectives of Multiple Regression Obectves of Multple Regresso Establsh the lear equato that best predcts values of a depedet varable Y usg more tha oe eplaator varable from a large set of potetal predctors {,,... k }. Fd that subset of

More information

Optimal Placement of Wind Turbine DG in Primary Distribution Systems for Real Loss Reduction

Optimal Placement of Wind Turbine DG in Primary Distribution Systems for Real Loss Reduction Optmal Placemet of Wd Turbe DG Prmary Dstrbuto s for Real oss Reducto Pukar Mahat, Weerakor Ogsakul ad Nadaraah Mthulaatha Abstract Optmal placemet of wd geerators s essetal, as approprate placemet may

More information

Arithmetic Mean and Geometric Mean

Arithmetic Mean and Geometric Mean Acta Mathematca Ntresa Vol, No, p 43 48 ISSN 453-6083 Arthmetc Mea ad Geometrc Mea Mare Varga a * Peter Mchalča b a Departmet of Mathematcs, Faculty of Natural Sceces, Costate the Phlosopher Uversty Ntra,

More information

MA/CSSE 473 Day 27. Dynamic programming

MA/CSSE 473 Day 27. Dynamic programming MA/CSSE 473 Day 7 Dyamc Programmg Bomal Coeffcets Warshall's algorthm (Optmal BSTs) Studet questos? Dyamc programmg Used for problems wth recursve solutos ad overlappg subproblems Typcally, we save (memoze)

More information

Lecture 3. Sampling, sampling distributions, and parameter estimation

Lecture 3. Sampling, sampling distributions, and parameter estimation Lecture 3 Samplg, samplg dstrbutos, ad parameter estmato Samplg Defto Populato s defed as the collecto of all the possble observatos of terest. The collecto of observatos we take from the populato s called

More information

Simple Linear Regression

Simple Linear Regression Statstcal Methods I (EST 75) Page 139 Smple Lear Regresso Smple regresso applcatos are used to ft a model descrbg a lear relatoshp betwee two varables. The aspects of least squares regresso ad correlato

More information

Pinaki Mitra Dept. of CSE IIT Guwahati

Pinaki Mitra Dept. of CSE IIT Guwahati Pak Mtra Dept. of CSE IIT Guwahat Hero s Problem HIGHWAY FACILITY LOCATION Faclty Hgh Way Farm A Farm B Illustrato of the Proof of Hero s Theorem p q s r r l d(p,r) + d(q,r) = d(p,q) p d(p,r ) + d(q,r

More information

Tokyo Institute of Technology Tokyo Institute of Technology

Tokyo Institute of Technology Tokyo Institute of Technology Outle ult-aget Search usg oroo Partto ad oroo D eermet Revew Itroducto Decreasg desty fucto Stablty Cocluso Fujta Lab, Det. of Cotrol ad System Egeerg, FL07--: July 09,007 Davd Ask ork rogress:. Smulato

More information

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632

More information

( ) 2 2. Multi-Layer Refraction Problem Rafael Espericueta, Bakersfield College, November, 2006

( ) 2 2. Multi-Layer Refraction Problem Rafael Espericueta, Bakersfield College, November, 2006 Mult-Layer Refracto Problem Rafael Espercueta, Bakersfeld College, November, 006 Lght travels at dfferet speeds through dfferet meda, but refracts at layer boudares order to traverse the least-tme path.

More information

STA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1

STA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1 STA 08 Appled Lear Models: Regresso Aalyss Sprg 0 Soluto for Homework #. Let Y the dollar cost per year, X the umber of vsts per year. The the mathematcal relato betwee X ad Y s: Y 300 + X. Ths s a fuctoal

More information

Mu Sequences/Series Solutions National Convention 2014

Mu Sequences/Series Solutions National Convention 2014 Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed

