HW10.nb 1. It is basically quadratic around zero and is very steep. One may guess that the harmonic oscillator is a pretty good approximation.

Size: px
Start display at page:

Download "HW10.nb 1. It is basically quadratic around zero and is very steep. One may guess that the harmonic oscillator is a pretty good approximation."

Transcription

1 HW0.nb HW #0. Varatonal Method Plot of the potental The potental n the problem s V = 50 He -x - L. V = 50 HE -x - L 50 H- + -x L Plot@V, 8x, -, <D Ü Graphcs Ü It s bascally quadratc around zero and s very steep. One may guess that the harmonc oscllator s a pretty good approxmaton. Intal guess Seres@V, 8x, 0, 3<D 50 x - 50 x 3 + O@xD 4 If we try to dentfy the frst term wth the harmonc oscllator potental ÅÅÅÅ m w x, we fnd w = 0 because m =. Then one may hope that t would gve the ground-state energy ÅÅÅÅ w = 5. However, ths hope s not qute fulflled. Usng the groundstate wave functon of the harmonc oscllator, y = J ÅÅÅÅÅÅÅÅÅ m w ê4 p N E -m w x êh L - m x w ÅÅÅÅÅÅÅÅÅÅÅÅÅ H ÅÅÅÅÅÅ m w ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ Lê4 p ê4 ÅÅÅÅÅ

2 HW0.nb y = y ê. 8m Ø, w Ø 0, Ø < -5 x J ÅÅÅÅÅÅÅ 0 Nê4 p K = ÅÅÅÅÅÅÅÅÅ - Integrate@y D@y, 8x, <D, 8x, -, <D ê. 8 Ø, m Ø < m 5 ÅÅÅÅ P = Integrate@y V, 8x, -, <D Unque::usym : 5ê s not a symbol or a vald symbol name. The error s 50 H - ê40 + ê0 L Ebar = K + P 5 ÅÅÅÅ + 50 H - ê40 + ê0 L N@%D % - 39 ê 8 ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ ÅÅÅÅÅÅ 39 ê and s bgger than 5%, but s pretty good, accurate wthn 7.%. Therefore, we are motvated to try a Gaussan as a tral functon. Gaussan tral functon y = ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ ÅÅÅÅ p ê4 D ê E-x êh D L - ÅÅÅÅÅÅÅÅÅ x D ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ pê4 è!!! D K = ÅÅÅÅÅÅÅÅÅ - Integrate@y D@y, 8x, <D, 8x, -, <, Assumptons Ø D > 0D ê. 8 Ø, m Ø < m Unque::usym : 5ê s not a symbol or a vald symbol name. ÅÅÅÅÅÅÅÅÅ 4 D P = Integrate@y V, 8x, -, <, Assumptons Ø D > 0D Unque::usym : êh4*d^l s not a symbol or a vald symbol name. 50 J - D ÅÅÅÅÅÅ 4 + D N

3 HW0.nb 3 Ebar = K + P ÅÅÅÅÅÅ 50 J - D 4 + D N + ÅÅÅÅÅÅÅÅÅ 4 D FndMnmum@Ebar, 8D, 0 -ê <D , 8D Ø << %@@DD - 39 ê 8 ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ 39 ê It has mproved, but not yet wthn 5%. Lnear tmes Gaussan One way to mprove t further s the followng. Note that the potental s not party symmetrc, and hence the groundstate wavefuncton s not expected to be an even functon. Because the potental s lower on the rght, we expect the wave functon s skewed towards the rght. Therefore, we can try y = H + k xl E -x êh D L - ÅÅÅÅÅÅÅÅÅÅ x D H + k xl It s not normalzed at ths pont, and we calculate ts norm norm = Integrate@y, 8x, -, <, Assumptons Ø D > 0D Unque::usym : êh4*d^l s not a symbol or a vald symbol name. è!!! ÅÅÅÅ p D H + k D L K = ÅÅÅÅÅÅÅÅÅ - Integrate@y D@y, 8x, <D, 8x, -, <, Assumptons Ø D > 0D ê. 8m Ø, Ø < m Unque::usym : êh4*d^l s not a symbol or a vald symbol name. è!!! p H + 3 k D L ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ ÅÅÅÅÅÅÅ 8 D P = Integrate@y V, 8x, -, <, Assumptons Ø D > 0D Unque::usym : á30à s not a symbol or a vald symbol name. 5 è!!! p D ÅÅÅÅÅÅÅ J - 4 D 4 + D ÅÅÅÅÅÅÅ + 4 D 4 k D - 4 D k D + k D ÅÅÅÅÅÅ - D 4 k D + D k D ÅÅÅÅÅÅ - D 4 k D 4 + D k D 4 N

4 HW0.nb 4 Ebar = K + P ÅÅÅÅÅÅÅÅÅÅÅÅÅÅ norm j è!!! p H + 3 k j D L ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ ÅÅÅÅÅÅÅ + 5 è!!! ÅÅÅÅÅÅÅ p D J - 4 D 4 + k k 8 D D ÅÅÅÅÅÅÅ + 4 D 4 k D - 4 D k D + k D ÅÅÅÅÅÅÅ - D 4 k D + D k D ÅÅÅÅÅÅ - D 4 k D 4 + D k D 4 N y z y z ì H è!!! p D H + k D LL soluton = FndMnmum@Ebar, 8D, 0 -ê <, 8k, 0<D , 8D Ø , k Ø << soluton@@dd - 39 ê 8 ÅÅÅÅÅÅÅ ÅÅÅÅÅÅÅ 39 ê Ths s correct at the % level! The wave functon s therefore y ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ è!!!!!!!!!!!!! ÅÅÅÅÅ norm ê. soluton@@dd x H xl y PlotA ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ ÅÅÅÅÅ è!!!!!!!!!!!! norm ê. soluton@@dd, 8x, -, <E Ü Graphcs Ü As expected, t s more or less a Gaussan, but skewed to the rght. Obvously, there are many ways to mprove Gaussan. I hope you found one successfully. The analytc soluton Ths problem s actually a specal case of the Morse potental V = D HE -a u - L D H- + -a u L

