Two Uncertain Programming Models for Inverse Minimum Spanning Tree Problem

Size: px
Start display at page:

Download "Two Uncertain Programming Models for Inverse Minimum Spanning Tree Problem"

Transcription

1 Idustral Egeerg & Maageet Systes Vol, No, March 3, pp.9-5 ISSN EISSN KIIE Two Ucerta Prograg Models for Iverse Mu Spag Tree Proble Xag Zhag, Qa Wag, Ja Zhou School of Maageet, Shagha Uversty, Shagha, Cha (Receved: August 5, / Revsed: Deceber 3, / Accepted: March 5, 3 ABSTRACT A verse u spag tree proble akes the least odfcato o the edge weghts such that a predetered spag tree s a u spag tree wth respect to the ew edge weghts. I ths paper, the cocept of ucerta α-u spag tree s tated for u spag tree proble wth ucerta edge weghts. Usg dfferet decso crtera, two ucerta prograg odels are preseted to forulate a specfc verse u spag tree proble wth ucerta edge weghts volvg a su-type odel ad a a-type odel. By eas of the operatoal law of depedet ucerta varables, the two ucerta prograg odels are trasfored to ther equvalet deterstc odels whch ca be solved by classc optzato ethods. Fally, soe uercal eaples o a traffc etwork recostructo proble are put forward to llustrate the effectveess of the proposed odels. Keywords: Mu Spag Tree, Ucerta Mu Spag Tree, Iverse Optzato, Ucerta Prograg Correspodg Author, E-al: zhou_a@shu.edu.c. INTRODUCTION The verse optzato proble s a subect etesvely studed the cotet of toographc studes, sesc wave propagato, ad a wde rage of statstcal ferece wth pror probles. The verse u spag tree (IMST proble s a type of verse optzato probles. I a IMST proble, a coected graph wth edge weghts s cosdered. The obectve of IMST proble s to odfy the weghts so that a predetered spag tree s a u spag tree wth respect to the ew weghts, ad sultaeously the total odfcato of weghts s a u. The IMST proble was frst studed by Zhag et al. (996. Followg that, uch research work has bee doe othe IMST proble sce ay applcatos ca be trasfored to ths proble (Farago et al., 3; Gua ad Zhag, 7; Wag et al., 6; Yag ad Zhag, 7. Ad ay effcet algorths have bee developed for solvg the classc IMST probles ad ther dervatves (Ahua ad Orl, ; He et al., 5; Zhag et al., 6. I vew of the odeteracy of soe paraeters applcatos, soe edeavor was doe to deal wth IMST probles wth deterate forato the lteratures. For eaple, Zhag ad Zhou (6 cosdered the IMST proble whe the edge weghts were assued to be stochastc varables, ad stochastc prograg odels together wth hybrd tellget algorths were preseted for IMST probles. I practce, however, t s ot approprate to set the edge weghts as rado ubers soe cases due to a lack of observed data (Peg ad L, ; Zhou ad Peg,. Hece, we adopt the ucertaty theory, a brach of aoatc atheatcs for odelg hua ucertaty fouded by Lu (7, to hadle ths proble. I ths paper, a specfc IMST proble s dscussed uder the assupto of ucerta edge weghts. Ths paper proposes a ew cocept of ucerta α-u

2 Zhag, Wag, ad Zhou: Idustral Egeerg & Maageet Systes Vol, No, March 3, pp.9-5, 3 KIIE spag tree ad develops two ucerta prograg odels to forulate ths proble accordg to dfferet decso crtera. I ths paper, the IMST proble wth ucerta edge weghts s referred to as a ucerta verse u spag tree (UIMST proble for coveece. The rest of ths paper s orgazed as follows. Secto brefly revews the prelary cocepts of ucertaty theory. Secto 3 troduces the classc IMST proble ad the atheatcal descrpto of UIMST proble, ad the proposes a cocept of ucerta α- u spag tree. I Secto 4, two ucerta prograg odels are gve based o dfferet decso obectves. Followg that, Secto 5 presets the uercal eaples ters of the two ucerta odels. Fally, coclusos are draw Secto 6.. PRELIMINARIES Ucertaty theory provdes a ew approach to deal wth deteracy factors whe there s a lack of observed data (Lu, 7,. Nowadays, ucertaty theory has becoe a brach of aoatc atheatcs for odelg hua ucertaty, wdely appled ay research areas (Che, ; L ad Peg, ; Sheg ad Yao, ; Xu ad Zhu,. Ths secto s teded to revew soe basc cocepts ucertaty theory whch wll be used to establsh ucerta prograg odels for the UIMST proble. where Λ k are arbtrarly chose evets fro L k for k =,, L, respectvely. A ucerta varable ξ s essetally a easurable fucto fro a ucertaty space to the set of real ubers. I order to descrbe a ucerta varable practce, Lu (7 defed a cocept of ucertaty dstrbuto as follows. Defto (Lu, 7. Let ξ be a ucerta varable. The, ts ucertaty dstrbuto s defed by ( M{ ξ } Φ = ( for ay real uber. Furtherore, Peg ad Iwaura ( showed that a fucto M: R [, ] s a ucertaty dstrbuto f ad oly f t s a ootoe creasg fucto ecept Φ( ad Φ(. For stace, a ucerta varable ξ s called lear f t has a lear ucertaty dstrbuto (Fgure,, f a Φ ( = ( a/( b a f a b, f b deoted by ξ : Lab (,, where a ad b are real ubers wth a < b. Defto (Lu, 7. Let L be a σ -algebra o a oepty set Γ. A set fucto M: L [, ] s called a ucerta easure f t satsfes the followg aos: Ao (Noralty Ao. M{ L } = for the uversal set Γ ; c Ao (Dualty Ao. M{ Λ } + M{ Λ }= for ay evet Λ ; Ao 3 (Subaddtvty Ao. For every coutable sequece of evets Λ, Λ, L, we have M { U Λ} M{ Λ}. = The trplet ( Γ, L, M s called a ucertaty space. Besdes, the product ucerta easure o the product σ -algebra was defed by Lu (9 va the followg product ao: Ao 4 (Product Ao. Let ( Γ k, Lk, M k be ucertaty spaces for k =,, L. The product ucerta easure M s a ucerta easure satsfyg M = Λ = M Λ { } k k k k =, Fgure. Lear ucertaty dstrbuto. A ucertaty dstrbuto Φ s sad to be regular f ts verse fucto Φ ( α ests ad s uque for each α (,. It s clear that a lear ucertaty dstrbuto La (, b s regular, ad ts verse ucertaty dstrbuto s Φ ( α = ( α a + ab. ( The verse ucertaty dstrbuto plays a portat role the operatos of depedet ucerta varables. Defto 3 (Lu, 9. The ucerta varables ξ, ξ L, ξ are sad to be depedet f,