More information

Awodiji Olurotimi.Olakunle*, Bakare Ganiyu.Ayinde**., Aliyu Usman.O.***

Awodiji Olurotimi.Olakunle*, Bakare Ganiyu.Ayinde**., Aliyu Usman.O.*** Iteratoal Joural of Scetfc & Egeerg Research, Volume 5, Issue 3, March-204 589 Short-Term Ecoomc Load Dspatch of Ngera Thermal ower lats Based o Dfferetal Evoluto Approach Awodj Olurotm.Olakule*, Bakare

More information

ECE 595, Section 10 Numerical Simulations Lecture 19: FEM for Electronic Transport. Prof. Peter Bermel February 22, 2013

ECE 595, Section 10 Numerical Simulations Lecture 19: FEM for Electronic Transport. Prof. Peter Bermel February 22, 2013 ECE 595, Secto 0 Numercal Smulatos Lecture 9: FEM for Electroc Trasport Prof. Peter Bermel February, 03 Outle Recap from Wedesday Physcs-based devce modelg Electroc trasport theory FEM electroc trasport

More information

Line Fitting and Regression

Line Fitting and Regression Marquette Uverst MSCS6 Le Fttg ad Regresso Dael B. Rowe, Ph.D. Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Coprght 8 b Marquette Uverst Least Squares Regresso MSCS6 For LSR we have pots

More information

Big Data Analytics. Data Fitting and Sampling. Acknowledgement: Notes by Profs. R. Szeliski, S. Seitz, S. Lazebnik, K. Chaturvedi, and S.

Big Data Analytics. Data Fitting and Sampling. Acknowledgement: Notes by Profs. R. Szeliski, S. Seitz, S. Lazebnik, K. Chaturvedi, and S. Bg Data Aaltcs Data Fttg ad Samplg Ackowledgemet: Notes b Profs. R. Szelsk, S. Setz, S. Lazebk, K. Chaturved, ad S. Shah Fttg: Cocepts ad recpes A bag of techques If we kow whch pots belog to the le, how

More information

LINEAR REGRESSION ANALYSIS

LINEAR REGRESSION ANALYSIS LINEAR REGRESSION ANALYSIS MODULE V Lecture - Correctg Model Iadequaces Through Trasformato ad Weghtg Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur Aalytcal methods for

More information

ESS Line Fitting

ESS Line Fitting ESS 5 014 17. Le Fttg A very commo problem data aalyss s lookg for relatoshpetwee dfferet parameters ad fttg les or surfaces to data. The smplest example s fttg a straght le ad we wll dscuss that here

More information

A conic cutting surface method for linear-quadraticsemidefinite

A conic cutting surface method for linear-quadraticsemidefinite A coc cuttg surface method for lear-quadratcsemdefte programmg Mohammad R. Osoorouch Calfora State Uversty Sa Marcos Sa Marcos, CA Jot wor wth Joh E. Mtchell RPI July 3, 2008 Outle: Secod-order coe: defto

More information

Payment Mechanisms for Electricity Markets with Uncertain Supply

Payment Mechanisms for Electricity Markets with Uncertain Supply Paymet Mechasms for Electrcty Markets wth Ucerta Supply Rya Cory-Wrght a, Ady Phlpott b, Golbo Zaker b a Operatos Research Ceter, Massachusetts Isttute of Techology b Electrc Power Optmzato Cetre, The

More information

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Numercal Computg -I UNIT SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Structure Page Nos..0 Itroducto 6. Objectves 7. Ital Approxmato to a Root 7. Bsecto Method 8.. Error Aalyss 9.4 Regula Fals Method

More information

On the Convexity of the System Loss Function

On the Convexity of the System Loss Function O the Covety of the System Loss Fucto Sebasta de la orre, Member, IEEE ad Fracsco D. Galaa, Fellow, IEEE Abstract We show that the system fucto a power etwork s bouded below by ay umber of supportg hyperplaes

More information

A novel analysis of Optimal Power Flow with TCPS

A novel analysis of Optimal Power Flow with TCPS IOSR Joural of Electrcal ad Electrocs Egeerg (IOSR-JEEE) e-issn: 78-1676,p-ISSN: 30-3331, Volume 10, Issue Ver. I (Mar Apr. 015), PP 68-76 www.osrourals.org S.Saar* S.Saravaaumar **, C.T.Maada***, M.Padmarasa***