5 HW0.nb 5 Ths potental s a form of the nter-atomc potental n datomc molecules proposed by P.M. Morse, Phys. Rev. 34, 57 (99). The varable u s the dstance between two atoms mnus ts equlbrum dstance u = r - r 0. The Schrödnger equaton can be solved analytcally. Expandng t around the mnmum, Seres@V, 8u, 0, 4<D a D u - a 3 D u ÅÅÅÅÅÅÅ a4 D u 4 + O@uD 5 Harmonc oscllator approxmaton gves w = "########### a D ÅÅÅÅÅÅÅÅÅÅÅÅÅ. The correct energy egenvalues are known to be m E n = whn + ÅÅÅÅ L - a ÅÅÅÅÅÅÅÅÅÅÅÅ m Hn + ÅÅÅÅ L, where the second term s called the anharmoncanharmonc correcton. For large n, the second term wll domnate and the energy appears to become negatve. Clearly, the bound state spectrum does not go forever, and ends at a certan value of n, qute dfferent from the harmonc oscllator. But ths s expcted because the potental energy asymptotes to D for u Ø + and hence states for E > D must be unbound and hence have a contnuous spectrum. The ground-state wave functon s known to have the form -d E-a u -b a uê y = E E -d -a u - ÅÅÅÅÅÅÅÅÅÅÅ a b u where b = d -, d = è!!!!!!!!!!!! D m ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ. Let us see that t satsfes the Schrödnger equaton. a I- ÅÅÅÅÅÅ m SmplfyA D@y, 8u, <D + D HE-a u - L ym ÅÅÅÅÅÅÅÅÅÅÅÅÅ ÅÅÅÅÅÅÅ ÅÅÅÅÅÅÅÅ y è!!! a H-4 è!!!!!!! D m + a L - ÅÅÅÅÅÅÅ ÅÅÅÅÅÅ 8 m ê. 8b Ø d - < ê. 9d -> è!!!!!!!!!! D m ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ =E a The frst term s ÅÅÅÅ w = a "######## ÅÅÅÅÅÅÅ D m and the zero-pont energy wth the harmonc oscllator approxmaton. The second term - a ÅÅÅÅÅÅÅÅÅÅÅÅ s the anharmonc correcton. 8 m The normalzaton s norm = Integrate@y, 8u, -, <, Assumptons Ø a > 0 && d > 0D Unque::usym : êh4*d^l s not a symbol or a vald symbol name. -b d -b Gamma@bD ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ ÅÅÅÅÅÅÅÅ a Actually, ths wave functon could have been guessed f one pays a careful attenton to the asymptotc behavors. For u Ø, the potental asymptotes to a constant D, and hence the wave functon must damp exponentally e -k u wth k = è!!!!!!!!!!!!!!! m» E» ë. For u Ø -, the potental rses extremely steeply as D e - a u. It suggests that the energy egenvalue becomes quckly rrelevant, and the behavor of the wave functon must be gven purely by the rsng behavor of the potental. By droppng the energy egenvalue and lookng at the Schrödnger equaton, - ÅÅÅÅÅÅÅÅ d y ÅÅÅÅÅÅÅÅÅÅ + D e - a u y = 0, m d u and change the varable to y = e -a u, we fnd - ÅÅÅÅÅÅÅÅ m I d y ÅÅÅÅÅÅÅÅÅÅ + ÅÅÅÅ ÅÅÅÅÅÅÅ d y d y y d y M + D y = 0. The second term n the parentheses s neglble for y Ø. Therefore the wave functon has the behavor y e -è!!!!!!!!!!! m D yê a. Combnng the behavor on both ends, the wave functon has precsely the exact form gven above. Ths s the lesson: the one-dmensonal potental problem s so smple that there are many ways to study the behavor of the wave functon. On the other hand, the real-world problem nvolves many more degrees of freedom. In many cases, the Hamltonan tself must be guessed.

6 HW0.nb 6 Back to the Morse potental. The case of the homework problem corresponds to D = 50, a =, m =, =. Therefore the groundstate energy s E 0 = ÅÅÅÅ a "######## ÅÅÅÅÅÅÅ D m - a ÅÅÅÅÅÅÅÅÅÅÅÅ 8 m = 5 - ÅÅÅÅ 8 = ÅÅÅÅÅÅ SmplfyA y ê. 8b Ø d - < ê. 9d Ø è!!!!!!!!!!!! norm u - ÅÅÅÅÅÅÅÅÅ 9 u ÅÅÅÅÅÅÅÅÅ 567 è!!!!!!!!!! 43 9 u ÅÅÅÅÅÅÅÅÅ è!!!!!!!!!! D m ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ a PlotA u - ÅÅÅÅÅÅÅÅÅ 567 è!!!!!!!!!!, 8u, -, <, PlotPonts Ø 50E 43 = ê. 8D Ø 50, a Ø, m Ø, Ø <E Ü Graphcs Ü The varatonal lnear tmes Gaussan wave functon was ` x PlotA ` H ` xl, 8x, -, <, PlotPonts Ø 50E Ü Graphcs Ü

7 HW0.nb 7 Show@%, %%D Ü Graphcs Ü Qute close.. Neutrno Oscllaton (a) The untarty matrx s 88, 0, 0<, 80, Cos@q 3 D, Sn@q 3 D<, 80, -Sn@q 3 D, Cos@q 3 D<< êê MatrxForm 0 0 y 0 Cos@q 3 D Sn@q 3 D j z k 0 -Sn@q 3 D Cos@q 3 D 88Cos@q 3 D, 0, Sn@q 3 D E -I d <, 80,, 0<, 8-Sn@q 3 D E I d, 0, Cos@q 3 D<< êê MatrxForm Cos@q 3 D 0 -Â d Sn@q 3 D y 0 0 j z k - Â d Sn@q 3 D 0 Cos@q 3 D 88Cos@q D, Sn@q D, 0<, 8-Sn@q D, Cos@q D, 0<, 80, 0, << êê MatrxForm Cos@q D Sn@q D 0 y -Sn@q D Cos@q D 0 j z k 0 0 U = 88, 0, 0<, 80, Cos@q 3 D, Sn@q 3 D<, 80, -Sn@q 3 D, Cos@q 3 D<<. 88Cos@q 3 D, 0, Sn@q 3 D E -I d <, 80,, 0<, 8-Sn@q 3 D E I d, 0, Cos@q 3 D<<. 88Cos@q D, Sn@q D, 0<, 8-Sn@q D, Cos@q D, 0<, 80, 0, << 88Cos@q D Cos@q 3 D, Cos@q 3 D Sn@q D, -Â d Sn@q 3 D<, 8-Cos@q 3 D Sn@q D - Â d Cos@q D Sn@q 3 D Sn@q 3 D, Cos@q D Cos@q 3 D - Â d Sn@q D Sn@q 3 D Sn@q 3 D, Cos@q 3 D Sn@q 3 D<, 8- Â d Cos@q D Cos@q 3 D Sn@q 3 D + Sn@q D Sn@q 3 D, - Â d Cos@q 3 D Sn@q D Sn@q 3 D - Cos@q D Sn@q 3 D, Cos@q 3 D Cos@q 3 D<<