3 Two Ucerta Prograg Models for Iverse Mu Spag Tree Proble Vol, No, March 3, pp.9-5, 3 KIIE for ay Borel sets M B M B = = I ( ξ = { ξ } (3 B, B, L, B of real ubers. Theore (Lu,. Let ξ, ξ, L, ξ be depedet ucerta varables wth regular ucertaty dstrbutos Φ, Φ, L, Φ, respectvely. If the fucto f (,,, L s strctly creasg wth respect to,, L, k ad strctly decreasg wth respect to k+, k+, L,, the ξ = f ( ξ, ξ, L, ξ s a ucerta varable wth verse ucertaty dstrbuto Ψ ( α = f ( Φ ( α, L, Φ ( α, Φ ( α, L, Φ ( α. k k+ 3. UNCERTAIN INVERSE MINIMUM SPANNING TREE PROBLEM I ths secto, a classc cocept of u spag tree as well as a path optalty codto s revewed brefly, ad the a UIMST proble s talzed by troducg ts applcato backgrouds ad atheatcal descrpto. Fally, a ew cocept of ucerta α -u spag tree s preseted. 3. Classc IMST Proble Defto 4 (Mu Spag Tree. Gve a coected graph G = ( V, E wth edge weghts, E{,, L, }, a spag tree T s sad to be a u spag tree f (5 T T holds for ay spag tree T. I a classc IMST proble, a predetered spag tree T s gve. The obectve of IMST proble s to fd soe ew edge weghts such that T s a u spag tree wth respect to the ew edge weghts ad accordgly the odfcato of edge weghts s a u. Fgure. A eaple of verse u spag tree proble. I order to provde the atheatcal descrpto of IMST proble, soe otos are proposed as follows. Frstly, we refer to the edges the gve spag tree T as tree edges, ad the edges ot T as o-tree edges. Hece the set of all the o-tree edges s E \ T. I the spag tree T, there s a uque path betwee the two vertces of ay o-tree edge, referred to as tree path of edge ad deoted by P. A eaple of classc IMST proble wth 6 vertces ad edges s show Fgure, where c ad deote the orgal ad ew weghts o edge, ad the sold le represets a gve spag tree T. The set of o-tree edges s E \ T = { 6, 7,8,9, }, ad the tree path of o-tree edge BD s AB-AE-DE,.e., P 9 = {, 3, 5 }. Moreover, Ahua et al. (993 proved a equvalet codto of u spag tree, called a path optalty codto as follows. Theore (Ahua et al., 993. T s a u spag tree wth respect to the edge weghts f ad oly f E T P (6, \, where tree path of edge. Accordg to Theore, the classc IMST proble ca be forulated as the followg odel, E \ T s the set of o-tree edges, ad -c = subect to : E T P, \, P s the (7 where c ad are the orgal ad ew weghts of edge, E, respectvely. Note that the obectve fucto c = ca be replaced wth soe other obectve fuctos f ecessary (Sokkalga et al., Applcato Backgrouds May recostructo probles practce ca be trasfored to ucerta probles. Let us cosder a LAN recostructo proble as follows. Much research work shows that the spag tree structure s the best topology for telecoucato etwork desgs (Kershebau, 993, especally coputer etwork systes. LANs are cooly used as a coucato frastructure that eets the deads of users a local evroet. These coputer etworks typcally cosst of several LAN segets coected va brdges. Suppose that there s a old LAN, whch several servce ceters are tercoected va brdges. Because of the treedous etwork cogesto, the badwdths o brdges ust be odfed. The decso-aker hopes that a predetered spag tree becoes a u

4 Zhag, Wag, ad Zhou: Idustral Egeerg & Maageet Systes Vol, No, March 3, pp.9-5, 3 KIIE spag tree wth respect to the travelg te (whch eas hgh et-speed betwee the a servce ceters. Also the total badwdth odfcato should be zed so as to dsh the cost of recostructo. Sce the travelg tes as well as the et speeds are related to badwdths, t s atural to descrbe the travelg te o a brdge as a ucerta uber stead of a deterstc oe wth respect to badwdths of brdges whe there are o forer statstcal data. Ths s a typcal verse spag tree proble wth ucerta weghts,.e., a UIST proble. 3.3 Notatos ad Proble Descrpto I ths paper, a specfc IMST proble wth ucerta edge weghts s vestgated. I order to provde a atheatcal descrpto for ths proble, the followg otatos are used: G = ( V, E : a coected graph wth set of vertces V ad edge set E = {,, L, } ; T : a predetered spag tree of G; c : the orgal edge weghts, E; : decso varables represetg the ew edge weghts, E; ξ ( : the ucerta edge weghts wth respect to, E. For our purpose, we assue that c, E, whch s practcal ay stuatos. For stace, a traffc syste recostructo proble, the roads are ofte requred to be broadeed stead of beg arrowed order for accoodatg the creasg traffc flow. Hece the obectve of UIMST proble here s to fd a ew edge weght vector to ze the odfcato (, c = ad sultaeously T s a u spag tree wth respect to the ucerta edge weghts ξ (, E. 3.4 Ucerta α-u Spag Tree I a UIMST proble, Defto 4 becoes powerless due to the ucertaty of edge weghts ξ. Therefore, before odelg the UIMST proble, a u spag tree wth respect to ucerta weghts ust be defed frst. I ths secto, by usg the ucertaty easure (see Secto, a ew cocept of ucerta α - u spag tree s recoeded as follows. Defto 5 (Ucerta α -Mu Spag Tree. Gve a coected graph G = ( V, E wth ucerta edge weghts ξ, E, ad a gve cofdece level α, a spag tree T s sad to be a ucerta α -u spag tree f M ξ ξ holds for ay spag tree T. (8 T T Defto 5 ples that a ucerta α -u spag tree has a chace ot less tha α of ot havg a ucerta weght larger tha every other spag tree, whch s tutvely reasoable. As troduced Secto 3., Theore s a ecessary ad suffcet codto of u spag tree, whch provdes a useful approach for odelg a IMST proble. I the UIMST proble, a slar result ca be obtaed for ucerta α -u spag tree by adoptg oly a ty chage as follows. Theore 3. T s a ucerta α -u spag tree wth respect to the ucerta edge weghts f ad oly f { ξ ξ } α M ( (, E \ T, P, (9 where E \ T s the set of o-tree edges, ad tree path of edge. P s the Proof. It follows drectly fro Theore ad Defto UNCERTAIN PROGRAMMING MODELS Based o the cocept of ucerta α -u spag tree ad Theore 3, two ucerta prograg odels are bult up for the UIMST proble ths secto cludg a ucerta su-type odel ad a ucerta a-type odel. Furtherore, the operatoal law of depedet ucerta varables,.e., Theore Secto, s used to derve two equvalet deterstc odels. 4. Ucerta Su-Type Model Let us cosder a traffc etwork recostructo proble, where soe roads should be broadeed for soe reasos. The decso-aker hopes that the predetered spag tree becoes a ucerta α -u spag tree wth respect to ucerta travelg tes betwee soe a traffc hubs, where α s provded as a approprate safety arg by the decso-aker. Ad the total odfcato of road wdths s also requred to be zed whch eas decreasg the cost of recostructo. I ths case, a so-called ucerta sutype odel ca be obtaed fro Theore 3 as follows, ( c = subect to : M { ξ ξ } α E T P c, =,, L,. ( (, \, ( where α s a predetered cofdece level. Ths odel eas to ake the least odfcato o the deterstc edge weghts, E, such that a gve spa-