More information

Unsupervised Learning and Other Neural Networks

Unsupervised Learning and Other Neural Networks CSE 53 Soft Computg NOT PART OF THE FINAL Usupervsed Learg ad Other Neural Networs Itroducto Mture Destes ad Idetfablty ML Estmates Applcato to Normal Mtures Other Neural Networs Itroducto Prevously, all

More information

Taylor s Series and Interpolation. Interpolation & Curve-fitting. CIS Interpolation. Basic Scenario. Taylor Series interpolates at a specific

Taylor s Series and Interpolation. Interpolation & Curve-fitting. CIS Interpolation. Basic Scenario. Taylor Series interpolates at a specific CIS 54 - Iterpolato Roger Crawfs Basc Scearo We are able to prod some fucto, but do ot kow what t really s. Ths gves us a lst of data pots: [x,f ] f(x) f f + x x + August 2, 25 OSU/CIS 54 3 Taylor s Seres

More information

Optimality Conditions for Distributive Justice

Optimality Conditions for Distributive Justice Optmalty Codtos for Dstrbutve Justce Joh Hooker Carege Mello Uversty Aprl 2008 1 Just Dstrbuto The problem: How to dstrbute resources Tax breaks Medcal care Salares Educato Govermet beefts 2 Justce ad

More information

Chapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements

Chapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements Aoucemets No-Parametrc Desty Estmato Techques HW assged Most of ths lecture was o the blacboard. These sldes cover the same materal as preseted DHS Bometrcs CSE 90-a Lecture 7 CSE90a Fall 06 CSE90a Fall

More information

Analysis of System Performance IN2072 Chapter 5 Analysis of Non Markov Systems

Analysis of System Performance IN2072 Chapter 5 Analysis of Non Markov Systems Char for Network Archtectures ad Servces Prof. Carle Departmet of Computer Scece U Müche Aalyss of System Performace IN2072 Chapter 5 Aalyss of No Markov Systems Dr. Alexader Kle Prof. Dr.-Ig. Georg Carle

More information

Gravitational Search Algorithm for Solving Combined Economic and Emission Dispatch

Gravitational Search Algorithm for Solving Combined Economic and Emission Dispatch IFOTEH-JAHORIA Vol. 4 March 205. ravtatoal Search Algorthm for Solvg Combed Ecoomc ad Emsso Dspatch Jorda Radosavlevć Faculty of Techcal Sceces Uversty of ršta Kosovsa Mtrovca Kosovsa Mtrovca Serba orda.radosavlevc@pr.ac.rs

More information

An Efficient Meta Heuristic Algorithm to Solve Economic Load Dispatch Problems

An Efficient Meta Heuristic Algorithm to Solve Economic Load Dispatch Problems A Effcet Meta Heurstc Algorthm to Solve Ecoomc Load Dspatch Problems R. Subramaa* (C.A.), K. Thaushkod* ad A. Prakash* Abstract: The Ecoomc Load Dspatch (ELD) problems power geerato systems are to reduce

More information

Radial Basis Function Networks

Radial Basis Function Networks Radal Bass Fucto Netorks Radal Bass Fucto Netorks A specal types of ANN that have three layers Iput layer Hdde layer Output layer Mappg from put to hdde layer s olear Mappg from hdde to output layer s

More information

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin Learzato of the Swg Equato We wll cover sectos.5.-.6 ad begg of Secto 3.3 these otes. 1. Sgle mache-fte bus case Cosder a sgle mache coected to a fte bus, as show Fg. 1 below. E y1 V=1./_ Fg. 1 The admttace

More information

CS 2750 Machine Learning. Lecture 8. Linear regression. CS 2750 Machine Learning. Linear regression. is a linear combination of input components x

