TECHNICAL TRANSACTIONS 7/2017 CZASOPISMO TECHNICZNE 7/2017 MECHANICS

Size: px
Start display at page:

Download "TECHNICAL TRANSACTIONS 7/2017 CZASOPISMO TECHNICZNE 7/2017 MECHANICS"

Transcription

1 TECHCAL TRASACTOS 7/207 CZASOPSMO TECHCZE 7/207 MECHACS DO: / XCT Artur Krowa nsttute of Computer Scence, Faculty of Mechancal Engneerng, Cracow Unversty of Technology Jordan Podgórs Student of Appled nformatcs, Faculty of Mechancal Engneerng, Cracow Unversty of Technology The adaptaton of the cross valdaton aproach for rbf-based collocaton methods Adaptacja podejsca rzyżowego sprawdzana do metod bazujących na funcjach rbf Abstract The paper shows the adaptaton of the cross valdaton approach, nown from nterpolaton problems, for estmatng the value of a shape parameter for radal bass functons. The latter are nvolved n two collocaton technques used on an unstructured grd to fnd approxmate soluton of dfferental equatons. To obtan accurate results, the shape parameter should be chosen as a result of a trade-off between accuracy and condtonng of the system. The cross valdaton approach called leave one out taes these aspects nto consderaton. The numercal examples that summarze the nvestgatons show the usefulness of the approach. Keywords: radal bass functons, shape parameter, cross-valdaton approach Streszczene W artyule poazano adaptację algorytmu rzyżowego sprawdzana, znanego z zagadneń statysty nterpolacj, do wyznaczena wartośc współczynna ształtu w radalnych funcjach bazowych. Funcje te użyto w dwóch typach techn oloacyjnych stosowanych na neregularnej satce do przyblżonego rozwązywana równań różnczowych. Aby otrzymać rezultaty o odpowednej doładnośc, współczynn ształtu pownen być dobrany na baze ompromsu pomędzy doładnoścą a uwarunowanem uładu równań. Przedstawony algorytm, zwany leave one out, berze te aspety pod uwagę. Podsumowanem artyułu są numeryczne testy, tóre poazują użyteczność tego podejśca. Słowa luczowe: radalne funcje bazowe, współczynn ształtu, algorytm cross-valdaton 47

2 . ntroducton n recent years a sgnfcant development of numercal methods for solvng dfferental equatons wth the use of an unstructured grd has been observed. All these technques are called meshless or meshfree methods as opposed to well-nown mesh based methods such as fnte element, fnte dfference, or fnte volume method. The meshless methods can overcome some drawbacs of mesh based technques assocated wth grd dstorton, remeshng n adaptaton approaches and handlng problems characterzed by complcated geometres. There are several formulatons of meshless methods [, 2]. Among them, one can dstngush methods that apply nterpolant composed of radal bass functons (RF) and use the collocaton technque n order to dscretze a dfferental problem the Kansa method and RF-based pseudospectral method (RF-PS). There are many papers devoted to these methods [3 7]. The problem of choosng a respectve value of the shape parameter for RF s the ssue whch appears n almost all of these papers. Ths parameter s responsble for the flatness of RF and nfluences the accuracy of the methods as well as condtonng of the system of algebrac equatons that follows from the dscretzaton procedure. To acheve hgh accuracy, the value of the parameter should be large but ths leads to an ll-condtoned system, whch cannot be easly solved [8]. To estmate a respectve value, a trade-off s needed. Ths value s estmated mostly on the base of researcher s experence, but there are also a few more sophstcated approaches [9, 0]. The present paper shows the adaptaton of a nd of cross valdaton algorthm to ths end. The latter s called leave one out and t has been used n statstcs and nterpolaton problems [5]. The paper presents an easy mplementaton of the approach n the Kansa method and demonstrates that the same value of the parameter s vald for the RF-PS method. The layout of the paper s as follows: n secton 2 two RF-based numercal technques are brefly descrbed, n secton 3 the use of the leave one out algorthm s demonstrated and fnally n secton 4 the numercal tests are shown. 2. RF-based collocaton methods for partal dfferental equatons There are several numercal methods for solvng boundary-value problem. The latter can be wrtten n a general form as: Lu= f n Ω, () u = g on Ω where L and denote lnear dfferental operators mposed on the sought functon u n the doman Ω and on the boundares Ω, respectvely, and f, g are nown functons. Among the methods that tae advantage of rregularly dstrbuted nodes for doman dscretzaton, one can dstngush collocaton technques that employs nterpolant consstng of RF. Such nterpolant has a general form, whch s as follows: 48

3 u( x) = αjϕ x x j j= where x, =,, represent a set of rregularly dstrbuted nodes n the doman as well as on the boundary. Among them one can dstngush nteror nodes x, =,..., and the nodes mposed on the boundary x, =,...,. n Eq. (2) ϕj( x) = ϕ( x x j ) represents RF and α j are the nterpolaton coeffcents. One of the approach whch falls nto the mentoned category called the Kansa method, ntroduces functon (2) to problem () and by the collocaton technque transforms dfferental problem nto a set of algebrac equatons. Another one uses the RF nterpolant to determne dscrete approxmaton of dervatves ncluded n, thereby obtanng algebrac approxmaton of the consdered problem the RF- PS method. The detals of these methods are gven below. (2) 2.. Kansa method y ntroducng nterpolaton functon descrbed by Eq. (2) nto Eq. () and by collocatng at each node n the doman ncludng boundares, one gets: j= j= α α Lϕ x x = f x, =,...,, j j x j ϕ ( x x j ) x = = g x,,..., Usng matrx notaton one can put Eq. (3) n the followng way: A α=b (4) A L f n Eq. A = b A, = g, where ( AL ) = Lϕ x x j ( j ), =,...,, j=,..., x ( A ) = ϕ x x j ( j ), =,...,, j=,..., x, f = f( x ), =,...,, g = g( x ), =,..., The nterpolaton coeffcents are obtaned from Eq. f only A s nvertble: (3) α =A b (5) The study on the nvertblty of the Kansa matrx (A ) can be found n several papers. One can conclude that although there are numercal examples showng that the matrx can be sngular for arbtrary center locatons [], these cases are rare and many other wors [2] ndcate a successful applcaton of the method. Snce the nterpolaton coeffcents are determned, the approxmate soluton s descrbed by nterpolant (2). 49