5 Two Ucerta Prograg Models for Iverse Mu Spag Tree Proble Vol, No, March 3, pp.9-5, 3 KIIE 3 g tree becoes a ucerta α -u spag tree wth respect to the ucerta edge weghts ξ, E. By the use of Theore, t s easy to covert odel ( to the followg crsp equvalet odel, ( c = subect to : Φ (, α Φ (, α, E\ T, P c, =,, L, ( where Φ represet the verse dstrbutos of ucerta weghts ξ, =,, L,, whch are related to the ew edge weghts. 4. Ucerta Ma-Type Model Soetes, the decso-aker hopes to ze the aal oe aog all the odfcatos so as to keep a balace of cost durg the syste recostructo. I order to eet ths obectve, a ucerta atype odel ca be establshed as follows, a ( c = subect to : M { ξ ξ } α E T P c, =,, L,. ( (, \, ( The obectve fucto of odel (3 s to ze the largest odfcato all the edges provded that a gve spag tree T becoes a ucerta α - u spag tree regardg the ucerta edge weghts. Utl ow, two ucerta odels together wth ther equvalet deterstc odels are preseted for the UIMST proble accordg to dfferet decso obectves. We ca see that odels ( ad (3 have o dfferece wth classcal atheatcal prograg odels whe the verse ucertaty dstrbutos are kow. Thus, we ay solve the by classcal optzato ethods or tellget algorths. 5. COMPUTATIONAL EXAMPLES I order to llustrate the effectveess of the above two ucerta prograg odels, ths secto, a traffc etwork recostructo proble wth 6 traffc hubs ad roads s cosdered (Fgure 3, wth the sold le represetg the predetered spag tree T. There are three weghts o each road, where c ad are the orgal ad ew wdths of road, respectvely, ad ξ deote the ucerta travelg tes o road, whch are assued to be lear ucerta varables oly wth respect to,.e., ξ = ξ( = L(, + a (Table. Two ucerta odels are gve to forulate ths proble ad the solved by MATLAB (MathWorks, Natck, MA, USA. Slarly, usg the verse ucertaty dstrbutos Φ of edge weghts, E, a equvalet deterstc odel ca be obtaed as follows, a ( c = subect to : Φ (, α Φ (, α, E\ T, P c, =,, L,. (3 Fgure 3. Ucerta verse u spag tree proble for coputatoal eaples. Table. Edge weghts for UIMST proble Fgure 3 Edge Orgal weght Paraeter weght Ucerta weght c a ξ = L(, + a 5 (-, (-, (- 3, (- 4, (- 5, (- 6, (- 7, (- 8, (- 9, (-, 3- UMIST: ucerta verse u spag tree.

6 Zhag, Wag, ad Zhou: Idustral Egeerg & Maageet Systes Vol, No, March 3, pp.9-5, 3 KIIE 4 Eaple. Frstly, whe the su-type odel ( s used, the obectve s to ze the total odfcato of road wdths wth a gve cofdece level α =.8 so as to ze the total cost of recostructo, the the followg ucerta prograg odel s obtaed, ( c = subect to : Φ (,.8 Φ (,., E\ T, P c, =,, L, (4 where the o-tree edge set E \ T = { 6,7,8,9, }, P s the tree path of o-tree edge, ad Φ are verse ucertaty dstrbutos of ξ, =,, L,. Sce Φ (, = + Φ = + accord-.8.8 a, (,..a g to (, t follows that Φ (,.8 Φ (,. = + +.8a. a. As a result, a equvalet forulato of odel (4 ca be gaed as follows, ( c = subect to : + + a a E T P c, =,, L,.8., \, (5 whch s a lear prograg odel. Hece, we solve t by MATLAB 7. ad get the optal soluto = (, 68, 7, 9, 5, 6, 6, 4, 7, 3 ad the u total odfcato o road wdths s. Eaple. Secodly, by the use of the a-type IMST odel (3, we have the correspodg forulato for ths proble lke: a ( c subect to : + + a a E T P c, =,, L,..8., \, (6 Slarly, wth the help of MATLAB 7., odel (6 s solved easly wth the fal soluto = (, 68, 7,, 57, 6, 6, 4, 7, 65 as well as the optal obectve value 4. By akg a further vestgato o the eperetal results, we have ( c ( c a = a = 4, ( c ( c =, = 38, = = whch ply that both ad are optal solutos of odel (6, whle oly s the optal soluto of odel (5. We ca coclude fro the coputatoal results that odel (6 has ore tha oe optal soluto cludg that of odel (5. 6. CONCLUSION I ths paper, the cocept of ucerta α -u spag tree s tated for ucerta u spag tree based o the ucertaty easure. After that, two ucerta prograg odels are preseted to forulate the verse u spag tree proble wth ucerta edge weghts ad the trasfored to crsp equvalet odels. Fally, the uercal eaples o a traffc syste recostructo proble are put forward to llustrate the effectveess of odels proposed. The results of the coputatoal eaples led us to coclude that the ucerta a-type odel has ore tha oe optal soluto, ad the optal soluto of ucerta su-type odel s also a optal soluto of ucerta a-type odel. The ultple optal solutos Eaple dcate that there are ore tha oe pla for the decso-aker to choose for the recostructo of the traffc etwork. As a copleetary of ths paper, a further study o the aalyss of the solutos s eeded. Soe effcet algorths to deal wth the UIST proble should be also volved the future work. ACKNOWLEDGMENTS Ths work was supported part by a grat fro the Mstry of Educato Fuded Proect for Huates ad Socal Sceces Research (No. JDXF5, ad the Shagha Phlosophy ad Socal Scece Plag Proect (No. BGL6 ad Iovato Progra of Shagha Mucpal Educato Cosso (No. 3ZS65. REFERENCES Ahua, R. K., Magat, T. L., ad Orl, J. B. (993, Network Flows: Theory, Algorths, ad Applcatos, Pretce Hall, Eglewood Clffs, NJ. Ahua, R. K. ad Orl, J. B. (, A faster algorth for the verse spag tree proble, Joural of Algorths, 34(, Che, X. (, Aerca opto prcg forula for ucerta facal arket, Iteratoal Joural of

7 Two Ucerta Prograg Models for Iverse Mu Spag Tree Proble Vol, No, March 3, pp.9-5, 3 KIIE 5 Operatos Research, 8(, 7-3. Farago, A., Szetes, A., ad Szvatovszk, B. (3, Iverse optzato hgh-speed etworks, Dscrete Appled Matheatcs, 9(, Gua, X. ad Zhag, J. (7, Iverse costraed bottleeck probles uder weghted l or, Coputers ad Operatos Research, 34(, He, Y., Zhag, B., ad Yao, E. (5, Weghted verse u spag tree probles uder Hag dstace, Joural of Cobatoral Optzato, 9(, 9-. Kershebau, A. (993, Telecoucato Network Desg Algorths, McGraw-Hll, New York, NY. L, S. ad Peg, J. (, A ew approach to rsk coparso va ucerta easure, Idustral Egeerg & Maageet Systes, (, Lu, B. (7, Ucertaty Theory (d ed., Sprger- Verlag, Berl. Lu, B. (9, Soe research probles ucertaty theory, Joural of Ucerta Systes, 3(, 3-. Lu, B. (, Ucertaty Theory: A Brach of Matheatcs for Modelg Hua Ucertaty, Sprger- Verlag, Berl. Peg, J. ad L, S. (, Spag tree proble of ucerta etwork, Proceedgs of the 3rd Iteratoal Coferece o Coputer Desg ad Applcatos, X a, Shaa, Cha. Peg, Z. ad Iwaura, K. (, A suffcet ad ecessary codto of ucertaty dstrbuto, Joural of Iterdscplary Matheatcs, 3(3, Sheg, Y. ad Yao K. (, Fed charge trasportato proble ad ts ucerta prograg odel, Idustral Egeerg ad Maageet Systes, (, Sokkalga, P. T., Ahua, R. K., ad Orl, J. B. (999, Solvg verse spag tree probles through etwork flow techques, Operatos Research, 47(, Wag, Q., Yag, X., ad Zhag, J. (6, A class of verse doat probles uder weghted l or ad a proved coplety boud for Radzk s algorth, Joural of Global Optzato, 34(4, Xu, X. ad Zhu, Y. (, Ucerta bag-bag cotrol for cotuous te odel, Cyberetcs ad Systes, 43(6, Yag, X. ad Zhag, J. (7, Soe verse -a etwork probles uder weghted l ad l ors wth boud costrats o chages, Joural of Cobatoral Optzato, 3(, Zhag, B., Zhag, J., ad He, Y. (6, Costraed verse u spag tree probles uder the bottleeck-type Hag dstace, Joural of Global Optzato, 34(3, Zhag, J., Lu. Z., ad Ma, Z. (996, O the verse proble of u spag tree wth partto costrats, Matheatcal Methods of Operatos Research, 44(, Zhag, J. ad Zhou, J. (6, Models ad hybrd algorths for verse u spag tree proble wth stochastc edge weghts, World Joural of Modellg ad Sulato, (5, Zhou, C. ad Peg, J. (, Models ad algorth of au flow proble ucerta etwork, Proceedgs of the 3rd Iteratoal Coferece o- Artfcal Itellgece ad Coputatoal Itellgece, Tayua, Sha, Cha, -9.