8 HW0.nb 8 Udagger = Transpose@U ê. 8d Ø -d<d 88Cos@q D Cos@q 3 D, -Cos@q 3 D Sn@q D - - d Cos@q D Sn@q 3 D Sn@q 3 D, - - d Cos@q D Cos@q 3 D Sn@q 3 D + Sn@q D Sn@q 3 D<, 8Cos@q 3 D Sn@q D, Cos@q D Cos@q 3 D - - d Sn@q D Sn@q 3 D Sn@q 3 D, - - d Cos@q 3 D Sn@q D Sn@q 3 D - Cos@q D Sn@q 3 D<, 8  d Sn@q 3 D, Cos@q 3 D Sn@q 3 D, Cos@q 3 D Cos@q 3 D<< Smplfy@Udagger.UD 88, 0, 0<, 80,, 0<, 80, 0, << For the two-by-two case, the untarty matrx s U3 = U ê. 8q Ø 0, q 3 Ø 0< 88, 0, 0<, 80, Cos@q 3 D, Sn@q 3 D<, 80, -Sn@q 3 D, Cos@q 3 D<< % êê MatrxForm 0 0 y 0 Cos@q 3 D Sn@q 3 D j z k 0 -Sn@q 3 D Cos@q 3 D The man pont n the calculaton s that the Hamltonan can be wrtten as H = UIc p + m c ÅÅÅÅÅÅÅÅÅÅ 3 Å p M U, and hence the tme evoluton operator s e - H tê = U e -Hc p+m c 3 ê pl tê U e - m c 3 tê p 0 0 = e - c p tê U 0 e - m c 3 tê p 0 U j k 0 0 e - m 3 c 3 tê p z The ampltude of our nterest s y A = E -I c p tê I80,, 0<.U3.DagonalMatrxA9E -I m c 3 têh p L, E -I m c 3 têh p L, E -I m 3 c 3 têh p L =E. -  c p t ÅÅÅÅÅÅÅÅÅÅÅÅÅ Transpose@U3D.880<, 8<, 80<<M@@DD j -  c 3 ÅÅÅÅ p Cos@q 3 D + -  c3 3 p Sn@q 3 D y z k Astar = A ê. 8t Ø -t< ÅÅÅÅÅÅÅÅÅÅÅÅÅÅ Â c p t j  c3 k p Cos@q 3 D +  c3 3 p Sn@q 3 D y z Psurv = Smplfy@ExpToTrg@Expand@A AstarDDD Cos@q 3 D 4 + CosA c3 t Hm - m 3 L ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ E Cos@q p 3 D Sn@q 3 D + Sn@q 3 D 4

9 HW0.nb 9 Ths can be rewrtten as P surv = cos 4 q 3 + cos q 3 sn q 3 coshm - m 3 L c 3 t ÅÅÅÅÅÅÅÅÅÅ p + sn4 q 3 = Hcos q 3 + sn q 3 L - cos q 3 sn q 3 + cos q 3 sn q 3 coshm - m 3 L c 3 = - cos q 3 sn q 3 I - coshm - m 3 L c 3 t ÅÅÅÅÅÅÅÅÅÅÅ p M = - 4 cos q 3 sn q 3 sn Hm - m 3 L c 3 t ÅÅÅÅÅÅÅÅÅÅÅ 4 p = - sn q 3 sn Hm -m 3 L c 3 t ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ 4 p. Ths s nothng but Eq. (5) n the problem. (b) t ÅÅÅÅÅÅÅÅÅÅÅ p For p = GeV, m 3 - m =.5ä0-3 ev ê c, and usng = 6.58 ä0 - MeV sec = 6.58 ä0-6 ev sec and c = 3.00 ä0 0 cm sec -, the argument of sn.5ä0 s -3 ev êc c 3 t ÅÅÅÅÅÅÅÅÅÅÅÅÅÅ = ÅÅÅÅÅÅÅÅÅÅÅÅ 950 t = ÅÅÅÅÅÅÅÅÅÅÅÅÅ 950 c t = 37 L L ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ = 3.7 c ev ev sec sec c sec 0 0 ä0-3 ÅÅÅÅÅÅÅ cm km. Takng q 3 = ÅÅÅÅÅÅÅÅ è!!!! and hence sn q 3 =, we fnd P surv = - sn H3.7 ä0-3 ÅÅÅÅÅÅÅ L km L = cos H3.7 ä0-3 ÅÅÅÅÅÅÅ L km L. PlotACos@ LD, 8L, 0, 0000<E Ü Graphcs Ü The oscllaton occurs on the dstances of thousands of klometers. Ths s what had been observed by the SuperKamokande experment, Phys. Rev. 93, 080 (004), even though the data shows nearly washed-out oscllaton due to the so-so resoluton n energy and dstance. A very macroscopc quantum phenomenon. (c) For the three-state case, we keep all angles, and we calculate the oscllaton probablty from n m to n e,

10 HW0.nb 0 Amue = E -I c p tê I8, 0, 0<.U.DagonalMatrxA9E -I m c 3 têh p L, E -I m c 3 têh p L, E -I m 3 c 3 têh p L =E.Udagger. -  c p t ÅÅÅÅÅÅÅÅÅÅÅÅÅ 880<, 8<, 80<<M@@DD j - d-  c3 3 ÅÅÅ p Cos@q 3 D Sn@q 3 D Sn@q 3 D + k -  c3 p Cos@q D Cos@q 3 D H-Cos@q 3 D Sn@q D - - d Cos@q D Sn@q 3 D Sn@q 3 DL + -  c3 p Cos@q 3 D Sn@q D HCos@q D Cos@q 3 D - - d Sn@q D Sn@q 3 D Sn@q 3 DL y z Amuestar = % ê. 8t Ø -t, d Ø -d< ÅÅÅÅÅÅÅÅÅÅÅÅÅÅ Â c p t j  d+  c3 3 k  c3 p Cos@q 3 D Sn@q 3 D Sn@q 3 D + p Cos@q D Cos@q 3 D H-Cos@q 3 D Sn@q D -  d Cos@q D Sn@q 3 D Sn@q 3 DL +  c3 p Cos@q 3 D Sn@q D HCos@q D Cos@q 3 D -  d Sn@q D Sn@q 3 D Sn@q 3 DL y z and from n e to n m, Pmue = ExpToTrg@Expand@Amue AmuestarDD Cos@q D Cos@q 3 D Cos@q 3 D Sn@q D - CosA c3 ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ p E Cos@q D Cos@q 3 D Cos@q 3 D Sn@q D - CosAd - c3 ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ p + c3 p E Cos@q D Cos@q 3 D Cos@q 3 D Sn@q D Sn@q 3 D Sn@q 3 D + CosAd - c3 p + c3 p E Cos@q D Cos@q 3 D Cos@q 3 D Sn@q D Sn@q 3 D Sn@q 3 D + Cos@dD Cos@q D 3 Cos@q 3 D Cos@q 3 D Sn@q D Sn@q 3 D Sn@q 3 D - CosAd + c3 ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ p E Cos@q D 3 Cos@q 3 D Cos@q 3 D Sn@q D Sn@q 3 D Sn@q 3 D - Cos@dD Cos@q D Cos@q 3 D Cos@q 3 D Sn@q D 3 Sn@q 3 D Sn@q 3 D + CosAd - c3 ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ p + c3 p E Cos@q D Cos@q 3 D Cos@q 3 D Sn@q D 3 Sn@q 3 D Sn@q 3 D + Cos@q 3 D Sn@q 3 D Sn@q 3 D - CosA c3 ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ p E Cos@q D Cos@q 3 D Sn@q 3 D Sn@q 3 D + Cos@q D 4 Cos@q 3 D Sn@q 3 D Sn@q 3 D - CosA c3 p E Cos@q 3 D Sn@q D Sn@q 3 D Sn@q 3 D + CosA c3 ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ p E Cos@q D Cos@q 3 D Sn@q D Sn@q 3 D Sn@q 3 D + Cos@q 3 D Sn@q D 4 Sn@q 3 D Sn@q 3 D