4 2.2. RF-PS method RF-PS s a combnaton of RF nterpolaton wth the pseudospectral technque. n ths approach, the nterpolant n the form of (2) s used to determne the dscrete approxmaton of dfferental operator from Eq. (). To ths end, the nterpolaton condtons are taen nto account: j= αϕ j ( x x j )= u, =,..., (6) t allows for presentng the coeffcents α n terms of the values of the sought functon, whch can be put n the matrx notaton n the followng way: α = A u (7) T where α = α α, u = u u and Aj = ϕ( x x j ),, j=,...,. Then, by mposng an approprate dfferental operator on the nterpolant and evaluatng t at each nteror as well as boundary node, one gets: u = A α (8) L T u L L = A (9) where u L, u are dscrete representaton of approprate dervatves and A L, A are the same matrces that appear n Eq. (4). Usng Eq. (7) one can express dervatves u L and u n Eqs. (8) and (9) n terms of the sought functon values from the whole doman as: α u = A A u (0) T where u =[ ul, u ] and A matrx s composed of A L, A n the the same way as n the Kansa method (Eq. (4)). Matrx A A s a dscrete form of dfferental operators L, and s called as dfferentaton matrx n the nomenclature of pseudospectral methods. Wth the use of ths matrx Eq. () can be easly dscretzed: A A u= b () and solved for unnown functon values, whch yelds: u= AA b (2) From the above t can be clearly seen that the solvablty of the problem usng the RF-PS s condtoned by the nevtablty of the same matrx as n the Kansa approach. As one can notce, the approach presented s smlar to the Kansa method. The man dfference between the RF-PS and Kansa method s that n the latter we ntroduce the 50

5 nterpolaton functon drectly nto dfferental equaton obtanng the nterpolaton coeffcents. Wth these coeffcents, the nterpolaton functon approxmates the soluton at any pont of the doman. n the RF-PS we use the same nterpolaton functon to derve a dscrete approxmaton of a dfferental operator at each node and then ths approxmaton n the form of a dfferentaton matrx s used to dscretze the equaton. Fnally, n the RF-PS, the functon values at the nodes are obtaned as the soluton. Snce the RF-PS operates drectly wth functon values (does not need to evaluate the nterpolant) t s more effcent n non-lnear problems and n tme dependent problems, where a nd of teratons are requred to obtan the approxmate functon values. 3. Adoptng leave-one-out algorthm for boundary-value-problem t was found [5, 6, 8] that the shape parameter has a sgnfcant nfluence on accuracy. A larger value of ths parameter theoretcally should mae the soluton more accurate but leads to an ll-condtoned system, whch may not be accurately solved. Therefore, an mportant ssue n usng RF based methods s the choce of the approprate value of c. One of the approaches that can be employed comes from the nterpolaton problem: A α =u (3) where A, α and u are the RF nterpolaton matrx, vector of nterpolaton coeffcents and vector of nodal functon values, respectvely. Eq. s a result of the applcaton of the nterpolaton condtons. n ths case values of u are nown. The optmal value of c depends on the number of nodes and on the pattern of ther dstrbuton, on the rght-hand-sde vector and precson of computaton. All these factors are taen nto consderaton n the approach as t s reported n [3]. The approach s based on cross-valdaton and s called leave-one-out. n ths algorthm, an optmal value of c s obtaned by mnmzng the error of an nterpolant based on the data from whch one of the nodes was left out. The error at the th node, whch was left out can be obtaned as: E = u u [ ] ( x ) (4) [ where u s the functon value at ths node and u ] [ ] ( x) = αj ϕ x x nterpolant to the data u = [u j=,,u -,u +,, u ]. j ( j ) s the RF T Removng n turn each of the nodes, the vector of errors E = E E can be composed. The norm of ths vector ndcates the qualty of the ft, whch depends on the shape parameter. y repeatng calculatons for dfferent values of c, one can choose the optmal one whch mnmzes the E norm. Snce the mplementaton of hs strategy s rather expensve, Rppa [3] showed that E can be computed n a smpler way as: 5

6 E = α A (5) where α s the th coeffcent n the nterpolant u based on a full set of nodes and A s the th element n the dagonal of the nverse of the nterpolaton matrx. n the present paper, the above method for estmatng the optmal c s adopted to methods of solvng dfferental equatons presented n sectons 2. and 2.2. Here, we follow drectly the dea presented by Rppa, understandng the problem descrbed by Eq. () as a nd of nterpolaton problem, but defned for dervatves. At frst, let us consder the method from secton 2., where the dscretzed boundaryvalue-problem s approxmated by Eq.(4). We can consder ths system of equatons as the nterpolaton condtons such as Eq.(3), but wrtten for dervatve of the sought functon. n ths case the nterpolant appled at the nteror nodes assumes the form of: u ( x) = Lu( x) = α Lϕ x x L = and nterpolant for approxmaton of boundary values s as follows: u ( x) = u( x) = α ϕ x x = (6) (7) One can mae use of the leave one out algorthm to obtan the soluton of ths nterpolaton problem n the case, where the th node s omtted: (8) α [ ] [ ] [ = A ] b Wth the obtaned coeffcents, the nterpolant for dervatve at the th node s evaluated [ ] [ ] [ ] ul ( x ) = α Lϕ x x or u ( x ) = α =, yeldng the error at the th node as: x =, [ ] ( ) ϕ x x x (9) E = b u [ ] ( x ) or E = b u [ ] ( x ) (20) L where b s the th element of the rght-hand-sde vector ntroduced by Eq.(4). Mang use of Rppa s acceleraton (Eq.(5)), the above error can be computed faster by: α E = = A b (2) A ( A ) where A s the nverse of dervatve nterpolaton matrx based on full set of nodes, ntroduced by Eq.(4). 52

7 For the method presented n secton 2.2, Eq. () can be consdered as the nterpolaton problem defned for the dervatves and therefore can be the startng pont for dervaton of the formula for error n the context of leave one out algorthm. Unfortunately, n the RF-PS [ approach we are not able to fnd drectly the value of u ] L ( x ). y solvng the system n the form of Eq. (), but defned by omttng the th node, we obtan u, =,, -, +,,, whch can be used by Eq. (0) to approxmate the dervatves at all the nodes wthout the th node. Therefore, formula (20) cannot be drectly obtaned t requres the value of the dervatve at the th node. [ ] [ ] [ ] [ ] To overcome ths nconvenence, wth the values of u = A ( A ) b at all nodes [ wthout the th node, one should mae a step bac and use Eq. (7) to calculate a ] yeldng: α [ ] [ ] [ ] [ ] [ ] [ ] [ ] = = = A u A A A b A b [ where A ] s a Kansa matrx derved wthout the th node. t s obvous that the same coeffcents as those used n Eq. (8) are obtaned, whch allows for evaluatng the nterpolant for dervatve at the th node and leads to the same formula for the error as n the Kansa approach (Eq.(2)). (22) 4. umercal tests To show the usefulness of the approach proposed n the last secton several equatons n 2D space have been solved wth the Kansa and RF-PS methods. Due to the lmted space, the results of two of them are presented. Example. Posson equaton wth Drchlet boundary condtons: 2 uxy (, ) = sn( π x)sn( π y), ( x, y) Ω = [ 0, ] [ 0, ] uxy (, ) = 0, ( x, y) Ω (23) for whch the analytcal soluton has the form of: u= sn( π x)sn( π y) (24) 2π 2 Example 2. 2D modfed Helmholtz equaton wth non-homogeneous boundary condtons: 2 uxy (, ) u( x, y) = ( π 2 + )( ysn( π x) + xcos( π y)), ( x, y) Ω = [ 0, ] [ 0, ] u( 0, y) = 0, u(, y) = cos( π y), u( x, 0) = x, ux (, ) = sn( π x) x (25) 53