A New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming

A New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming ppled Matheatcal Sceces Vol 008 o 50 7-80 New Method for Solvg Fuzzy Lear Prograg by Solvg Lear Prograg S H Nasser a Departet of Matheatcs Faculty of Basc Sceces Mazadara Uversty Babolsar Ira b The Research

More information

A Penalty Function Algorithm with Objective Parameters and Constraint Penalty Parameter for Multi-Objective Programming

A Penalty Function Algorithm with Objective Parameters and Constraint Penalty Parameter for Multi-Objective Programming Aerca Joural of Operatos Research, 4, 4, 33-339 Publshed Ole Noveber 4 ScRes http://wwwscrporg/oural/aor http://ddoorg/436/aor4463 A Pealty Fucto Algorth wth Obectve Paraeters ad Costrat Pealty Paraeter

More information

Some Different Perspectives on Linear Least Squares

Some Different Perspectives on Linear Least Squares Soe Dfferet Perspectves o Lear Least Squares A stadard proble statstcs s to easure a respose or depedet varable, y, at fed values of oe or ore depedet varables. Soetes there ests a deterstc odel y f (,,

More information

Algorithms behind the Correlation Setting Window

Algorithms behind the Correlation Setting Window Algorths behd the Correlato Settg Wdow Itroducto I ths report detaled forato about the correlato settg pop up wdow s gve. See Fgure. Ths wdow s obtaed b clckg o the rado butto labelled Kow dep the a scree

More information

KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS. Peter J. Wilcoxen. Impact Research Centre, University of Melbourne.

KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS. Peter J. Wilcoxen. Impact Research Centre, University of Melbourne. KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS by Peter J. Wlcoxe Ipact Research Cetre, Uversty of Melboure Aprl 1989 Ths paper descrbes a ethod that ca be used to resolve cossteces

More information

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

Solving the fuzzy shortest path problem on networks by a new algorithm

Solving the fuzzy shortest path problem on networks by a new algorithm Proceedgs of the 0th WSEAS Iteratoal Coferece o FUZZY SYSTEMS Solvg the fuzzy shortest path proble o etworks by a ew algorth SADOAH EBRAHIMNEJAD a, ad REZA TAVAKOI-MOGHADDAM b a Departet of Idustral Egeerg,

More information

Global Optimization for Solving Linear Non-Quadratic Optimal Control Problems

Global Optimization for Solving Linear Non-Quadratic Optimal Control Problems Joural of Appled Matheatcs ad Physcs 06 4 859-869 http://wwwscrporg/joural/jap ISSN Ole: 37-4379 ISSN Prt: 37-435 Global Optzato for Solvg Lear No-Quadratc Optal Cotrol Probles Jghao Zhu Departet of Appled

More information

International Journal of Mathematical Archive-3(5), 2012, Available online through ISSN

International Journal of Mathematical Archive-3(5), 2012, Available online through   ISSN Iteratoal Joural of Matheatcal Archve-(5,, 88-845 Avalable ole through www.a.fo ISSN 9 546 FULLY FUZZY LINEAR PROGRAMS WITH TRIANGULAR FUZZY NUMERS S. Mohaaselv Departet of Matheatcs, SRM Uversty, Kattaulathur,

More information

A Conventional Approach for the Solution of the Fifth Order Boundary Value Problems Using Sixth Degree Spline Functions

A Conventional Approach for the Solution of the Fifth Order Boundary Value Problems Using Sixth Degree Spline Functions Appled Matheatcs, 1, 4, 8-88 http://d.do.org/1.4/a.1.448 Publshed Ole Aprl 1 (http://www.scrp.org/joural/a) A Covetoal Approach for the Soluto of the Ffth Order Boudary Value Probles Usg Sth Degree Sple

More information

A Mean Deviation Based Method for Intuitionistic Fuzzy Multiple Attribute Decision Making

A Mean Deviation Based Method for Intuitionistic Fuzzy Multiple Attribute Decision Making 00 Iteratoal Coferece o Artfcal Itellgece ad Coputatoal Itellgece A Mea Devato Based Method for Itutostc Fuzzy Multple Attrbute Decso Makg Yeu Xu Busess School HoHa Uversty Nag, Jagsu 0098, P R Cha xuyeoh@63co

More information

An Innovative Algorithmic Approach for Solving Profit Maximization Problems

An Innovative Algorithmic Approach for Solving Profit Maximization Problems Matheatcs Letters 208; 4(: -5 http://www.scecepublshggroup.co/j/l do: 0.648/j.l.208040. ISSN: 2575-503X (Prt; ISSN: 2575-5056 (Ole A Iovatve Algorthc Approach for Solvg Proft Maxzato Probles Abul Kala

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

Capacitated Plant Location Problem:

Capacitated Plant Location Problem: . L. Brcker, 2002 ept of Mechacal & Idustral Egeerg The Uversty of Iowa CPL/ 5/29/2002 page CPL/ 5/29/2002 page 2 Capactated Plat Locato Proble: where Mze F + C subect to = = =, =, S, =,... 0, =, ; =,

More information

D. L. Bricker, 2002 Dept of Mechanical & Industrial Engineering The University of Iowa. CPL/XD 12/10/2003 page 1

D. L. Bricker, 2002 Dept of Mechanical & Industrial Engineering The University of Iowa. CPL/XD 12/10/2003 page 1 D. L. Brcker, 2002 Dept of Mechacal & Idustral Egeerg The Uversty of Iowa CPL/XD 2/0/2003 page Capactated Plat Locato Proble: Mze FY + C X subject to = = j= where Y = j= X D, j =, j X SY, =,... X 0, =,

More information

A New Method for Decision Making Based on Soft Matrix Theory

A New Method for Decision Making Based on Soft Matrix Theory Joural of Scetfc esearch & eports 3(5): 0-7, 04; rtcle o. JS.04.5.00 SCIENCEDOMIN teratoal www.scecedoma.org New Method for Decso Mag Based o Soft Matrx Theory Zhmg Zhag * College of Mathematcs ad Computer

More information

Duality Theory for Interval Linear Programming Problems

Duality Theory for Interval Linear Programming Problems IOSR Joural of Matheatcs (IOSRJM) ISSN: 78-578 Volue 4, Issue 4 (Nov-Dec, ), 9-47 www.osrourals.org Dualty Theory for Iterval Lear Prograg Probles G. Raesh ad K. Gaesa, Departet of Matheatcs, Faculty of

More information

Multiple Attribute Decision Making Based on Interval Number Aggregation Operators Hui LI* and Bing-jiang ZHANG

Multiple Attribute Decision Making Based on Interval Number Aggregation Operators Hui LI* and Bing-jiang ZHANG 206 Iteratoal Coferece o Power, Eergy Egeerg ad Maageet (PEEM 206) ISBN: 978--60595-324-3 Multple Attrbute Decso Makg Based o Iterval Nuber Aggregato Operators Hu LI* ad Bg-jag ZHANG School of Appled Scece,

More information

Unbalanced Bidding Problem with Fuzzy Random Variables

Unbalanced Bidding Problem with Fuzzy Random Variables Iteratoal Busess Research Jauary 009 Ubalaced Bddg Proble wth Fuzzy Rado Varables Dogra Zag Departet of Maths ad Physcs Gul Uversty of techology Ja Ga Road, Gul 54004, Cha Tel: 86-77-589-947 E-al: zagdr@6.co

More information

Some results and conjectures about recurrence relations for certain sequences of binomial sums.