CS 2750 Machine Learning. Lecture 8. Linear regression. CS 2750 Machine Learning. Linear regression. is a linear combination of input components x CS 75 Mache Learg Lecture 8 Lear regresso Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square CS 75 Mache Learg Lear regresso Fucto f : X Y s a lear combato of put compoets f + + + K d d K k - parameters

More information

ε. Therefore, the estimate

ε. Therefore, the estimate Suggested Aswers, Problem Set 3 ECON 333 Da Hugerma. Ths s ot a very good dea. We kow from the secod FOC problem b) that ( ) SSE / = y x x = ( ) Whch ca be reduced to read y x x = ε x = ( ) The OLS model

More information

Calculus Appendix 1: Inequality Constraints

Calculus Appendix 1: Inequality Constraints Calculus Aed : Iequalty Costrats CA Mamzg wth Iequalty Costrats The method of solvg costraed etremum rolems devsed y Lagrage s arorate f the costrats hold wth strct equalty Ths method works eve whe the

More information

Extreme Value Theory: An Introduction

Extreme Value Theory: An Introduction (correcto d Extreme Value Theory: A Itroducto by Laures de Haa ad Aa Ferrera Wth ths webpage the authors ted to form the readers of errors or mstakes foud the book after publcato. We also gve extesos for

More information

Econometric Methods. Review of Estimation

Econometric Methods. Review of Estimation Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators

More information

Training Sample Model: Given n observations, [[( Yi, x i the sample model can be expressed as (1) where, zero and variance σ

Training Sample Model: Given n observations, [[( Yi, x i the sample model can be expressed as (1) where, zero and variance σ Stat 74 Estmato for Geeral Lear Model Prof. Goel Broad Outle Geeral Lear Model (GLM): Trag Samle Model: Gve observatos, [[( Y, x ), x = ( x,, xr )], =,,, the samle model ca be exressed as Y = µ ( x, x,,

More information

Source-Channel Prediction in Error Resilient Video Coding

Source-Channel Prediction in Error Resilient Video Coding Source-Chael Predcto Error Reslet Vdeo Codg Hua Yag ad Keeth Rose Sgal Compresso Laboratory ECE Departmet Uversty of Calfora, Sata Barbara Outle Itroducto Source-chael predcto Smulato results Coclusos

More information

ALGORITHMS FOR OPTIMAL DECISIONS 2. PRIMAL-DUAL INTERIOR POINT LP ALGORITHMS 3. QUADRATIC PROGRAMMING ALGORITHMS & PORTFOLIO OPTIMISATION

ALGORITHMS FOR OPTIMAL DECISIONS 2. PRIMAL-DUAL INTERIOR POINT LP ALGORITHMS 3. QUADRATIC PROGRAMMING ALGORITHMS & PORTFOLIO OPTIMISATION - (00) Itroducto - ALGORIHMS FOR OPIMAL DECISIONS. INRODUCION: ì Nolear decso problems ì Basc cocepts ad optmalty ì Basc Algorthms: Steepest descet; Frak-Wolfe; Barrer/SUM. PRIMAL-DUAL INERIOR POIN LP

More information

Electricity Market Theory Based on Continuous Time Commodity Model

Electricity Market Theory Based on Continuous Time Commodity Model Electrcty Market Theory Based o Cotuous Tme Commodty Model Haoyog Che 1, La Ha 1 Isttute of Power Ecoomcs ad Electrcty Markets, South Cha Uversty of Techology, Guagzhou 510641, Cha. Emal: eehyche@scut.edu.c

More information

Introduction to local (nonparametric) density estimation. methods

Introduction to local (nonparametric) density estimation. methods Itroducto to local (oparametrc) desty estmato methods A slecture by Yu Lu for ECE 66 Sprg 014 1. Itroducto Ths slecture troduces two local desty estmato methods whch are Parze desty estmato ad k-earest

More information

Model Fitting, RANSAC. Jana Kosecka

Model Fitting, RANSAC. Jana Kosecka Model Fttg, RANSAC Jaa Kosecka Fttg: Issues Prevous strateges Le detecto Hough trasform Smple parametrc model, two parameters m, b m + b Votg strateg Hard to geeralze to hgher dmesos a o + a + a 2 2 +