8 whose exact soluton has the form: uxy (, ) = ysn( π x) + xcos( y) (26) As a measure of the qualty of results, an error norm n the followng form has been ntroduced ε= n e 2 e 2 ( u u ) ( u ) 00%, where u n s numercal soluton, u e the = = exact one. The obtaned results are shown n Tables 4. (regular grd) Table. Results for example solved by Kansa s method c opt ε mn leave one out algorthm c ε e e e e e e e e-05 (rrreg. grd) e e e e e e e e-05 Table 2. Results for example solved by the RF-PS method (regular grd) c opt ε mn leave one out algorthm c ε e e e e e e e e-05 (rreg.grd) e e e e e e e e-05 t s obvous that the presented algorthm for fndng a good value of the shape parameter gves the same results for Kansa s method as well as for the RF-PS when appled on the same grd, snce t maes use of the same matrx. Therefore, approprate columns presentng 54

9 a good value of c possess the same values, comparng between two dscretzaton methods, but for clear comparson of the results they are ncluded n both tables. For comparson, the optmal value of c obtaned on the base of the exact soluton s also ncluded n the tables. y analyzng the results one can conclude that the presented approach gves the values of c that lead to acceptable results. (regular grd) Table 3. Results for example 2 solved by Kansa s method c opt ε mn leave one out algorthm c ε e e e e e e e e-04 (rrreg. grd) e e e e e e e e-04 Table 4. Results for example 2 solved by the RF-PS method (regular grd) c opt ε mn leave one out algorthm c ε e e e e e e e e-04 (rreg.grd) e e e e e e e e Concluson n the paper, the problem of accuracy of two meshless collocaton methods that employ RF nterpolaton s consdered. t s well-nown that the accuracy of such methods depends on the value of the shape parameter ncluded n RF. The paper apples a nd of cross valdaton approach, nown form nterpolaton problems, to fnd respectve value of ths parameter. 55

10 To ths end, the system of algebrac equatons followng from the applcaton of the Kansa or RF-PS method s treated as a nd of nterpolaton problem. The leave one out approach taes nto consderaton several dscretzaton and computatonal parameters to fnd the value of c, whch s a great value of ths approach. The use of ths algorthm requres to set a range, whch s searched for the optmal value of c and many evaluatons of system matrx. These can be consdered as some weanesses of the algorthm that should be mproved n future wor. References [] elytscho T., Krongauz Y., Organ D., Flrmng M., Krysl P., Meshless methods: an overvew and recent developments, Computer Methods n Appled Mechancs and Engneerng, vol. 39, 996, [2] Lu G.R., Meshlees Methods Movng beyond the Fnte Element Method, CRC Press, oca Raton, Florda [3] Kansa E., Multquadrcs A scattered data approxmaton scheme wth applcatons to computatonal flud dynamcs : Surface approxmatons and partal dervatve estmates, Computers and Mathematcs wth Applcatons, vol. 9, 990, [4] Kansa E., Multquadrcs A scattered data approxmaton scheme wth applcatons to computatonal flud dynamcs : Solutons to parabolc, hyperbolc, and ellptc partal dfferental equatons, Computers and Mathematcs wth Applcatons, vol. 9, 990, [5] Fasshauer G.E., Meshfree Approxmaton Methods wth Matlab, World Scentfc Publshng, Sngapore, [6] Cheng A.H.D., Multquadrcs and ts shape parameter a numercal nvestgaton of error estmate, condton number and round-off error by arbtrary precson computaton, Engneerng analyss wth boundary elements, vol. 36, 202, [7] Ferrera A.J.M, A formulaton of the multquadrc radal bass functon method for the analyss of lamnated composte plates, Compost Structures, vol. 59, 2003, [8] Schabac R., Error estmates and condton numbers for radal bass functon nterpolaton, Advances n Computatonal Mathematcs, vol. 3, 995, [9] Krowa A., Radal bass functon-based pseudospectral method for statc analyss of thn plates, Engneerng Analyss wth oundary Elements, vol. 7, 206, [0] Krowa A., On choosng a value of shape parameter n Radal ass Functon collocaton methods, umercal Methods for Partal Dfferental Equatons, submtted for publcaton. [] Hon Y.C., Schabac R., On nonsymmetrc collocaton by radal bass functons, Appl. Math. Comput., vol. 9, 200, [2] Chen W., Fu Z.J., Chen C.S., Recent Advances n Radal ass Functon Collocaton Methods, Sprnger, 204. [3] Rppa S., An algorthm for selectng a good value for the parameter c n radal bass functon nterpolaton, Adv. n Comput. Math., vol., 999,

Hermite Splines in Lie Groups as Products of Geodesics

Hermite Splines in Lie Groups as Products of Geodesics Hermte Splnes n Le Groups as Products of Geodescs Ethan Eade Updated May 28, 2017 1 Introducton 1.1 Goal Ths document defnes a curve n the Le group G parametrzed by tme and by structural parameters n the

More information

Solitary and Traveling Wave Solutions to a Model. of Long Range Diffusion Involving Flux with. Stability Analysis

Solitary and Traveling Wave Solutions to a Model. of Long Range Diffusion Involving Flux with. Stability Analysis Internatonal Mathematcal Forum, Vol. 6,, no. 7, 8 Soltary and Travelng Wave Solutons to a Model of Long Range ffuson Involvng Flux wth Stablty Analyss Manar A. Al-Qudah Math epartment, Rabgh Faculty of

More information

Very simple computational domains can be discretized using boundary-fitted structured meshes (also called grids)

Very simple computational domains can be discretized using boundary-fitted structured meshes (also called grids) Structured meshes Very smple computatonal domans can be dscretzed usng boundary-ftted structured meshes (also called grds) The grd lnes of a Cartesan mesh are parallel to one another Structured meshes

More information

NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS

NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS ARPN Journal of Engneerng and Appled Scences 006-017 Asan Research Publshng Network (ARPN). All rghts reserved. NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS Igor Grgoryev, Svetlana

More information

An Accurate Evaluation of Integrals in Convex and Non convex Polygonal Domain by Twelve Node Quadrilateral Finite Element Method

An Accurate Evaluation of Integrals in Convex and Non convex Polygonal Domain by Twelve Node Quadrilateral Finite Element Method Internatonal Journal of Computatonal and Appled Mathematcs. ISSN 89-4966 Volume, Number (07), pp. 33-4 Research Inda Publcatons http://www.rpublcaton.com An Accurate Evaluaton of Integrals n Convex and

More information

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements Module 3: Element Propertes Lecture : Lagrange and Serendpty Elements 5 In last lecture note, the nterpolaton functons are derved on the bass of assumed polynomal from Pascal s trangle for the fled varable.