11 HW0.nb Aemu = E -I c p tê I80,, 0<.U.DagonalMatrxA9E -I m c 3 têh p L, E -I m c 3 têh p L, E -I m 3 c 3 têh p L =E.Udagger. -  c p t ÅÅÅÅÅÅÅÅÅÅÅÅÅ 88<, 80<, 80<<M@@DD j  d-  c3 3 ÅÅÅ p Cos@q 3 D Sn@q 3 D Sn@q 3 D + k -  c3 p Cos@q D Cos@q 3 D H-Cos@q 3 D Sn@q D -  d Cos@q D Sn@q 3 D Sn@q 3 DL + -  c3 p Cos@q 3 D Sn@q D HCos@q D Cos@q 3 D -  d Sn@q D Sn@q 3 D Sn@q 3 DL y z Aemustar = % ê. 8t Ø -t, d Ø -d< ÅÅÅÅÅÅÅÅÅÅÅÅÅÅ Â c p t j - d+  c3 3 k  c3 p Cos@q 3 D Sn@q 3 D Sn@q 3 D + p Cos@q D Cos@q 3 D H-Cos@q 3 D Sn@q D - - d Cos@q D Sn@q 3 D Sn@q 3 DL +  c3 p Cos@q 3 D Sn@q D HCos@q D Cos@q 3 D - - d Sn@q D Sn@q 3 D Sn@q 3 DL y z Pemu = ExpToTrg@Expand@Aemu AemustarDD Cos@q D Cos@q 3 D Cos@q 3 D Sn@q D - CosA c3 ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ p E Cos@q D Cos@q 3 D Cos@q 3 D Sn@q D - CosAd + c3 ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ p E Cos@q D Cos@q 3 D Cos@q 3 D Sn@q D Sn@q 3 D Sn@q 3 D + CosAd + c3 p E Cos@q D Cos@q 3 D Cos@q 3 D Sn@q D Sn@q 3 D Sn@q 3 D + Cos@dD Cos@q D 3 Cos@q 3 D Cos@q 3 D Sn@q D Sn@q 3 D Sn@q 3 D - CosAd - c3 ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ p + c3 p E Cos@q D 3 Cos@q 3 D Cos@q 3 D Sn@q D Sn@q 3 D Sn@q 3 D - Cos@dD Cos@q D Cos@q 3 D Cos@q 3 D Sn@q D 3 Sn@q 3 D Sn@q 3 D + CosAd + c3 ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ p E Cos@q D Cos@q 3 D Cos@q 3 D Sn@q D 3 Sn@q 3 D Sn@q 3 D + Cos@q 3 D Sn@q 3 D Sn@q 3 D - CosA c3 ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ p E Cos@q D Cos@q 3 D Sn@q 3 D Sn@q 3 D + Cos@q D 4 Cos@q 3 D Sn@q 3 D Sn@q 3 D - CosA c3 p E Cos@q 3 D Sn@q D Sn@q 3 D Sn@q 3 D + CosA c3 ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ p E Cos@q D Cos@q 3 D Sn@q D Sn@q 3 D Sn@q 3 D + Cos@q 3 D Sn@q D 4 Sn@q 3 D Sn@q 3 D Smplfy@Pmue - PemuD 4 Cos@q 3 D Sn@dD SnA c3 t Hm - m L ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ E 4 p SnA c3 t Hm - m 3 L ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ E SnA c3 t Hm - m 3 L ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ E Sn@ q 4 p 4 p D Sn@q 3 D Sn@ q 3 D Indeed, when d 0, two probabltes are dfferent.

12 HW0.nb (d) As we dd n the class, the tme-reserval nvarance states that Xa» e - H tê» b\ = Xb è» e - H tê» a è \, and hecepha Ø bl = PHb è Ø a è L. Here, a è s the tme-reversed state of a, and b è s the tme-reversed state of b. In our case, the tme-reversed state has the opposte momentum. (If you worry about the helcty, both the momentum and the spn flp, and remans the same, namely left-handed.) However, the Hamltonan depends only on the magntude of the momentum and hence the oscllaton probabltes also depend only on the magntude of the momentum. Therefore PHb è Ø a è L = PHb Ø al. Choosng n e and n m of the same momenta to be the ntal and fnal states, we fnd that the tme reversal nvarance would predct PHn m Ø n e L = PHn e Ø n m L. The fact that the tme-reversal volaton requres d 0makes sense because the tme-reversal opeator nvolves the complex conjugaton, and d s the only complex parameter n the Hamltonan.

Simple Interest Loans (Section 5.1) :

Simple Interest Loans (Section 5.1) : Chapter 5 Fnance The frst part of ths revew wll explan the dfferent nterest and nvestment equatons you learned n secton 5.1 through 5.4 of your textbook and go through several examples. The second part

More information

Recurrence. 1 Definitions and main statements

Recurrence. 1 Definitions and main statements Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.

More information

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12 14 The Ch-squared dstrbuton PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 1 If a normal varable X, havng mean µ and varance σ, s standardsed, the new varable Z has a mean 0 and varance 1. When ths standardsed

More information

8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by

8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by 6 CHAPTER 8 COMPLEX VECTOR SPACES 5. Fnd the kernel of the lnear transformaton gven n Exercse 5. In Exercses 55 and 56, fnd the mage of v, for the ndcated composton, where and are gven by the followng

More information

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..

More information

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ). REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or

More information

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting Causal, Explanatory Forecastng Assumes cause-and-effect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of

More information

Lecture 3: Force of Interest, Real Interest Rate, Annuity

Lecture 3: Force of Interest, Real Interest Rate, Annuity Lecture 3: Force of Interest, Real Interest Rate, Annuty Goals: Study contnuous compoundng and force of nterest Dscuss real nterest rate Learn annuty-mmedate, and ts present value Study annuty-due, and

More information

Section 5.3 Annuities, Future Value, and Sinking Funds

Section 5.3 Annuities, Future Value, and Sinking Funds Secton 5.3 Annutes, Future Value, and Snkng Funds Ordnary Annutes A sequence of equal payments made at equal perods of tme s called an annuty. The tme between payments s the payment perod, and the tme

More information

Implementation of Deutsch's Algorithm Using Mathcad

Implementation of Deutsch's Algorithm Using Mathcad Implementaton of Deutsch's Algorthm Usng Mathcad Frank Roux The followng s a Mathcad mplementaton of Davd Deutsch's quantum computer prototype as presented on pages - n "Machnes, Logc and Quantum Physcs"

More information

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy 4.02 Quz Solutons Fall 2004 Multple-Choce Questons (30/00 ponts) Please, crcle the correct answer for each of the followng 0 multple-choce questons. For each queston, only one of the answers s correct.

More information

Faraday's Law of Induction

Faraday's Law of Induction Introducton Faraday's Law o Inducton In ths lab, you wll study Faraday's Law o nducton usng a wand wth col whch swngs through a magnetc eld. You wll also examne converson o mechanc energy nto electrc energy

More information

Hedging Interest-Rate Risk with Duration

Hedging Interest-Rate Risk with Duration FIXED-INCOME SECURITIES Chapter 5 Hedgng Interest-Rate Rsk wth Duraton Outlne Prcng and Hedgng Prcng certan cash-flows Interest rate rsk Hedgng prncples Duraton-Based Hedgng Technques Defnton of duraton

More information

Support Vector Machines

Support Vector Machines Support Vector Machnes Max Wellng Department of Computer Scence Unversty of Toronto 10 Kng s College Road Toronto, M5S 3G5 Canada wellng@cs.toronto.edu Abstract Ths s a note to explan support vector machnes.