More information

Research on SVM Prediction Model Based on Chaos Theory

Research on SVM Prediction Model Based on Chaos Theory Advaced Scece ad Techology Letters Vol.3 (SoftTech 06, pp.59-63 http://dx.do.org/0.457/astl.06.3.3 Research o SVM Predcto Model Based o Chaos Theory Sog Lagog, Wu Hux, Zhag Zezhog 3, College of Iformato

More information

Linear Regression Linear Regression with Shrinkage. Some slides are due to Tommi Jaakkola, MIT AI Lab

Linear Regression Linear Regression with Shrinkage. Some slides are due to Tommi Jaakkola, MIT AI Lab Lear Regresso Lear Regresso th Shrkage Some sldes are due to Tomm Jaakkola, MIT AI Lab Itroducto The goal of regresso s to make quattatve real valued predctos o the bass of a vector of features or attrbutes.

More information

Functions of Random Variables

Functions of Random Variables Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,

More information

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur

More information

CHAPTER 6. d. With success = observation greater than 10, x = # of successes = 4, and

CHAPTER 6. d. With success = observation greater than 10, x = # of successes = 4, and CHAPTR 6 Secto 6.. a. We use the samle mea, to estmate the oulato mea µ. Σ 9.80 µ 8.407 7 ~ 7. b. We use the samle meda, 7 (the mddle observato whe arraged ascedg order. c. We use the samle stadard devato,

More information

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted

More information

Supervised learning: Linear regression Logistic regression

Supervised learning: Linear regression Logistic regression CS 57 Itroducto to AI Lecture 4 Supervsed learg: Lear regresso Logstc regresso Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square CS 57 Itro to AI Data: D { D D.. D D Supervsed learg d a set of eamples s

More information

å 1 13 Practice Final Examination Solutions - = CS109 Dec 5, 2018

å 1 13 Practice Final Examination Solutions - = CS109 Dec 5, 2018 Chrs Pech Fal Practce CS09 Dec 5, 08 Practce Fal Examato Solutos. Aswer: 4/5 8/7. There are multle ways to obta ths aswer; here are two: The frst commo method s to sum over all ossbltes for the rak of

More information

Unimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods

Unimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods Malaysa Umodalty Joural Tests of Mathematcal for Global Optmzato Sceces (): of 05 Sgle - 5 Varable (007) Fuctos Usg Statstcal Methods Umodalty Tests for Global Optmzato of Sgle Varable Fuctos Usg Statstcal

More information

KLT Tracker. Alignment. 1. Detect Harris corners in the first frame. 2. For each Harris corner compute motion between consecutive frames

KLT Tracker. Alignment. 1. Detect Harris corners in the first frame. 2. For each Harris corner compute motion between consecutive frames KLT Tracker Tracker. Detect Harrs corers the frst frame 2. For each Harrs corer compute moto betwee cosecutve frames (Algmet). 3. Lk moto vectors successve frames to get a track 4. Itroduce ew Harrs pots

More information

ANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK

ANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK Far East Joural of Appled Mathematcs Volume, Number, 2008, Pages Ths paper s avalable ole at http://www.pphm.com 2008 Pushpa Publshg House ANALYSIS ON THE NATURE OF THE ASI EQUATIONS IN SYNERGETI INTER-REPRESENTATION

More information

Qualifying Exam Statistical Theory Problem Solutions August 2005

Qualifying Exam Statistical Theory Problem Solutions August 2005 Qualfyg Exam Statstcal Theory Problem Solutos August 5. Let X, X,..., X be d uform U(,),

More information

OPTIMAL LAY-OUT OF NATURAL GAS PIPELINE NETWORK

OPTIMAL LAY-OUT OF NATURAL GAS PIPELINE NETWORK 23rd World Gas Coferece, Amsterdam 2006 OPTIMAL LAY-OUT OF NATURAL GAS PIPELINE NETWORK Ma author Tg-zhe, Ne CHINA ABSTRACT I cha, there are lots of gas ppele etwork eeded to be desged ad costructed owadays.