More information

Numerical Solution of Deformation Equations. in Homotopy Analysis Method

Numerical Solution of Deformation Equations. in Homotopy Analysis Method Appled Mathematcal Scences, Vol. 6, 2012, no. 8, 357 367 Nmercal Solton of Deformaton Eqatons n Homotopy Analyss Method J. Izadan and M. MohammadzadeAttar Department of Mathematcs, Faclty of Scences, Mashhad

More information

An Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices

An Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices Internatonal Mathematcal Forum, Vol 7, 2012, no 52, 2549-2554 An Applcaton of the Dulmage-Mendelsohn Decomposton to Sparse Null Space Bases of Full Row Rank Matrces Mostafa Khorramzadeh Department of Mathematcal

More information

S1 Note. Basis functions.

S1 Note. Basis functions. S1 Note. Bass functons. Contents Types of bass functons...1 The Fourer bass...2 B-splne bass...3 Power and type I error rates wth dfferent numbers of bass functons...4 Table S1. Smulaton results of type

More information

Mathematics 256 a course in differential equations for engineering students

Mathematics 256 a course in differential equations for engineering students Mathematcs 56 a course n dfferental equatons for engneerng students Chapter 5. More effcent methods of numercal soluton Euler s method s qute neffcent. Because the error s essentally proportonal to the

More information

S.P.H. : A SOLUTION TO AVOID USING EROSION CRITERION?

S.P.H. : A SOLUTION TO AVOID USING EROSION CRITERION? S.P.H. : A SOLUTION TO AVOID USING EROSION CRITERION? Célne GALLET ENSICA 1 place Emle Bloun 31056 TOULOUSE CEDEX e-mal :cgallet@ensca.fr Jean Luc LACOME DYNALIS Immeuble AEROPOLE - Bat 1 5, Avenue Albert

More information

High-Boost Mesh Filtering for 3-D Shape Enhancement

High-Boost Mesh Filtering for 3-D Shape Enhancement Hgh-Boost Mesh Flterng for 3-D Shape Enhancement Hrokazu Yagou Λ Alexander Belyaev y Damng We z Λ y z ; ; Shape Modelng Laboratory, Unversty of Azu, Azu-Wakamatsu 965-8580 Japan y Computer Graphcs Group,

More information

Using Radial Basis Functions to Solve Geodesics Equations for Body Measurements *

Using Radial Basis Functions to Solve Geodesics Equations for Body Measurements * 7659, England, UK Journal of Informaton and Computng Scence Vol. 2, No. 2, 2007, pp. 119-126 Usng Radal Bass Functons to Solve Geodescs Equatons for Body Measurements * R. Ng 1+, G.T.Y. Pong 2 and M. Wong

More information

LU Decomposition Method Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America

LU Decomposition Method Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America nbm_sle_sm_ludecomp.nb 1 LU Decomposton Method Jame Trahan, Autar Kaw, Kevn Martn Unverst of South Florda Unted States of Amerca aw@eng.usf.edu nbm_sle_sm_ludecomp.nb 2 Introducton When solvng multple

More information

Chapter 6 Programmng the fnte element method Inow turn to the man subject of ths book: The mplementaton of the fnte element algorthm n computer programs. In order to make my dscusson as straghtforward

More information

Determining the Optimal Bandwidth Based on Multi-criterion Fusion

Determining the Optimal Bandwidth Based on Multi-criterion Fusion Proceedngs of 01 4th Internatonal Conference on Machne Learnng and Computng IPCSIT vol. 5 (01) (01) IACSIT Press, Sngapore Determnng the Optmal Bandwdth Based on Mult-crteron Fuson Ha-L Lang 1+, Xan-Mn

More information

A Simple and Efficient Goal Programming Model for Computing of Fuzzy Linear Regression Parameters with Considering Outliers

A Simple and Efficient Goal Programming Model for Computing of Fuzzy Linear Regression Parameters with Considering Outliers 62626262621 Journal of Uncertan Systems Vol.5, No.1, pp.62-71, 211 Onlne at: www.us.org.u A Smple and Effcent Goal Programmng Model for Computng of Fuzzy Lnear Regresson Parameters wth Consderng Outlers

More information

Quality Improvement Algorithm for Tetrahedral Mesh Based on Optimal Delaunay Triangulation

Quality Improvement Algorithm for Tetrahedral Mesh Based on Optimal Delaunay Triangulation Intellgent Informaton Management, 013, 5, 191-195 Publshed Onlne November 013 (http://www.scrp.org/journal/m) http://dx.do.org/10.36/m.013.5601 Qualty Improvement Algorthm for Tetrahedral Mesh Based on

More information

Structured Grid Generation Via Constraint on Displacement of Internal Nodes

Structured Grid Generation Via Constraint on Displacement of Internal Nodes Internatonal Journal of Basc & Appled Scences IJBAS-IJENS Vol: 11 No: 4 79 Structured Grd Generaton Va Constrant on Dsplacement of Internal Nodes Al Ashrafzadeh, Razeh Jalalabad Abstract Structured grd

More information

For instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1)

For instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1) Secton 1.2 Subsets and the Boolean operatons on sets If every element of the set A s an element of the set B, we say that A s a subset of B, or that A s contaned n B, or that B contans A, and we wrte A

More information

A COMPARISON OF TWO METHODS FOR FITTING HIGH DIMENSIONAL RESPONSE SURFACES

A COMPARISON OF TWO METHODS FOR FITTING HIGH DIMENSIONAL RESPONSE SURFACES Mam, Florda, U.S.A., Aprl 6-8, 7 A COMPARISON OF TWO METHODS FOR FITTING HIGH DIMENSIONAL RESPONSE SURFACES Marcelo J. Colaço Department of Mechancal and Materals Eng. Mltary Insttute of Engneerng Ro de

More information

Solving two-person zero-sum game by Matlab

Solving two-person zero-sum game by Matlab Appled Mechancs and Materals Onlne: 2011-02-02 ISSN: 1662-7482, Vols. 50-51, pp 262-265 do:10.4028/www.scentfc.net/amm.50-51.262 2011 Trans Tech Publcatons, Swtzerland Solvng two-person zero-sum game by

More information

Course Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms

Course Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms Course Introducton Course Topcs Exams, abs, Proects A quc loo at a few algorthms 1 Advanced Data Structures and Algorthms Descrpton: We are gong to dscuss algorthm complexty analyss, algorthm desgn technques

More information

Support Vector Machines

Support Vector Machines /9/207 MIST.6060 Busness Intellgence and Data Mnng What are Support Vector Machnes? Support Vector Machnes Support Vector Machnes (SVMs) are supervsed learnng technques that analyze data and recognze patterns.