More information

THE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek

THE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek HE DISRIBUION OF LOAN PORFOLIO VALUE * Oldrch Alfons Vascek he amount of captal necessary to support a portfolo of debt securtes depends on the probablty dstrbuton of the portfolo loss. Consder a portfolo

More information

BERNSTEIN POLYNOMIALS

BERNSTEIN POLYNOMIALS On-Lne Geometrc Modelng Notes BERNSTEIN POLYNOMIALS Kenneth I. Joy Vsualzaton and Graphcs Research Group Department of Computer Scence Unversty of Calforna, Davs Overvew Polynomals are ncredbly useful

More information

1. Math 210 Finite Mathematics

1. Math 210 Finite Mathematics 1. ath 210 Fnte athematcs Chapter 5.2 and 5.3 Annutes ortgages Amortzaton Professor Rchard Blecksmth Dept. of athematcal Scences Northern Illnos Unversty ath 210 Webste: http://math.nu.edu/courses/math210

More information

Section 2 Introduction to Statistical Mechanics

Section 2 Introduction to Statistical Mechanics Secton 2 Introducton to Statstcal Mechancs 2.1 Introducng entropy 2.1.1 Boltzmann s formula A very mportant thermodynamc concept s that of entropy S. Entropy s a functon of state, lke the nternal energy.

More information

Vision Mouse. Saurabh Sarkar a* University of Cincinnati, Cincinnati, USA ABSTRACT 1. INTRODUCTION

Vision Mouse. Saurabh Sarkar a* University of Cincinnati, Cincinnati, USA ABSTRACT 1. INTRODUCTION Vson Mouse Saurabh Sarkar a* a Unversty of Cncnnat, Cncnnat, USA ABSTRACT The report dscusses a vson based approach towards trackng of eyes and fngers. The report descrbes the process of locatng the possble

More information

The Mathematical Derivation of Least Squares

The Mathematical Derivation of Least Squares Pscholog 885 Prof. Federco The Mathematcal Dervaton of Least Squares Back when the powers that e forced ou to learn matr algera and calculus, I et ou all asked ourself the age-old queston: When the hell

More information

Chapter 31B - Transient Currents and Inductance

Chapter 31B - Transient Currents and Inductance Chapter 31B - Transent Currents and Inductance A PowerPont Presentaton by Paul E. Tppens, Professor of Physcs Southern Polytechnc State Unversty 007 Objectves: After completng ths module, you should be

More information

Luby s Alg. for Maximal Independent Sets using Pairwise Independence

Luby s Alg. for Maximal Independent Sets using Pairwise Independence Lecture Notes for Randomzed Algorthms Luby s Alg. for Maxmal Independent Sets usng Parwse Independence Last Updated by Erc Vgoda on February, 006 8. Maxmal Independent Sets For a graph G = (V, E), an ndependent

More information

Thursday, December 10, 2009 Noon - 1:50 pm Faraday 143

Thursday, December 10, 2009 Noon - 1:50 pm Faraday 143 1. ath 210 Fnte athematcs Chapter 5.2 and 4.3 Annutes ortgages Amortzaton Professor Rchard Blecksmth Dept. of athematcal Scences Northern Illnos Unversty ath 210 Webste: http://math.nu.edu/courses/math210

More information

THE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES

THE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES The goal: to measure (determne) an unknown quantty x (the value of a RV X) Realsaton: n results: y 1, y 2,..., y j,..., y n, (the measured values of Y 1, Y 2,..., Y j,..., Y n ) every result s encumbered

More information

INVESTIGATION OF VEHICULAR USERS FAIRNESS IN CDMA-HDR NETWORKS

INVESTIGATION OF VEHICULAR USERS FAIRNESS IN CDMA-HDR NETWORKS 21 22 September 2007, BULGARIA 119 Proceedngs of the Internatonal Conference on Informaton Technologes (InfoTech-2007) 21 st 22 nd September 2007, Bulgara vol. 2 INVESTIGATION OF VEHICULAR USERS FAIRNESS

More information

+ + + - - This circuit than can be reduced to a planar circuit

+ + + - - This circuit than can be reduced to a planar circuit MeshCurrent Method The meshcurrent s analog of the nodeoltage method. We sole for a new set of arables, mesh currents, that automatcally satsfy KCLs. As such, meshcurrent method reduces crcut soluton to

More information

Chapter 12 Inductors and AC Circuits

Chapter 12 Inductors and AC Circuits hapter Inductors and A rcuts awrence B. ees 6. You may make a sngle copy of ths document for personal use wthout wrtten permsson. Hstory oncepts from prevous physcs and math courses that you wll need for

More information

Chapter 4 ECONOMIC DISPATCH AND UNIT COMMITMENT

Chapter 4 ECONOMIC DISPATCH AND UNIT COMMITMENT Chapter 4 ECOOMIC DISATCH AD UIT COMMITMET ITRODUCTIO A power system has several power plants. Each power plant has several generatng unts. At any pont of tme, the total load n the system s met by the

More information

v a 1 b 1 i, a 2 b 2 i,..., a n b n i.

v a 1 b 1 i, a 2 b 2 i,..., a n b n i. SECTION 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS 455 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS All the vector spaces we have studed thus far n the text are real vector spaces snce the scalars are

More information

How Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence

How Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence 1 st Internatonal Symposum on Imprecse Probabltes and Ther Applcatons, Ghent, Belgum, 29 June 2 July 1999 How Sets of Coherent Probabltes May Serve as Models for Degrees of Incoherence Mar J. Schervsh

More information

5.74 Introductory Quantum Mechanics II

5.74 Introductory Quantum Mechanics II MIT OpenCourseWare http://ocw.mt.edu 5.74 Introductory Quantum Mechancs II Sprng 9 For nformaton about ctng these materals or our Terms of Use, vst: http://ocw.mt.edu/terms. 4-1 4.1. INTERACTION OF LIGHT

More information

Regression Models for a Binary Response Using EXCEL and JMP

Regression Models for a Binary Response Using EXCEL and JMP SEMATECH 997 Statstcal Methods Symposum Austn Regresson Models for a Bnary Response Usng EXCEL and JMP Davd C. Trndade, Ph.D. STAT-TECH Consultng and Tranng n Appled Statstcs San Jose, CA Topcs Practcal

More information

Mean Molecular Weight

Mean Molecular Weight Mean Molecular Weght The thermodynamc relatons between P, ρ, and T, as well as the calculaton of stellar opacty requres knowledge of the system s mean molecular weght defned as the mass per unt mole of

More information

NMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING. Dr. Stephen Bruder NMT EE 589 & UNM ME 482/582

NMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING. Dr. Stephen Bruder NMT EE 589 & UNM ME 482/582 NMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING Dr. Stephen Bruder NMT EE 589 & UNM ME 482/582 7. Root Dynamcs 7.2 Intro to Root Dynamcs We now look at the forces requred to cause moton of the root.e. dynamcs!!