Some results and conjectures about recurrence relations for certain sequences of binomial sums. Soe results ad coectures about recurrece relatos for certa sequeces of boal sus Joha Cgler Faultät für Matheat Uverstät We A-9 We Nordbergstraße 5 Joha Cgler@uveacat Abstract I a prevous paper [] I have

More information

OPTIMALITY CONDITIONS FOR LOCALLY LIPSCHITZ GENERALIZED B-VEX SEMI-INFINITE PROGRAMMING

OPTIMALITY CONDITIONS FOR LOCALLY LIPSCHITZ GENERALIZED B-VEX SEMI-INFINITE PROGRAMMING Mrcea cel Batra Naval Acadey Scetfc Bullet, Volue XIX 6 Issue he joural s dexed : PROQUES / DOAJ / Crossref / EBSCOhost / INDEX COPERNICUS / DRJI / OAJI / JOURNAL INDEX / IOR / SCIENCE LIBRARY INDEX /

More information

Analyzing Fuzzy System Reliability Using Vague Set Theory

Analyzing Fuzzy System Reliability Using Vague Set Theory Iteratoal Joural of Appled Scece ad Egeerg 2003., : 82-88 Aalyzg Fuzzy System Relablty sg Vague Set Theory Shy-Mg Che Departmet of Computer Scece ad Iformato Egeerg, Natoal Tawa versty of Scece ad Techology,

More information

Lecture 8 IEEE DCF Performance

Lecture 8 IEEE DCF Performance Lecture 8 IEEE82. DCF Perforace IEEE82. DCF Basc Access Mechas A stato wth a ew packet to trast otors the chael actvty. If the chael s dle for a perod of te equal to a dstrbuted terfrae space (DIFS), the

More information

PRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION

PRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION PRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION Bars Erkus, 4 March 007 Itroducto Ths docuet provdes a revew of fudaetal cocepts structural dyacs ad soe applcatos hua-duced vbrato aalyss ad tgato of

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

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

Interval extension of Bézier curve

Interval extension of Bézier curve WSEAS TRANSACTIONS o SIGNAL ROCESSING Jucheg L Iterval exteso of Bézer curve JUNCHENG LI Departet of Matheatcs Hua Uversty of Huates Scece ad Techology Dxg Road Loud cty Hua rovce 47 R CHINA E-al: ljucheg8@6co

More information

COMPROMISE HYPERSPHERE FOR STOCHASTIC DOMINANCE MODEL

COMPROMISE HYPERSPHERE FOR STOCHASTIC DOMINANCE MODEL Sebasta Starz COMPROMISE HYPERSPHERE FOR STOCHASTIC DOMINANCE MODEL Abstract The am of the work s to preset a method of rakg a fte set of dscrete radom varables. The proposed method s based o two approaches:

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

2/20/2013. Topics. Power Flow Part 1 Text: Power Transmission. Power Transmission. Power Transmission. Power Transmission

2/20/2013. Topics. Power Flow Part 1 Text: Power Transmission. Power Transmission. Power Transmission. Power Transmission /0/0 Topcs Power Flow Part Text: 0-0. Power Trassso Revsted Power Flow Equatos Power Flow Proble Stateet ECEGR 45 Power Systes Power Trassso Power Trassso Recall that for a short trassso le, the power

More information

International Journal of Mathematical Archive-5(8), 2014, Available online through ISSN

International Journal of Mathematical Archive-5(8), 2014, Available online through   ISSN Iteratoal Joural of Mathematcal Archve-5(8) 204 25-29 Avalable ole through www.jma.fo ISSN 2229 5046 COMMON FIXED POINT OF GENERALIZED CONTRACTION MAPPING IN FUZZY METRIC SPACES Hamd Mottagh Golsha* ad

More information

Summary of the lecture in Biostatistics

Summary of the lecture in Biostatistics Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the

More information

Generalization of the Dissimilarity Measure of Fuzzy Sets

Generalization of the Dissimilarity Measure of Fuzzy Sets Iteratoal Mathematcal Forum 2 2007 o. 68 3395-3400 Geeralzato of the Dssmlarty Measure of Fuzzy Sets Faramarz Faghh Boformatcs Laboratory Naobotechology Research Ceter vesa Research Isttute CECR Tehra

More information

Debabrata Dey and Atanu Lahiri

Debabrata Dey and Atanu Lahiri RESEARCH ARTICLE QUALITY COMPETITION AND MARKET SEGMENTATION IN THE SECURITY SOFTWARE MARKET Debabrata Dey ad Atau Lahr Mchael G. Foster School of Busess, Uersty of Washgto, Seattle, Seattle, WA 9895 U.S.A.

More information

Standard Deviation for PDG Mass Data

Standard Deviation for PDG Mass Data 4 Dec 06 Stadard Devato for PDG Mass Data M. J. Gerusa Retred, 47 Clfde Road, Worghall, HP8 9JR, UK. gerusa@aol.co, phoe: +(44) 844 339754 Abstract Ths paper aalyses the data for the asses of eleetary

More information

Estimation of R= P [Y < X] for Two-parameter Burr Type XII Distribution

Estimation of R= P [Y < X] for Two-parameter Burr Type XII Distribution World Acade of Scece, Egeerg ad Techolog Iteratoal Joural of Matheatcal ad Coputatoal Sceces Vol:4, No:, Estato of R P [Y < X] for Two-paraeter Burr Tpe XII Dstruto H.Paah, S.Asad Iteratoal Scece Ide,

More information

Journal Of Inequalities And Applications, 2008, v. 2008, p

Journal Of Inequalities And Applications, 2008, v. 2008, p Ttle O verse Hlbert-tye equaltes Authors Chagja, Z; Cheug, WS Ctato Joural Of Iequaltes Ad Alcatos, 2008, v. 2008,. 693248 Issued Date 2008 URL htt://hdl.hadle.et/0722/56208 Rghts Ths work s lcesed uder

More information

Symmetry of the Solution of Semidefinite Program by Using Primal-Dual Interior-Point Method

Symmetry of the Solution of Semidefinite Program by Using Primal-Dual Interior-Point Method Syetry of the Soluto of Sedefte Progra by Usg Pral-Dual Iteror-Pot Method Yoshhro Kao Makoto Ohsak ad Naok Katoh Departet of Archtecture ad Archtectural Systes Kyoto Uversty Kyoto 66-85 Japa kao@s-jarchkyoto-uacjp

More information

CHAPTER 4 RADICAL EXPRESSIONS

CHAPTER 4 RADICAL EXPRESSIONS 6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube

More information

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information Malaysa Joural of Mathematcal Sceces (): 97- (9) Bayes Estmator for Expoetal Dstrbuto wth Exteso of Jeffery Pror Iformato Hadeel Salm Al-Kutub ad Noor Akma Ibrahm Isttute for Mathematcal Research, Uverst

More information

Mathematical and Computational Applications, Vol. 20, No. 3, pp ,

Mathematical and Computational Applications, Vol. 20, No. 3, pp , Matheatcal ad oputatoal pplcatos Vol. No. pp. 89-99 http://d.do.org/.99/ca--6 MULTIPLE TTRIBUTE DEISION-MKING MODEL OF GREY TRGET BSED ON POSITIVE ND NEGTIVE BULL S-EYE Sha Fu Departet of Iforato Maageet

More information

Research Article A New Iterative Method for Common Fixed Points of a Finite Family of Nonexpansive Mappings

Research Article A New Iterative Method for Common Fixed Points of a Finite Family of Nonexpansive Mappings Hdaw Publshg Corporato Iteratoal Joural of Mathematcs ad Mathematcal Sceces Volume 009, Artcle ID 391839, 9 pages do:10.1155/009/391839 Research Artcle A New Iteratve Method for Commo Fxed Pots of a Fte

More information

MULTIOBJECTIVE NONLINEAR FRACTIONAL PROGRAMMING PROBLEMS INVOLVING GENERALIZED d - TYPE-I n -SET FUNCTIONS

MULTIOBJECTIVE NONLINEAR FRACTIONAL PROGRAMMING PROBLEMS INVOLVING GENERALIZED d - TYPE-I n -SET FUNCTIONS THE PUBLIHING HOUE PROCEEDING OF THE ROMANIAN ACADEMY, eres A OF THE ROMANIAN ACADEMY Volue 8, Nuber /27,.- MULTIOBJECTIVE NONLINEAR FRACTIONAL PROGRAMMING PROBLEM INVOLVING GENERALIZED d - TYPE-I -ET

More information

Stationary states of atoms and molecules

Stationary states of atoms and molecules Statoary states of atos ad olecules I followg wees the geeral aspects of the eergy level structure of atos ad olecules that are essetal for the terpretato ad the aalyss of spectral postos the rotatoal

More information

On Probability of Undetected Error for Hamming Codes over Q-ary Symmetric Channel

On Probability of Undetected Error for Hamming Codes over Q-ary Symmetric Channel Joural of Coucato ad Coputer 8 (2 259-263 O Probablty of Udetected Error for Hag Codes over Q-ary Syetrc Chael Mash Gupta, Jaskar Sgh Bhullar 2 ad O Parkash Vocha 3. D.A.V. College, Bathda 5, Ida 2. Malout

More information

TRIANGULAR MEMBERSHIP FUNCTIONS FOR SOLVING SINGLE AND MULTIOBJECTIVE FUZZY LINEAR PROGRAMMING PROBLEM.

TRIANGULAR MEMBERSHIP FUNCTIONS FOR SOLVING SINGLE AND MULTIOBJECTIVE FUZZY LINEAR PROGRAMMING PROBLEM. Abbas Iraq Joural of SceceVol 53No 12012 Pp. 125-129 TRIANGULAR MEMBERSHIP FUNCTIONS FOR SOLVING SINGLE AND MULTIOBJECTIVE FUZZY LINEAR PROGRAMMING PROBLEM. Iraq Tarq Abbas Departemet of Mathematc College

More information

Numerical Experiments with the Lagrange Multiplier and Conjugate Gradient Methods (ILMCGM)

Numerical Experiments with the Lagrange Multiplier and Conjugate Gradient Methods (ILMCGM) Aerca Joural of Appled Matheatcs 4; (6: -6 Publshed ole Jauary 5, 5 (http://wwwscecepublshroupco//aa do: 648/aa465 ISSN: 33-43 (Prt; ISSN: 33-6X (Ole Nuercal Eperets wth the Larae Multpler ad Couate Gradet

More information

Strong Convergence of Weighted Averaged Approximants of Asymptotically Nonexpansive Mappings in Banach Spaces without Uniform Convexity

Strong Convergence of Weighted Averaged Approximants of Asymptotically Nonexpansive Mappings in Banach Spaces without Uniform Convexity BULLETIN of the MALAYSIAN MATHEMATICAL SCIENCES SOCIETY Bull. Malays. Math. Sc. Soc. () 7 (004), 5 35 Strog Covergece of Weghted Averaged Appromats of Asymptotcally Noepasve Mappgs Baach Spaces wthout

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

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

Solving Interval and Fuzzy Multi Objective. Linear Programming Problem. by Necessarily Efficiency Points

Solving Interval and Fuzzy Multi Objective. Linear Programming Problem. by Necessarily Efficiency Points Iteratoal Mathematcal Forum, 3, 2008, o. 3, 99-06 Solvg Iterval ad Fuzzy Mult Obectve ear Programmg Problem by Necessarly Effcecy Pots Hassa Mshmast Neh ad Marzeh Aleghad Mathematcs Departmet, Faculty

More information

Evaluation study on training of 100m sprint athletes based on improved D-S evidence theory

Evaluation study on training of 100m sprint athletes based on improved D-S evidence theory Avalable ole www.ocpr.co Joural of Checal ad Pharaceutcal Research, 0, 6(:-6 Research Artcle ISSN : 097-78 CODEN(USA : JCPRC Evaluato study o trag of 00 sprt athletes based o proved D-S evdece theory WU

More information

Robust Mean-Conditional Value at Risk Portfolio Optimization

Robust Mean-Conditional Value at Risk Portfolio Optimization 3 rd Coferece o Facal Matheatcs & Applcatos, 30,3 Jauary 203, Sea Uversty, Sea, Ira, pp. xxx-xxx Robust Mea-Codtoal Value at Rsk Portfolo Optato M. Salah *, F. Pr 2, F. Mehrdoust 2 Faculty of Matheatcal

More information

The Necessarily Efficient Point Method for Interval Molp Problems

The Necessarily Efficient Point Method for Interval Molp Problems ISS 6-69 Eglad K Joural of Iformato ad omputg Scece Vol. o. 9 pp. - The ecessarly Effcet Pot Method for Iterval Molp Problems Hassa Mshmast eh ad Marzeh Alezhad + Mathematcs Departmet versty of Ssta ad

More information

-Pareto Optimality for Nondifferentiable Multiobjective Programming via Penalty Function

-Pareto Optimality for Nondifferentiable Multiobjective Programming via Penalty Function JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS 198, 248261 1996 ARTICLE NO. 0080 -Pareto Otalty for Nodfferetable Multobectve Prograg va Pealty Fucto J. C. Lu Secto of Matheatcs, Natoal Uersty Prearatory

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

Uniform asymptotical stability of almost periodic solution of a discrete multispecies Lotka-Volterra competition system

Uniform asymptotical stability of almost periodic solution of a discrete multispecies Lotka-Volterra competition system Iteratoal Joural of Egeerg ad Advaced Research Techology (IJEART) ISSN: 2454-9290, Volume-2, Issue-1, Jauary 2016 Uform asymptotcal stablty of almost perodc soluto of a dscrete multspeces Lotka-Volterra

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

The Mathematical Appendix

The Mathematical Appendix The Mathematcal Appedx Defto A: If ( Λ, Ω, where ( λ λ λ whch the probablty dstrbutos,,..., Defto A. uppose that ( Λ,,..., s a expermet type, the σ-algebra o λ λ λ are defed s deoted by ( (,,...,, σ Ω.