More information

A New Approach For the Ranking of Fuzzy Sets With Different Heights

A New Approach For the Ranking of Fuzzy Sets With Different Heights New pproach For the ankng of Fuzzy Sets Wth Dfferent Heghts Pushpnder Sngh School of Mathematcs Computer pplcatons Thapar Unversty, Patala-7 00 Inda pushpndersnl@gmalcom STCT ankng of fuzzy sets plays

More information

Learning the Kernel Parameters in Kernel Minimum Distance Classifier

Learning the Kernel Parameters in Kernel Minimum Distance Classifier Learnng the Kernel Parameters n Kernel Mnmum Dstance Classfer Daoqang Zhang 1,, Songcan Chen and Zh-Hua Zhou 1* 1 Natonal Laboratory for Novel Software Technology Nanjng Unversty, Nanjng 193, Chna Department

More information

Smoothing Spline ANOVA for variable screening

Smoothing Spline ANOVA for variable screening Smoothng Splne ANOVA for varable screenng a useful tool for metamodels tranng and mult-objectve optmzaton L. Rcco, E. Rgon, A. Turco Outlne RSM Introducton Possble couplng Test case MOO MOO wth Game Theory

More information

A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS

A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS Proceedngs of the Wnter Smulaton Conference M E Kuhl, N M Steger, F B Armstrong, and J A Jones, eds A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS Mark W Brantley Chun-Hung

More information

An Improved Isogeometric Analysis Using the Lagrange Multiplier Method

An Improved Isogeometric Analysis Using the Lagrange Multiplier Method An Improved Isogeometrc Analyss Usng the Lagrange Mltpler Method N. Valzadeh 1, S. Sh. Ghorash 2, S. Mohammad 3, S. Shojaee 1, H. Ghasemzadeh 2 1 Department of Cvl Engneerng, Unversty of Kerman, Kerman,

More information

Radial Basis Functions

Radial Basis Functions Radal Bass Functons Mesh Reconstructon Input: pont cloud Output: water-tght manfold mesh Explct Connectvty estmaton Implct Sgned dstance functon estmaton Image from: Reconstructon and Representaton of

More information

Parallelism for Nested Loops with Non-uniform and Flow Dependences

Parallelism for Nested Loops with Non-uniform and Flow Dependences Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr

More information

A Fast Visual Tracking Algorithm Based on Circle Pixels Matching

A Fast Visual Tracking Algorithm Based on Circle Pixels Matching A Fast Vsual Trackng Algorthm Based on Crcle Pxels Matchng Zhqang Hou hou_zhq@sohu.com Chongzhao Han czhan@mal.xjtu.edu.cn Ln Zheng Abstract: A fast vsual trackng algorthm based on crcle pxels matchng

More information

Analysis of Continuous Beams in General

Analysis of Continuous Beams in General Analyss of Contnuous Beams n General Contnuous beams consdered here are prsmatc, rgdly connected to each beam segment and supported at varous ponts along the beam. onts are selected at ponts of support,

More information

5 The Primal-Dual Method

5 The Primal-Dual Method 5 The Prmal-Dual Method Orgnally desgned as a method for solvng lnear programs, where t reduces weghted optmzaton problems to smpler combnatoral ones, the prmal-dual method (PDM) has receved much attenton

More information

Lecture #15 Lecture Notes

Lecture #15 Lecture Notes Lecture #15 Lecture Notes The ocean water column s very much a 3-D spatal entt and we need to represent that structure n an economcal way to deal wth t n calculatons. We wll dscuss one way to do so, emprcal

More information

arxiv: v3 [cs.na] 18 Mar 2015

arxiv: v3 [cs.na] 18 Mar 2015 A Fast Block Low-Rank Dense Solver wth Applcatons to Fnte-Element Matrces AmrHossen Amnfar a,1,, Svaram Ambkasaran b,, Erc Darve c,1 a 496 Lomta Mall, Room 14, Stanford, CA, 9435 b Warren Weaver Hall,

More information

A Binarization Algorithm specialized on Document Images and Photos

A Binarization Algorithm specialized on Document Images and Photos A Bnarzaton Algorthm specalzed on Document mages and Photos Ergna Kavalleratou Dept. of nformaton and Communcaton Systems Engneerng Unversty of the Aegean kavalleratou@aegean.gr Abstract n ths paper, a

More information

AP PHYSICS B 2008 SCORING GUIDELINES

AP PHYSICS B 2008 SCORING GUIDELINES AP PHYSICS B 2008 SCORING GUIDELINES General Notes About 2008 AP Physcs Scorng Gudelnes 1. The solutons contan the most common method of solvng the free-response questons and the allocaton of ponts for

More information

Solutions to Programming Assignment Five Interpolation and Numerical Differentiation

Solutions to Programming Assignment Five Interpolation and Numerical Differentiation College of Engneerng and Coputer Scence Mechancal Engneerng Departent Mechancal Engneerng 309 Nuercal Analyss of Engneerng Systes Sprng 04 Nuber: 537 Instructor: Larry Caretto Solutons to Prograng Assgnent

More information

Proper Choice of Data Used for the Estimation of Datum Transformation Parameters

Proper Choice of Data Used for the Estimation of Datum Transformation Parameters Proper Choce of Data Used for the Estmaton of Datum Transformaton Parameters Hakan S. KUTOGLU, Turkey Key words: Coordnate systems; transformaton; estmaton, relablty. SUMMARY Advances n technologes and

More information

APPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT

APPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT 3. - 5. 5., Brno, Czech Republc, EU APPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT Abstract Josef TOŠENOVSKÝ ) Lenka MONSPORTOVÁ ) Flp TOŠENOVSKÝ

More information

Review of approximation techniques

Review of approximation techniques CHAPTER 2 Revew of appromaton technques 2. Introducton Optmzaton problems n engneerng desgn are characterzed by the followng assocated features: the objectve functon and constrants are mplct functons evaluated

More information

An accelerated value/policy iteration scheme for the solution of DP equations

An accelerated value/policy iteration scheme for the solution of DP equations An accelerated value/polcy teraton scheme for the soluton of DP equatons Alessandro Alla 1, Maurzo Falcone 2, and Dante Kalse 3 1 SAPIENZA - Unversty of Rome, Ple. Aldo Moro 2, Rome, Italy alla@mat.unroma1.t

More information

Type-2 Fuzzy Non-uniform Rational B-spline Model with Type-2 Fuzzy Data

Type-2 Fuzzy Non-uniform Rational B-spline Model with Type-2 Fuzzy Data Malaysan Journal of Mathematcal Scences 11(S) Aprl : 35 46 (2017) Specal Issue: The 2nd Internatonal Conference and Workshop on Mathematcal Analyss (ICWOMA 2016) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES

More information

Content Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers

Content Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers IOSR Journal of Electroncs and Communcaton Engneerng (IOSR-JECE) e-issn: 78-834,p- ISSN: 78-8735.Volume 9, Issue, Ver. IV (Mar - Apr. 04), PP 0-07 Content Based Image Retreval Usng -D Dscrete Wavelet wth

More information

Support Vector Machines

Support Vector Machines Support Vector Machnes Decson surface s a hyperplane (lne n 2D) n feature space (smlar to the Perceptron) Arguably, the most mportant recent dscovery n machne learnng In a nutshell: map the data to a predetermned

More information

In the planar case, one possibility to create a high quality. curve that interpolates a given set of points is to use a clothoid spline,

In the planar case, one possibility to create a high quality. curve that interpolates a given set of points is to use a clothoid spline, Dscrete Farng of Curves and Surfaces Based on Lnear Curvature Dstrbuton R. Schneder and L. Kobbelt Abstract. In the planar case, one possblty to create a hgh qualty curve that nterpolates a gven set of