More information

IDENTIFICATION AND CORRECTION OF A COMMON ERROR IN GENERAL ANNUITY CALCULATIONS

IDENTIFICATION AND CORRECTION OF A COMMON ERROR IN GENERAL ANNUITY CALCULATIONS IDENTIFICATION AND CORRECTION OF A COMMON ERROR IN GENERAL ANNUITY CALCULATIONS Chrs Deeley* Last revsed: September 22, 200 * Chrs Deeley s a Senor Lecturer n the School of Accountng, Charles Sturt Unversty,

More information

FINANCIAL MATHEMATICS. A Practical Guide for Actuaries. and other Business Professionals

FINANCIAL MATHEMATICS. A Practical Guide for Actuaries. and other Business Professionals FINANCIAL MATHEMATICS A Practcal Gude for Actuares and other Busness Professonals Second Edton CHRIS RUCKMAN, FSA, MAAA JOE FRANCIS, FSA, MAAA, CFA Study Notes Prepared by Kevn Shand, FSA, FCIA Assstant

More information

where the coordinates are related to those in the old frame as follows.

where the coordinates are related to those in the old frame as follows. Chapter 2 - Cartesan Vectors and Tensors: Ther Algebra Defnton of a vector Examples of vectors Scalar multplcaton Addton of vectors coplanar vectors Unt vectors A bass of non-coplanar vectors Scalar product

More information

Calculation of Sampling Weights

Calculation of Sampling Weights Perre Foy Statstcs Canada 4 Calculaton of Samplng Weghts 4.1 OVERVIEW The basc sample desgn used n TIMSS Populatons 1 and 2 was a two-stage stratfed cluster desgn. 1 The frst stage conssted of a sample

More information

EXAMPLE PROBLEMS SOLVED USING THE SHARP EL-733A CALCULATOR

EXAMPLE PROBLEMS SOLVED USING THE SHARP EL-733A CALCULATOR EXAMPLE PROBLEMS SOLVED USING THE SHARP EL-733A CALCULATOR 8S CHAPTER 8 EXAMPLES EXAMPLE 8.4A THE INVESTMENT NEEDED TO REACH A PARTICULAR FUTURE VALUE What amount must you nvest now at 4% compoune monthly

More information

Time Value of Money. Types of Interest. Compounding and Discounting Single Sums. Page 1. Ch. 6 - The Time Value of Money. The Time Value of Money

Time Value of Money. Types of Interest. Compounding and Discounting Single Sums. Page 1. Ch. 6 - The Time Value of Money. The Time Value of Money Ch. 6 - The Tme Value of Money Tme Value of Money The Interest Rate Smple Interest Compound Interest Amortzng a Loan FIN21- Ahmed Y, Dasht TIME VALUE OF MONEY OR DISCOUNTED CASH FLOW ANALYSIS Very Important

More information

1. Fundamentals of probability theory 2. Emergence of communication traffic 3. Stochastic & Markovian Processes (SP & MP)

1. Fundamentals of probability theory 2. Emergence of communication traffic 3. Stochastic & Markovian Processes (SP & MP) 6.3 / -- Communcaton Networks II (Görg) SS20 -- www.comnets.un-bremen.de Communcaton Networks II Contents. Fundamentals of probablty theory 2. Emergence of communcaton traffc 3. Stochastc & Markovan Processes

More information

An Alternative Way to Measure Private Equity Performance

An Alternative Way to Measure Private Equity Performance An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate

More information

ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING

ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING Matthew J. Lberatore, Department of Management and Operatons, Vllanova Unversty, Vllanova, PA 19085, 610-519-4390,

More information

Point cloud to point cloud rigid transformations. Minimizing Rigid Registration Errors

Point cloud to point cloud rigid transformations. Minimizing Rigid Registration Errors Pont cloud to pont cloud rgd transformatons Russell Taylor 600.445 1 600.445 Fall 000-014 Copyrght R. H. Taylor Mnmzng Rgd Regstraton Errors Typcally, gven a set of ponts {a } n one coordnate system and

More information

Introduction to Statistical Physics (2SP)

Introduction to Statistical Physics (2SP) Introducton to Statstcal Physcs (2SP) Rchard Sear March 5, 20 Contents What s the entropy (aka the uncertanty)? 2. One macroscopc state s the result of many many mcroscopc states.......... 2.2 States wth

More information

PERRON FROBENIUS THEOREM

PERRON FROBENIUS THEOREM PERRON FROBENIUS THEOREM R. CLARK ROBINSON Defnton. A n n matrx M wth real entres m, s called a stochastc matrx provded () all the entres m satsfy 0 m, () each of the columns sum to one, m = for all, ()

More information

Finite Math Chapter 10: Study Guide and Solution to Problems

Finite Math Chapter 10: Study Guide and Solution to Problems Fnte Math Chapter 10: Study Gude and Soluton to Problems Basc Formulas and Concepts 10.1 Interest Basc Concepts Interest A fee a bank pays you for money you depost nto a savngs account. Prncpal P The amount

More information

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis The Development of Web Log Mnng Based on Improve-K-Means Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.

More information

1 De nitions and Censoring

1 De nitions and Censoring De ntons and Censorng. Survval Analyss We begn by consderng smple analyses but we wll lead up to and take a look at regresson on explanatory factors., as n lnear regresson part A. The mportant d erence

More information

7.5. Present Value of an Annuity. Investigate

7.5. Present Value of an Annuity. Investigate 7.5 Present Value of an Annuty Owen and Anna are approachng retrement and are puttng ther fnances n order. They have worked hard and nvested ther earnngs so that they now have a large amount of money on

More information

Logistic Regression. Lecture 4: More classifiers and classes. Logistic regression. Adaboost. Optimization. Multiple class classification

Logistic Regression. Lecture 4: More classifiers and classes. Logistic regression. Adaboost. Optimization. Multiple class classification Lecture 4: More classfers and classes C4B Machne Learnng Hlary 20 A. Zsserman Logstc regresson Loss functons revsted Adaboost Loss functons revsted Optmzaton Multple class classfcaton Logstc Regresson

More information

Product-Form Stationary Distributions for Deficiency Zero Chemical Reaction Networks

Product-Form Stationary Distributions for Deficiency Zero Chemical Reaction Networks Bulletn of Mathematcal Bology (21 DOI 1.17/s11538-1-9517-4 ORIGINAL ARTICLE Product-Form Statonary Dstrbutons for Defcency Zero Chemcal Reacton Networks Davd F. Anderson, Gheorghe Cracun, Thomas G. Kurtz

More information

8 Algorithm for Binary Searching in Trees

8 Algorithm for Binary Searching in Trees 8 Algorthm for Bnary Searchng n Trees In ths secton we present our algorthm for bnary searchng n trees. A crucal observaton employed by the algorthm s that ths problem can be effcently solved when the

More information

Production. 2. Y is closed A set is closed if it contains its boundary. We need this for the solution existence in the profit maximization problem.