More information

Descriptive Statistics

Descriptive Statistics Page Techcal Math II Descrptve Statstcs Descrptve Statstcs Descrptve statstcs s the body of methods used to represet ad summarze sets of data. A descrpto of how a set of measuremets (for eample, people

More information

Connective Eccentricity Index of Some Thorny Graphs

Connective Eccentricity Index of Some Thorny Graphs Aals of ure ad Appled Matheatcs Vol. 7, No., 04, 59-64 IN: 79-087X (), 79-0888(ole) ublshed o 9 epteber 04 www.researchathsc.org Aals of oectve Eccetrcty Idex of oe Thory raphs Nlaja De, k. Md. Abu Nayee

More information

The Study on Direct Adaptive Fuzzy Controllers

The Study on Direct Adaptive Fuzzy Controllers Iteratoal Joural of Fuzzy Systes, Vol., No.3, Septeber The Study o Drect Adaptve Fuzzy Cotrollers Shu-Feg Su, Jua-Chh Chag, ad Sog-Shyog Che Abstract Drect adaptve fuzzy cotrollers have bee proposed ad

More information

A Characterization of Jacobson Radical in Γ-Banach Algebras

A Characterization of Jacobson Radical in Γ-Banach Algebras Advaces Pure Matheatcs 43-48 http://dxdoorg/436/ap66 Publshed Ole Noveber (http://wwwscrporg/joural/ap) A Characterzato of Jacobso Radcal Γ-Baach Algebras Nlash Goswa Departet of Matheatcs Gauhat Uversty

More information

An Indian Journal FULL PAPER ABSTRACT KEYWORDS. Trade Science Inc. Research on scheme evaluation method of automation mechatronic systems

An Indian Journal FULL PAPER ABSTRACT KEYWORDS. Trade Science Inc. Research on scheme evaluation method of automation mechatronic systems [ype text] [ype text] [ype text] ISSN : 0974-7435 Volume 0 Issue 6 Boechology 204 Ida Joural FULL PPER BIJ, 0(6, 204 [927-9275] Research o scheme evaluato method of automato mechatroc systems BSRC Che

More information

Correlation of Neutrosophic Sets in Probability Spaces

Correlation of Neutrosophic Sets in Probability Spaces JMSI 10 014 No. 1 45 orrelato of Neutrosophc Sets Probablty Spaces I.M. HNFY.. SLM O. M. KHLED ND K. M. MHFOUZ bstract I ths paper we troduce ad study the cocepts of correlato ad correlato coeffcet of

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

Beam Warming Second-Order Upwind Method

Beam Warming Second-Order Upwind Method Beam Warmg Secod-Order Upwd Method Petr Valeta Jauary 6, 015 Ths documet s a part of the assessmet work for the subject 1DRP Dfferetal Equatos o Computer lectured o FNSPE CTU Prague. Abstract Ths documet

More information

A New Measure of Probabilistic Entropy. and its Properties

A New Measure of Probabilistic Entropy. and its Properties Appled Mathematcal Sceces, Vol. 4, 200, o. 28, 387-394 A New Measure of Probablstc Etropy ad ts Propertes Rajeesh Kumar Departmet of Mathematcs Kurukshetra Uversty Kurukshetra, Ida rajeesh_kuk@redffmal.com

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

Strong Laws of Large Numbers for Fuzzy Set-Valued Random Variables in Gα Space

Strong Laws of Large Numbers for Fuzzy Set-Valued Random Variables in Gα Space Advaces Pure Matheatcs 26 6 583-592 Publshed Ole August 26 ScRes http://wwwscrporg/oural/ap http://dxdoorg/4236/ap266947 Strog Laws of Large Nubers for uzzy Set-Valued Rado Varables G Space Lae She L Gua

More information

On Monotone Eigenvectors of a Max-T Fuzzy Matrix

On Monotone Eigenvectors of a Max-T Fuzzy Matrix Joural of Appled Mathematcs ad hyscs, 08, 6, 076-085 http://wwwscrporg/joural/jamp ISSN Ole: 37-4379 ISSN rt: 37-435 O Mootoe Egevectors of a Max-T Fuzzy Matrx Qg Wag, Na Q, Zxua Yag, Lfe Su, Lagju eg,

More information

Reliability and Cost Analysis of a Series System Model Using Fuzzy Parametric Geometric Programming

Reliability and Cost Analysis of a Series System Model Using Fuzzy Parametric Geometric Programming P P P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Iue 8, October 204. Relablty ad Cot Aaly of a Sere Syte Model Ug Fuzzy Paraetrc Geoetrc Prograg Medhat El-Dacee P 2 2 P, Fahee

More information

Parallelized methods for solving polynomial equations

Parallelized methods for solving polynomial equations IOSR Joural of Matheatcs (IOSR-JM) e-issn: 2278-5728, p-issn: 239-765X. Volue 2, Issue 4 Ver. II (Jul. - Aug.206), PP 75-79 www.osrourals.org Paralleled ethods for solvg polyoal equatos Rela Kapçu, Fatr

More information

A Family of Non-Self Maps Satisfying i -Contractive Condition and Having Unique Common Fixed Point in Metrically Convex Spaces *

A Family of Non-Self Maps Satisfying i -Contractive Condition and Having Unique Common Fixed Point in Metrically Convex Spaces * Advaces Pure Matheatcs 0 80-84 htt://dxdoorg/0436/a04036 Publshed Ole July 0 (htt://wwwscrporg/oural/a) A Faly of No-Self Mas Satsfyg -Cotractve Codto ad Havg Uque Coo Fxed Pot Metrcally Covex Saces *

More information

3.1 Introduction to Multinomial Logit and Probit

3.1 Introduction to Multinomial Logit and Probit ES3008 Ecooetrcs Lecture 3 robt ad Logt - Multoal 3. Itroducto to Multoal Logt ad robt 3. Estato of β 3. Itroducto to Multoal Logt ad robt The ultoal Logt odel s used whe there are several optos (ad therefore

More information

Sebastián Martín Ruiz. Applications of Smarandache Function, and Prime and Coprime Functions

Sebastián Martín Ruiz. Applications of Smarandache Function, and Prime and Coprime Functions Sebastá Martí Ruz Alcatos of Saradache Fucto ad Pre ad Core Fuctos 0 C L f L otherwse are core ubers Aerca Research Press Rehoboth 00 Sebastá Martí Ruz Avda. De Regla 43 Choa 550 Cadz Sa Sarada@telele.es

More information

Study of Correlation using Bayes Approach under bivariate Distributions

Study of Correlation using Bayes Approach under bivariate Distributions Iteratoal Joural of Scece Egeerg ad Techolog Research IJSETR Volume Issue Februar 4 Stud of Correlato usg Baes Approach uder bvarate Dstrbutos N.S.Padharkar* ad. M.N.Deshpade** *Govt.Vdarbha Isttute of

More information

Analysis of Lagrange Interpolation Formula

Analysis of Lagrange Interpolation Formula P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal

More information

CHAPTER VI Statistical Analysis of Experimental Data

CHAPTER VI Statistical Analysis of Experimental Data Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca

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

. The set of these sums. be a partition of [ ab, ]. Consider the sum f( x) f( x 1)

. The set of these sums. be a partition of [ ab, ]. Consider the sum f( x) f( x 1) Chapter 7 Fuctos o Bouded Varato. Subject: Real Aalyss Level: M.Sc. Source: Syed Gul Shah (Charma, Departmet o Mathematcs, US Sargodha Collected & Composed by: Atq ur Rehma (atq@mathcty.org, http://www.mathcty.org