More information

SENSITIVITY ANALYSIS IN LINEAR PROGRAMMING USING A CALCULATOR

SENSITIVITY ANALYSIS IN LINEAR PROGRAMMING USING A CALCULATOR SENSITIVITY ANALYSIS IN LINEAR PROGRAMMING USING A CALCULATOR Judth Aronow Rchard Jarvnen Independent Consultant Dept of Math/Stat 559 Frost Wnona State Unversty Beaumont, TX 7776 Wnona, MN 55987 aronowju@hal.lamar.edu

More information

Simulation of a Ship with Partially Filled Tanks Rolling in Waves by Applying Moving Particle Semi-Implicit Method

Simulation of a Ship with Partially Filled Tanks Rolling in Waves by Applying Moving Particle Semi-Implicit Method Smulaton of a Shp wth Partally Flled Tanks Rollng n Waves by Applyng Movng Partcle Sem-Implct Method Jen-Shang Kouh Department of Engneerng Scence and Ocean Engneerng, Natonal Tawan Unversty, Tape, Tawan,

More information

An Optimal Algorithm for Prufer Codes *

An Optimal Algorithm for Prufer Codes * J. Software Engneerng & Applcatons, 2009, 2: 111-115 do:10.4236/jsea.2009.22016 Publshed Onlne July 2009 (www.scrp.org/journal/jsea) An Optmal Algorthm for Prufer Codes * Xaodong Wang 1, 2, Le Wang 3,

More information

Classification / Regression Support Vector Machines

Classification / Regression Support Vector Machines Classfcaton / Regresson Support Vector Machnes Jeff Howbert Introducton to Machne Learnng Wnter 04 Topcs SVM classfers for lnearly separable classes SVM classfers for non-lnearly separable classes SVM

More information

Hybrid Non-Blind Color Image Watermarking

Hybrid Non-Blind Color Image Watermarking Hybrd Non-Blnd Color Image Watermarkng Ms C.N.Sujatha 1, Dr. P. Satyanarayana 2 1 Assocate Professor, Dept. of ECE, SNIST, Yamnampet, Ghatkesar Hyderabad-501301, Telangana 2 Professor, Dept. of ECE, AITS,

More information

Edge Detection in Noisy Images Using the Support Vector Machines

Edge Detection in Noisy Images Using the Support Vector Machines Edge Detecton n Nosy Images Usng the Support Vector Machnes Hlaro Gómez-Moreno, Saturnno Maldonado-Bascón, Francsco López-Ferreras Sgnal Theory and Communcatons Department. Unversty of Alcalá Crta. Madrd-Barcelona

More information

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization Problem efntons and Evaluaton Crtera for Computatonal Expensve Optmzaton B. Lu 1, Q. Chen and Q. Zhang 3, J. J. Lang 4, P. N. Suganthan, B. Y. Qu 6 1 epartment of Computng, Glyndwr Unversty, UK Faclty

More information

Modeling of the Absorption of the Electromagnetic Wave Energy in the Human Head Induced by Cell Phone

Modeling of the Absorption of the Electromagnetic Wave Energy in the Human Head Induced by Cell Phone Journal of Appled Mathematcs and Physcs, 14,, 179-184 Publshed Onlne November 14 n ScRes. http://www.scrp.org/ournal/amp http://dx.do.org/1.436/amp.14.114 Modelng of the Absorpton of the Electromagnetc

More information

Kiran Joy, International Journal of Advanced Engineering Technology E-ISSN

Kiran Joy, International Journal of Advanced Engineering Technology E-ISSN Kran oy, nternatonal ournal of Advanced Engneerng Technology E-SS 0976-3945 nt Adv Engg Tech/Vol. V/ssue /Aprl-une,04/9-95 Research Paper DETERMATO O RADATVE VEW ACTOR WTOUT COSDERG TE SADOWG EECT Kran

More information

Feature Reduction and Selection

Feature Reduction and Selection Feature Reducton and Selecton Dr. Shuang LIANG School of Software Engneerng TongJ Unversty Fall, 2012 Today s Topcs Introducton Problems of Dmensonalty Feature Reducton Statstc methods Prncpal Components

More information

X- Chart Using ANOM Approach

X- Chart Using ANOM Approach ISSN 1684-8403 Journal of Statstcs Volume 17, 010, pp. 3-3 Abstract X- Chart Usng ANOM Approach Gullapall Chakravarth 1 and Chaluvad Venkateswara Rao Control lmts for ndvdual measurements (X) chart are

More information

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009.

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009. Farrukh Jabeen Algorthms 51 Assgnment #2 Due Date: June 15, 29. Assgnment # 2 Chapter 3 Dscrete Fourer Transforms Implement the FFT for the DFT. Descrbed n sectons 3.1 and 3.2. Delverables: 1. Concse descrpton

More information

Module 6: FEM for Plates and Shells Lecture 6: Finite Element Analysis of Shell

Module 6: FEM for Plates and Shells Lecture 6: Finite Element Analysis of Shell Module 6: FEM for Plates and Shells Lecture 6: Fnte Element Analyss of Shell 3 6.6. Introducton A shell s a curved surface, whch by vrtue of ther shape can wthstand both membrane and bendng forces. A shell

More information

A Newton-Type Method for Constrained Least-Squares Data-Fitting with Easy-to-Control Rational Curves

A Newton-Type Method for Constrained Least-Squares Data-Fitting with Easy-to-Control Rational Curves A Newton-Type Method for Constraned Least-Squares Data-Fttng wth Easy-to-Control Ratonal Curves G. Cascola a, L. Roman b, a Department of Mathematcs, Unversty of Bologna, P.zza d Porta San Donato 5, 4017

More information

Overview. Basic Setup [9] Motivation and Tasks. Modularization 2008/2/20 IMPROVED COVERAGE CONTROL USING ONLY LOCAL INFORMATION

Overview. Basic Setup [9] Motivation and Tasks. Modularization 2008/2/20 IMPROVED COVERAGE CONTROL USING ONLY LOCAL INFORMATION Overvew 2 IMPROVED COVERAGE CONTROL USING ONLY LOCAL INFORMATION Introducton Mult- Smulator MASIM Theoretcal Work and Smulaton Results Concluson Jay Wagenpfel, Adran Trachte Motvaton and Tasks Basc Setup

More information

Wavefront Reconstructor

Wavefront Reconstructor A Dstrbuted Smplex B-Splne Based Wavefront Reconstructor Coen de Vsser and Mchel Verhaegen 14-12-201212 2012 Delft Unversty of Technology Contents Introducton Wavefront reconstructon usng Smplex B-Splnes

More information

SVM-based Learning for Multiple Model Estimation

SVM-based Learning for Multiple Model Estimation SVM-based Learnng for Multple Model Estmaton Vladmr Cherkassky and Yunqan Ma Department of Electrcal and Computer Engneerng Unversty of Mnnesota Mnneapols, MN 55455 {cherkass,myq}@ece.umn.edu Abstract:

More information

Helsinki University Of Technology, Systems Analysis Laboratory Mat Independent research projects in applied mathematics (3 cr)