More information

1 Lyapunov Stability Theory

1 Lyapunov Stability Theory Lyapuov Stablty heory I ths secto we cosder proofs of stablty of equlbra of autoomous systems. hs s stadard theory for olear systems, ad oe of the most mportat tools the aalyss of olear systems. It may

More information

L5 Polynomial / Spline Curves

L5 Polynomial / Spline Curves L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a

More information

ECONOMIC OPERATION OF POWER SYSTEMS

ECONOMIC OPERATION OF POWER SYSTEMS ECOOMC OEATO OF OWE SYSTEMS TOUCTO Oe of the earliest applicatios of o-lie cetralized cotrol was to provide a cetral facility, to operate ecoomically, several geeratig plats supplyig the loads of the system.

More information

BAL-001-AB-0a Real Power Balancing Control Performance

BAL-001-AB-0a Real Power Balancing Control Performance Alberta Relablty Stadards Resource ad Demad Balacg BAL-00-AB-0a. Purpose BAL-00-AB-0a Real Power Balacg Cotrol Performace The purpose of ths relablty stadard s to mata WECC steady-state frequecy wth defed

More information

Basics of Information Theory: Markku Juntti. Basic concepts and tools 1 Introduction 2 Entropy, relative entropy and mutual information

Basics of Information Theory: Markku Juntti. Basic concepts and tools 1 Introduction 2 Entropy, relative entropy and mutual information : Maru Jutt Overvew he propertes of adlmted Gaussa chaels are further studed, parallel Gaussa chaels ad Gaussa chaels wth feedac are solved. Source he materal s maly ased o Sectos.4.6 of the course oo

More information

Lecture 16: Backpropogation Algorithm Neural Networks with smooth activation functions

Lecture 16: Backpropogation Algorithm Neural Networks with smooth activation functions CO-511: Learg Theory prg 2017 Lecturer: Ro Lv Lecture 16: Bacpropogato Algorthm Dsclamer: These otes have ot bee subected to the usual scruty reserved for formal publcatos. They may be dstrbuted outsde

More information

LECTURE 2: Linear and quadratic classifiers

LECTURE 2: Linear and quadratic classifiers LECURE : Lear ad quadratc classfers g Part : Bayesa Decso heory he Lkelhood Rato est Maxmum A Posteror ad Maxmum Lkelhood Dscrmat fuctos g Part : Quadratc classfers Bayes classfers for ormally dstrbuted

More information

C.11 Bang-bang Control

C.11 Bang-bang Control Itroucto to Cotrol heory Iclug Optmal Cotrol Nguye a e -.5 C. Bag-bag Cotrol. Itroucto hs chapter eals wth the cotrol wth restrctos: s boue a mght well be possble to have scotutes. o llustrate some of

More information

Singular Value Decomposition. Linear Algebra (3) Singular Value Decomposition. SVD and Eigenvectors. Solving LEs with SVD

Singular Value Decomposition. Linear Algebra (3) Singular Value Decomposition. SVD and Eigenvectors. Solving LEs with SVD Sgular Value Decomosto Lear Algera (3) m Cootes Ay m x matrx wth m ca e decomosed as follows Dagoal matrx A UWV m x x Orthogoal colums U U I w1 0 0 w W M M 0 0 x Orthoormal (Pure rotato) VV V V L 0 L 0

More information

Lecture 07: Poles and Zeros

Lecture 07: Poles and Zeros Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto

More information

Salih Fadıl 1, Burak Urazel 2. Abstract. 1. Introduction. 2. Problem Formulation

Salih Fadıl 1, Burak Urazel 2.  Abstract. 1. Introduction. 2. Problem Formulation Applcato of Modfed Subgradet Algor Based o Feasble Values to Securty Costraed Ecooc Dspatch roble w rohbted Operato Zoes Salh Fadıl, Burak Urazel, Eskşehr Osagaz Uversty, Faculty of Egeerg, Departet of

More information