More information

SUBCLASS OF HARMONIC UNIVALENT FUNCTIONS ASSOCIATED WITH SALAGEAN DERIVATIVE. Sayali S. Joshi

SUBCLASS OF HARMONIC UNIVALENT FUNCTIONS ASSOCIATED WITH SALAGEAN DERIVATIVE. Sayali S. Joshi Faculty of Sceces ad Matheatcs, Uversty of Nš, Serba Avalable at: http://wwwpfacyu/float Float 3:3 (009), 303 309 DOI:098/FIL0903303J SUBCLASS OF ARMONIC UNIVALENT FUNCTIONS ASSOCIATED WIT SALAGEAN DERIVATIVE

More information

Unique Common Fixed Point of Sequences of Mappings in G-Metric Space M. Akram *, Nosheen

Unique Common Fixed Point of Sequences of Mappings in G-Metric Space M. Akram *, Nosheen Vol No : Joural of Facult of Egeerg & echolog JFE Pages 9- Uque Coo Fed Pot of Sequeces of Mags -Metrc Sace M. Ara * Noshee * Deartet of Matheatcs C Uverst Lahore Pasta. Eal: ara7@ahoo.co Deartet of Matheatcs

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

Rademacher Complexity. Examples

Rademacher Complexity. Examples Algorthmc Foudatos of Learg Lecture 3 Rademacher Complexty. Examples Lecturer: Patrck Rebesch Verso: October 16th 018 3.1 Itroducto I the last lecture we troduced the oto of Rademacher complexty ad showed

More information

CIS 800/002 The Algorithmic Foundations of Data Privacy October 13, Lecture 9. Database Update Algorithms: Multiplicative Weights

CIS 800/002 The Algorithmic Foundations of Data Privacy October 13, Lecture 9. Database Update Algorithms: Multiplicative Weights CIS 800/002 The Algorthmc Foudatos of Data Prvacy October 13, 2011 Lecturer: Aaro Roth Lecture 9 Scrbe: Aaro Roth Database Update Algorthms: Multplcatve Weghts We ll recall aga) some deftos from last tme:

More information

GENERATE FUZZY CONCEPTS BASED ON JOIN-IRREDUCIBLE ELEMENTS

GENERATE FUZZY CONCEPTS BASED ON JOIN-IRREDUCIBLE ELEMENTS GENERATE FUZZY CONCEPTS BASED ON JOIN-IRREDUCIBLE ELEMENTS Hua Mao ad *Zhe Zheg Departmet of Mathematcs ad Iformato Scece Hebe Uversty Baodg 071002 Cha *Author for Correspodece: 373380431@qq.com ABSTRACT

More information

A Bivariate Distribution with Conditional Gamma and its Multivariate Form

A Bivariate Distribution with Conditional Gamma and its Multivariate Form Joural of Moder Appled Statstcal Methods Volue 3 Issue Artcle 9-4 A Bvarate Dstrbuto wth Codtoal Gaa ad ts Multvarate For Sue Se Old Doo Uversty, sxse@odu.edu Raja Lachhae Texas A&M Uversty, raja.lachhae@tauk.edu

More information

SMOOTH SUPPORT VECTOR REGRESSION BASED ON MODIFICATION SPLINE INTERPOLATION

SMOOTH SUPPORT VECTOR REGRESSION BASED ON MODIFICATION SPLINE INTERPOLATION Joural of heoretcal ad Appled Iforato echology 5 th October. Vol. 44 No. 5 - JAI & LLS. All rghts reserved. ISSN: 99-8645 www.att.org E-ISSN: 87-395 OOH SUPPOR VECOR REGRESSION BASED ON ODIFICAION SPLINE

More information

Construction of Composite Indices in Presence of Outliers

Construction of Composite Indices in Presence of Outliers Costructo of Coposte dces Presece of Outlers SK Mshra Dept. of Ecoocs North-Easter Hll Uversty Shllog (da). troducto: Oftetes we requre costructg coposte dces by a lear cobato of a uber of dcator varables.

More information

Q-analogue of a Linear Transformation Preserving Log-concavity

Q-analogue of a Linear Transformation Preserving Log-concavity Iteratoal Joural of Algebra, Vol. 1, 2007, o. 2, 87-94 Q-aalogue of a Lear Trasformato Preservg Log-cocavty Daozhog Luo Departmet of Mathematcs, Huaqao Uversty Quazhou, Fua 362021, P. R. Cha ldzblue@163.com

More information

The Mathematics of Portfolio Theory

The Mathematics of Portfolio Theory The Matheatcs of Portfolo Theory The rates of retur of stocks, ad are as follows Market odtos state / scearo) earsh Neutral ullsh Probablty 0. 0.5 0.3 % 5% 9% -3% 3% % 5% % -% Notato: R The retur of stock

More information

The theoretical background of

The theoretical background of he theoretcal backgroud of -echologes he theoretcal backgroud of FactSage he followg sldes gve a abrdged overvew of the ajor uderlyg prcples of the calculatoal odules of FactSage. -echologes he bbs Eergy

More information

A Remark on the Uniform Convergence of Some Sequences of Functions

A Remark on the Uniform Convergence of Some Sequences of Functions Advaces Pure Mathematcs 05 5 57-533 Publshed Ole July 05 ScRes. http://www.scrp.org/joural/apm http://dx.do.org/0.436/apm.05.59048 A Remark o the Uform Covergece of Some Sequeces of Fuctos Guy Degla Isttut

More information

Compromise Ratio Method for Decision Making under Fuzzy Environment using Fuzzy Distance Measure

Compromise Ratio Method for Decision Making under Fuzzy Environment using Fuzzy Distance Measure World Acadey of Scece, Egeerg ad Techology Iteratoal Joural of Matheatcal ad Coputatoal Sceces Vol:, No:, 7 Coprose ato Method for Decso Makg uder Fuzzy Evroet usg Fuzzy Dstace Measure Debashree Guha,

More information

Optimization of Reorder Point Strategy of Assembly Manufacturer with Random Variables

Optimization of Reorder Point Strategy of Assembly Manufacturer with Random Variables Iteratoal Joural of Busess ad Maageet; Vol. 8, No. 5; 013 ISSN 1833-3850 E-ISSN 1833-8119 Publshed by Caada Ceter of Scece ad Educato Optzato of Reorder Pot Strategy of Assebly Maufacturer wth Rado Varables

More information

TESTS BASED ON MAXIMUM LIKELIHOOD

TESTS BASED ON MAXIMUM LIKELIHOOD ESE 5 Toy E. Smth. The Basc Example. TESTS BASED ON MAXIMUM LIKELIHOOD To llustrate the propertes of maxmum lkelhood estmates ad tests, we cosder the smplest possble case of estmatg the mea of the ormal

More information

CS 2750 Machine Learning. Lecture 7. Linear regression. CS 2750 Machine Learning. Linear regression. is a linear combination of input components x

CS 2750 Machine Learning. Lecture 7. Linear regression. CS 2750 Machine Learning. Linear regression. is a linear combination of input components x CS 75 Mache Learg Lecture 7 Lear regresso Mlos Hauskrecht los@cs.ptt.edu 59 Seott Square CS 75 Mache Learg Lear regresso Fucto f : X Y s a lear cobato of put copoets f + + + K d d K k - paraeters eghts

More information

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b CS 70 Dscrete Mathematcs ad Probablty Theory Fall 206 Sesha ad Walrad DIS 0b. Wll I Get My Package? Seaky delvery guy of some compay s out delverg packages to customers. Not oly does he had a radom package

More information

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg

More information