Helsinki University Of Technology, Systems Analysis Laboratory Mat Independent research projects in applied mathematics (3 cr) Helsnk Unversty Of Technology, Systems Analyss Laboratory Mat-2.08 Independent research projects n appled mathematcs (3 cr) "! #$&% Antt Laukkanen 506 R ajlaukka@cc.hut.f 2 Introducton...3 2 Multattrbute

More information

SLAM Summer School 2006 Practical 2: SLAM using Monocular Vision

SLAM Summer School 2006 Practical 2: SLAM using Monocular Vision SLAM Summer School 2006 Practcal 2: SLAM usng Monocular Vson Javer Cvera, Unversty of Zaragoza Andrew J. Davson, Imperal College London J.M.M Montel, Unversty of Zaragoza. josemar@unzar.es, jcvera@unzar.es,

More information

Harmonic Coordinates for Character Articulation PIXAR

Harmonic Coordinates for Character Articulation PIXAR Harmonc Coordnates for Character Artculaton PIXAR Pushkar Josh Mark Meyer Tony DeRose Bran Green Tom Sanock We have a complex source mesh nsde of a smpler cage mesh We want vertex deformatons appled to

More information

AMath 483/583 Lecture 21 May 13, Notes: Notes: Jacobi iteration. Notes: Jacobi with OpenMP coarse grain

AMath 483/583 Lecture 21 May 13, Notes: Notes: Jacobi iteration. Notes: Jacobi with OpenMP coarse grain AMath 483/583 Lecture 21 May 13, 2011 Today: OpenMP and MPI versons of Jacob teraton Gauss-Sedel and SOR teratve methods Next week: More MPI Debuggng and totalvew GPU computng Read: Class notes and references

More information

A gradient smoothing method (GSM) for fluid dynamics problems

A gradient smoothing method (GSM) for fluid dynamics problems INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS Int. J. Numer. Meth. Fluds 2008; 58:1101 1133 Publshed onlne 27 March 2008 n Wley InterScence (www.nterscence.wley.com)..1788 A gradent smoothng method

More information

MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION

MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION Paulo Quntlano 1 & Antono Santa-Rosa 1 Federal Polce Department, Brasla, Brazl. E-mals: quntlano.pqs@dpf.gov.br and

More information

Sum of Linear and Fractional Multiobjective Programming Problem under Fuzzy Rules Constraints

Sum of Linear and Fractional Multiobjective Programming Problem under Fuzzy Rules Constraints Australan Journal of Basc and Appled Scences, 2(4): 1204-1208, 2008 ISSN 1991-8178 Sum of Lnear and Fractonal Multobjectve Programmng Problem under Fuzzy Rules Constrants 1 2 Sanjay Jan and Kalash Lachhwan

More information

Multiblock method for database generation in finite element programs

Multiblock method for database generation in finite element programs Proc. of the 9th WSEAS Int. Conf. on Mathematcal Methods and Computatonal Technques n Electrcal Engneerng, Arcachon, October 13-15, 2007 53 Multblock method for database generaton n fnte element programs

More information

An Iterative Solution Approach to Process Plant Layout using Mixed Integer Optimisation

An Iterative Solution Approach to Process Plant Layout using Mixed Integer Optimisation 17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 An Iteratve Soluton Approach to Process Plant Layout usng Mxed

More information

Programming in Fortran 90 : 2017/2018

Programming in Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Exercse 1 : Evaluaton of functon dependng on nput Wrte a program who evaluate the functon f (x,y) for any two user specfed values

More information

Categories and Subject Descriptors B.7.2 [Integrated Circuits]: Design Aids Verification. General Terms Algorithms

Categories and Subject Descriptors B.7.2 [Integrated Circuits]: Design Aids Verification. General Terms Algorithms 3. Fndng Determnstc Soluton from Underdetermned Equaton: Large-Scale Performance Modelng by Least Angle Regresson Xn L ECE Department, Carnege Mellon Unversty Forbs Avenue, Pttsburgh, PA 3 xnl@ece.cmu.edu

More information

LECTURE : MANIFOLD LEARNING

LECTURE : MANIFOLD LEARNING LECTURE : MANIFOLD LEARNING Rta Osadchy Some sldes are due to L.Saul, V. C. Raykar, N. Verma Topcs PCA MDS IsoMap LLE EgenMaps Done! Dmensonalty Reducton Data representaton Inputs are real-valued vectors

More information

6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour

6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour 6.854 Advanced Algorthms Petar Maymounkov Problem Set 11 (November 23, 2005) Wth: Benjamn Rossman, Oren Wemann, and Pouya Kheradpour Problem 1. We reduce vertex cover to MAX-SAT wth weghts, such that the

More information

NAG Fortran Library Chapter Introduction. G10 Smoothing in Statistics

NAG Fortran Library Chapter Introduction. G10 Smoothing in Statistics Introducton G10 NAG Fortran Lbrary Chapter Introducton G10 Smoothng n Statstcs Contents 1 Scope of the Chapter... 2 2 Background to the Problems... 2 2.1 Smoothng Methods... 2 2.2 Smoothng Splnes and Regresson

More information

An Efficient Genetic Algorithm with Fuzzy c-means Clustering for Traveling Salesman Problem

An Efficient Genetic Algorithm with Fuzzy c-means Clustering for Traveling Salesman Problem An Effcent Genetc Algorthm wth Fuzzy c-means Clusterng for Travelng Salesman Problem Jong-Won Yoon and Sung-Bae Cho Dept. of Computer Scence Yonse Unversty Seoul, Korea jwyoon@sclab.yonse.ac.r, sbcho@cs.yonse.ac.r

More information

An adaptive gradient smoothing method (GSM) for fluid dynamics problems

An adaptive gradient smoothing method (GSM) for fluid dynamics problems INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS Int. J. Numer. Meth. Fluds 2010; 62:499 529 Publshed onlne 17 March 2009 n Wley InterScence (www.nterscence.wley.com)..2032 An adaptve gradent smoothng

More information

Classifier Selection Based on Data Complexity Measures *

Classifier Selection Based on Data Complexity Measures * Classfer Selecton Based on Data Complexty Measures * Edth Hernández-Reyes, J.A. Carrasco-Ochoa, and J.Fco. Martínez-Trndad Natonal Insttute for Astrophyscs, Optcs and Electroncs, Lus Enrque Erro No.1 Sta.