Production. 2. Y is closed A set is closed if it contains its boundary. We need this for the solution existence in the profit maximization problem. Producer Theory Producton ASSUMPTION 2.1 Propertes of the Producton Set The producton set Y satsfes the followng propertes 1. Y s non-empty If Y s empty, we have nothng to talk about 2. Y s closed A set

More information

Chapter 9. Linear Momentum and Collisions

Chapter 9. Linear Momentum and Collisions Chapter 9 Lnear Momentum and Collsons CHAPTER OUTLINE 9.1 Lnear Momentum and Its Conservaton 9.2 Impulse and Momentum 9.3 Collsons n One Dmenson 9.4 Two-Dmensonal Collsons 9.5 The Center of Mass 9.6 Moton

More information

Calculating the high frequency transmission line parameters of power cables

Calculating the high frequency transmission line parameters of power cables < ' Calculatng the hgh frequency transmsson lne parameters of power cables Authors: Dr. John Dcknson, Laboratory Servces Manager, N 0 RW E B Communcatons Mr. Peter J. Ncholson, Project Assgnment Manager,

More information

The OC Curve of Attribute Acceptance Plans

The OC Curve of Attribute Acceptance Plans The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4

More information

Least Squares Fitting of Data

Least Squares Fitting of Data Least Squares Fttng of Data Davd Eberly Geoetrc Tools, LLC http://www.geoetrctools.co/ Copyrght c 1998-2016. All Rghts Reserved. Created: July 15, 1999 Last Modfed: January 5, 2015 Contents 1 Lnear Fttng

More information

HÜCKEL MOLECULAR ORBITAL THEORY

HÜCKEL MOLECULAR ORBITAL THEORY 1 HÜCKEL MOLECULAR ORBITAL THEORY In general, the vast maorty polyatomc molecules can be thought of as consstng of a collecton of two electron bonds between pars of atoms. So the qualtatve pcture of σ

More information

What is Candidate Sampling

What is Candidate Sampling What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble

More information

A frequency decomposition time domain model of broadband frequency-dependent absorption: Model II

A frequency decomposition time domain model of broadband frequency-dependent absorption: Model II A frequenc decomposton tme doman model of broadband frequenc-dependent absorpton: Model II W. Chen Smula Research Laborator, P. O. Box. 134, 135 Lsaker, Norwa (1 Aprl ) (Proect collaborators: A. Bounam,

More information

Rotation Kinematics, Moment of Inertia, and Torque

Rotation Kinematics, Moment of Inertia, and Torque Rotaton Knematcs, Moment of Inerta, and Torque Mathematcally, rotaton of a rgd body about a fxed axs s analogous to a lnear moton n one dmenson. Although the physcal quanttes nvolved n rotaton are qute

More information

Portfolio Loss Distribution

Portfolio Loss Distribution Portfolo Loss Dstrbuton Rsky assets n loan ortfolo hghly llqud assets hold-to-maturty n the bank s balance sheet Outstandngs The orton of the bank asset that has already been extended to borrowers. Commtment

More information

4 Cosmological Perturbation Theory

4 Cosmological Perturbation Theory 4 Cosmologcal Perturbaton Theory So far, we have treated the unverse as perfectly homogeneous. To understand the formaton and evoluton of large-scale structures, we have to ntroduce nhomogenetes. As long

More information

PRACTICE 1: MUTUAL FUNDS EVALUATION USING MATLAB.

PRACTICE 1: MUTUAL FUNDS EVALUATION USING MATLAB. PRACTICE 1: MUTUAL FUNDS EVALUATION USING MATLAB. INDEX 1. Load data usng the Edtor wndow and m-fle 2. Learnng to save results from the Edtor wndow. 3. Computng the Sharpe Rato 4. Obtanng the Treynor Rato

More information

Section 5.4 Annuities, Present Value, and Amortization

Section 5.4 Annuities, Present Value, and Amortization Secton 5.4 Annutes, Present Value, and Amortzaton Present Value In Secton 5.2, we saw that the present value of A dollars at nterest rate per perod for n perods s the amount that must be deposted today

More information

Linear Circuits Analysis. Superposition, Thevenin /Norton Equivalent circuits

Linear Circuits Analysis. Superposition, Thevenin /Norton Equivalent circuits Lnear Crcuts Analyss. Superposton, Theenn /Norton Equalent crcuts So far we hae explored tmendependent (resste) elements that are also lnear. A tmendependent elements s one for whch we can plot an / cure.

More information

CHAPTER 14 MORE ABOUT REGRESSION

CHAPTER 14 MORE ABOUT REGRESSION CHAPTER 14 MORE ABOUT REGRESSION We learned n Chapter 5 that often a straght lne descrbes the pattern of a relatonshp between two quanttatve varables. For nstance, n Example 5.1 we explored the relatonshp

More information

substances (among other variables as well). ( ) Thus the change in volume of a mixture can be written as

substances (among other variables as well). ( ) Thus the change in volume of a mixture can be written as Mxtures and Solutons Partal Molar Quanttes Partal molar volume he total volume of a mxture of substances s a functon of the amounts of both V V n,n substances (among other varables as well). hus the change

More information

Texas Instruments 30X IIS Calculator

Texas Instruments 30X IIS Calculator Texas Instruments 30X IIS Calculator Keystrokes for the TI-30X IIS are shown for a few topcs n whch keystrokes are unque. Start by readng the Quk Start secton. Then, before begnnng a specfc unt of the

More information

Lecture 2: Single Layer Perceptrons Kevin Swingler

Lecture 2: Single Layer Perceptrons Kevin Swingler Lecture 2: Sngle Layer Perceptrons Kevn Sngler kms@cs.str.ac.uk Recap: McCulloch-Ptts Neuron Ths vastly smplfed model of real neurons s also knon as a Threshold Logc Unt: W 2 A Y 3 n W n. A set of synapses

More information

Inter-Ing 2007. INTERDISCIPLINARITY IN ENGINEERING SCIENTIFIC INTERNATIONAL CONFERENCE, TG. MUREŞ ROMÂNIA, 15-16 November 2007.

Inter-Ing 2007. INTERDISCIPLINARITY IN ENGINEERING SCIENTIFIC INTERNATIONAL CONFERENCE, TG. MUREŞ ROMÂNIA, 15-16 November 2007. Inter-Ing 2007 INTERDISCIPLINARITY IN ENGINEERING SCIENTIFIC INTERNATIONAL CONFERENCE, TG. MUREŞ ROMÂNIA, 15-16 November 2007. UNCERTAINTY REGION SIMULATION FOR A SERIAL ROBOT STRUCTURE MARIUS SEBASTIAN

More information

A Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy S-curve Regression

A Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy S-curve Regression Novel Methodology of Workng Captal Management for Large Publc Constructons by Usng Fuzzy S-curve Regresson Cheng-Wu Chen, Morrs H. L. Wang and Tng-Ya Hseh Department of Cvl Engneerng, Natonal Central Unversty,

More information

SIMPLE LINEAR CORRELATION

SIMPLE LINEAR CORRELATION SIMPLE LINEAR CORRELATION Smple lnear correlaton s a measure of the degree to whch two varables vary together, or a measure of the ntensty of the assocaton between two varables. Correlaton often s abused.

More information

Solution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt.

Solution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt. Chapter 9 Revew problems 9.1 Interest rate measurement Example 9.1. Fund A accumulates at a smple nterest rate of 10%. Fund B accumulates at a smple dscount rate of 5%. Fnd the pont n tme at whch the forces

More information

How To Calculate The Accountng Perod Of Nequalty

How To Calculate The Accountng Perod Of Nequalty Inequalty and The Accountng Perod Quentn Wodon and Shlomo Ytzha World Ban and Hebrew Unversty September Abstract Income nequalty typcally declnes wth the length of tme taen nto account for measurement.