More information

The Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique

The Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique //00 :0 AM Outlne and Readng The Greedy Method The Greedy Method Technque (secton.) Fractonal Knapsack Problem (secton..) Task Schedulng (secton..) Mnmum Spannng Trees (secton.) Change Money Problem Greedy

More information

Lecture 4: Principal components

Lecture 4: Principal components /3/6 Lecture 4: Prncpal components 3..6 Multvarate lnear regresson MLR s optmal for the estmaton data...but poor for handlng collnear data Covarance matrx s not nvertble (large condton number) Robustness

More information

Cluster Analysis of Electrical Behavior

Cluster Analysis of Electrical Behavior Journal of Computer and Communcatons, 205, 3, 88-93 Publshed Onlne May 205 n ScRes. http://www.scrp.org/ournal/cc http://dx.do.org/0.4236/cc.205.350 Cluster Analyss of Electrcal Behavor Ln Lu Ln Lu, School

More information

Load-Balanced Anycast Routing

Load-Balanced Anycast Routing Load-Balanced Anycast Routng Chng-Yu Ln, Jung-Hua Lo, and Sy-Yen Kuo Department of Electrcal Engneerng atonal Tawan Unversty, Tape, Tawan sykuo@cc.ee.ntu.edu.tw Abstract For fault-tolerance and load-balance

More information

The Research of Ellipse Parameter Fitting Algorithm of Ultrasonic Imaging Logging in the Casing Hole

The Research of Ellipse Parameter Fitting Algorithm of Ultrasonic Imaging Logging in the Casing Hole Appled Mathematcs, 04, 5, 37-3 Publshed Onlne May 04 n ScRes. http://www.scrp.org/journal/am http://dx.do.org/0.436/am.04.584 The Research of Ellpse Parameter Fttng Algorthm of Ultrasonc Imagng Loggng

More information

Parallel Numerics. 1 Preconditioning & Iterative Solvers (From 2016)

Parallel Numerics. 1 Preconditioning & Iterative Solvers (From 2016) Technsche Unverstät München WSe 6/7 Insttut für Informatk Prof. Dr. Thomas Huckle Dpl.-Math. Benjamn Uekermann Parallel Numercs Exercse : Prevous Exam Questons Precondtonng & Iteratve Solvers (From 6)

More information

SENSITIVITY ANALYSIS WITH UNSTRUCTURED FREE MESH GENERATORS IN 2-D AND 3-D SHAPE OPTIMIZATION.

SENSITIVITY ANALYSIS WITH UNSTRUCTURED FREE MESH GENERATORS IN 2-D AND 3-D SHAPE OPTIMIZATION. SENSITIVITY ANALYSIS WITH UNSTRUCTURED FREE MESH GENERATORS IN 2-D AND 3-D SHAPE OPTIMIZATION. P. Duysnx, W.H. Zhang, C. Fleury. Aerospace Laboratory, LTAS, Unversty of Lège B-4000 LIEGE, BELGIUM. ABSTRACT.

More information

Term Weighting Classification System Using the Chi-square Statistic for the Classification Subtask at NTCIR-6 Patent Retrieval Task

Term Weighting Classification System Using the Chi-square Statistic for the Classification Subtask at NTCIR-6 Patent Retrieval Task Proceedngs of NTCIR-6 Workshop Meetng, May 15-18, 2007, Tokyo, Japan Term Weghtng Classfcaton System Usng the Ch-square Statstc for the Classfcaton Subtask at NTCIR-6 Patent Retreval Task Kotaro Hashmoto

More information

Subspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points;

Subspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points; Subspace clusterng Clusterng Fundamental to all clusterng technques s the choce of dstance measure between data ponts; D q ( ) ( ) 2 x x = x x, j k = 1 k jk Squared Eucldean dstance Assumpton: All features

More information

Polyhedral Compilation Foundations

Polyhedral Compilation Foundations Polyhedral Complaton Foundatons Lous-Noël Pouchet pouchet@cse.oho-state.edu Dept. of Computer Scence and Engneerng, the Oho State Unversty Feb 8, 200 888., Class # Introducton: Polyhedral Complaton Foundatons

More information

Vectorization of Image Outlines Using Rational Spline and Genetic Algorithm

Vectorization of Image Outlines Using Rational Spline and Genetic Algorithm 01 Internatonal Conference on Image, Vson and Computng (ICIVC 01) IPCSIT vol. 50 (01) (01) IACSIT Press, Sngapore DOI: 10.776/IPCSIT.01.V50.4 Vectorzaton of Image Outlnes Usng Ratonal Splne and Genetc

More information

Multiobjective fuzzy optimization method

Multiobjective fuzzy optimization method Buletnul Ştnţfc al nverstăţ "Poltehnca" dn Tmşoara Sera ELECTRONICĂ ş TELECOMNICAŢII TRANSACTIONS on ELECTRONICS and COMMNICATIONS Tom 49(63, Fasccola, 24 Multobjectve fuzzy optmzaton method Gabrel Oltean

More information

Network Coding as a Dynamical System

Network Coding as a Dynamical System Network Codng as a Dynamcal System Narayan B. Mandayam IEEE Dstngushed Lecture (jont work wth Dan Zhang and a Su) Department of Electrcal and Computer Engneerng Rutgers Unversty Outlne. Introducton 2.

More information

RECENT research on structured mesh flow solver for aerodynamic problems shows that for practical levels of

RECENT research on structured mesh flow solver for aerodynamic problems shows that for practical levels of A Hgh-Order Accurate Unstructured GMRES Algorthm for Invscd Compressble Flows A. ejat * and C. Ollver-Gooch Department of Mechancal Engneerng, The Unversty of Brtsh Columba, 054-650 Appled Scence Lane,

More information

Parameterization of Quadrilateral Meshes

Parameterization of Quadrilateral Meshes Parameterzaton of Quadrlateral Meshes L Lu 1, CaMng Zhang 1,, and Frank Cheng 3 1 School of Computer Scence and Technology, Shandong Unversty, Jnan, Chna Department of Computer Scence and Technology, Shandong

More information

A HIGH-ORDER SPECTRAL (FINITE) VOLUME METHOD FOR CONSERVATION LAWS ON UNSTRUCTURED GRIDS

A HIGH-ORDER SPECTRAL (FINITE) VOLUME METHOD FOR CONSERVATION LAWS ON UNSTRUCTURED GRIDS AIAA-00-058 A HIGH-ORDER SPECTRAL (FIITE) VOLUME METHOD FOR COSERVATIO LAWS O USTRUCTURED GRIDS Z.J. Wang Department of Mechancal Engneerng Mchgan State Unversty, East Lansng, MI 88 Yen Lu * MS T7B-, ASA

More information

A Fast Content-Based Multimedia Retrieval Technique Using Compressed Data

A Fast Content-Based Multimedia Retrieval Technique Using Compressed Data A Fast Content-Based Multmeda Retreval Technque Usng Compressed Data Borko Furht and Pornvt Saksobhavvat NSF Multmeda Laboratory Florda Atlantc Unversty, Boca Raton, Florda 3343 ABSTRACT In ths paper,

More information

R s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes

R s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes SPH3UW Unt 7.3 Sphercal Concave Mrrors Page 1 of 1 Notes Physcs Tool box Concave Mrror If the reflectng surface takes place on the nner surface of the sphercal shape so that the centre of the mrror bulges

More information

A Robust LS-SVM Regression

A Robust LS-SVM Regression PROCEEDIGS OF WORLD ACADEMY OF SCIECE, EGIEERIG AD ECHOLOGY VOLUME 7 AUGUS 5 ISS 37- A Robust LS-SVM Regresson József Valyon, and Gábor Horváth Abstract In comparson to the orgnal SVM, whch nvolves a quadratc

More information