More information

The quantum mechanics based on a general kinetic energy

The quantum mechanics based on a general kinetic energy The quantum mechancs based on a general knetc energy Yuchuan We * Internatonal Center of Quantum Mechancs, Three Gorges Unversty, Chna, 4400 Department of adaton Oncology, Wake Forest Unversty, NC, 7157

More information

Formulating & Solving Integer Problems Chapter 11 289

Formulating & Solving Integer Problems Chapter 11 289 Formulatng & Solvng Integer Problems Chapter 11 289 The Optonal Stop TSP If we drop the requrement that every stop must be vsted, we then get the optonal stop TSP. Ths mght correspond to a ob sequencng

More information

Do Hidden Variables. Improve Quantum Mechanics?

Do Hidden Variables. Improve Quantum Mechanics? Radboud Unverstet Njmegen Do Hdden Varables Improve Quantum Mechancs? Bachelor Thess Author: Denns Hendrkx Begeleder: Prof. dr. Klaas Landsman Abstract Snce the dawn of quantum mechancs physcst have contemplated

More information

Global stability of Cohen-Grossberg neural network with both time-varying and continuous distributed delays

Global stability of Cohen-Grossberg neural network with both time-varying and continuous distributed delays Global stablty of Cohen-Grossberg neural network wth both tme-varyng and contnuous dstrbuted delays José J. Olvera Departamento de Matemátca e Aplcações and CMAT, Escola de Cêncas, Unversdade do Mnho,

More information

Loop Parallelization

Loop Parallelization - - Loop Parallelzaton C-52 Complaton steps: nested loops operatng on arrays, sequentell executon of teraton space DECLARE B[..,..+] FOR I :=.. FOR J :=.. I B[I,J] := B[I-,J]+B[I-,J-] ED FOR ED FOR analyze

More information

Face Verification Problem. Face Recognition Problem. Application: Access Control. Biometric Authentication. Face Verification (1:1 matching)

Face Verification Problem. Face Recognition Problem. Application: Access Control. Biometric Authentication. Face Verification (1:1 matching) Face Recognton Problem Face Verfcaton Problem Face Verfcaton (1:1 matchng) Querymage face query Face Recognton (1:N matchng) database Applcaton: Access Control www.vsage.com www.vsoncs.com Bometrc Authentcaton

More information

Chapter 11 Torque and Angular Momentum

Chapter 11 Torque and Angular Momentum Chapter 11 Torque and Angular Momentum I. Torque II. Angular momentum - Defnton III. Newton s second law n angular form IV. Angular momentum - System of partcles - Rgd body - Conservaton I. Torque - Vector

More information

n + d + q = 24 and.05n +.1d +.25q = 2 { n + d + q = 24 (3) n + 2d + 5q = 40 (2)

n + d + q = 24 and.05n +.1d +.25q = 2 { n + d + q = 24 (3) n + 2d + 5q = 40 (2) MATH 16T Exam 1 : Part I (In-Class) Solutons 1. (0 pts) A pggy bank contans 4 cons, all of whch are nckels (5 ), dmes (10 ) or quarters (5 ). The pggy bank also contans a con of each denomnaton. The total

More information

Lecture 3: Annuity. Study annuities whose payments form a geometric progression or a arithmetic progression.

Lecture 3: Annuity. Study annuities whose payments form a geometric progression or a arithmetic progression. Lecture 3: Annuty Goals: Learn contnuous annuty and perpetuty. Study annutes whose payments form a geometrc progresson or a arthmetc progresson. Dscuss yeld rates. Introduce Amortzaton Suggested Textbook

More information

The issue of June, 1925 of Industrial and Engineering Chemistry published a famous paper entitled

The issue of June, 1925 of Industrial and Engineering Chemistry published a famous paper entitled Revsta Cêncas & Tecnologa Reflectons on the use of the Mccabe and Thele method GOMES, João Fernando Perera Chemcal Engneerng Department, IST - Insttuto Superor Técnco, Torre Sul, Av. Rovsco Pas, 1, 1049-001

More information

MOLECULAR PARTITION FUNCTIONS

MOLECULAR PARTITION FUNCTIONS MOLECULR PRTITIO FUCTIOS Introducton In the last chapter, we have been ntroduced to the three man ensembles used n statstcal mechancs and some examples of calculatons of partton functons were also gven.

More information

University Physics AI No. 11 Kinetic Theory

University Physics AI No. 11 Kinetic Theory Unersty hyscs AI No. 11 Knetc heory Class Number Name I.Choose the Correct Answer 1. Whch type o deal gas wll hae the largest alue or C -C? ( D (A Monatomc (B Datomc (C olyatomc (D he alue wll be the same

More information

Forecasting the Direction and Strength of Stock Market Movement

Forecasting the Direction and Strength of Stock Market Movement Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye cjngwe@stanford.edu mchen5@stanford.edu nanye@stanford.edu Abstract - Stock market s one of the most complcated systems

More information

The Application of Fractional Brownian Motion in Option Pricing

The Application of Fractional Brownian Motion in Option Pricing Vol. 0, No. (05), pp. 73-8 http://dx.do.org/0.457/jmue.05.0..6 The Applcaton of Fractonal Brownan Moton n Opton Prcng Qng-xn Zhou School of Basc Scence,arbn Unversty of Commerce,arbn zhouqngxn98@6.com

More information

Latent Class Regression. Statistics for Psychosocial Research II: Structural Models December 4 and 6, 2006

Latent Class Regression. Statistics for Psychosocial Research II: Structural Models December 4 and 6, 2006 Latent Class Regresson Statstcs for Psychosocal Research II: Structural Models December 4 and 6, 2006 Latent Class Regresson (LCR) What s t and when do we use t? Recall the standard latent class model

More information

A Probabilistic Theory of Coherence

A Probabilistic Theory of Coherence A Probablstc Theory of Coherence BRANDEN FITELSON. The Coherence Measure C Let E be a set of n propostons E,..., E n. We seek a probablstc measure C(E) of the degree of coherence of E. Intutvely, we want

More information

Heuristic Static Load-Balancing Algorithm Applied to CESM

Heuristic Static Load-Balancing Algorithm Applied to CESM Heurstc Statc Load-Balancng Algorthm Appled to CESM 1 Yur Alexeev, 1 Sher Mckelson, 1 Sven Leyffer, 1 Robert Jacob, 2 Anthony Crag 1 Argonne Natonal Laboratory, 9700 S. Cass Avenue, Argonne, IL 60439,

More information

Description of the Force Method Procedure. Indeterminate Analysis Force Method 1. Force Method con t. Force Method con t

Description of the Force Method Procedure. Indeterminate Analysis Force Method 1. Force Method con t. Force Method con t Indeternate Analyss Force Method The force (flexblty) ethod expresses the relatonshps between dsplaceents and forces that exst n a structure. Prary objectve of the force ethod s to deterne the chosen set

More information

Risk-based Fatigue Estimate of Deep Water Risers -- Course Project for EM388F: Fracture Mechanics, Spring 2008

Risk-based Fatigue Estimate of Deep Water Risers -- Course Project for EM388F: Fracture Mechanics, Spring 2008 Rsk-based Fatgue Estmate of Deep Water Rsers -- Course Project for EM388F: Fracture Mechancs, Sprng 2008 Chen Sh Department of Cvl, Archtectural, and Envronmental Engneerng The Unversty of Texas at Austn

More information

Analysis of Reactivity Induced Accident for Control Rods Ejection with Loss of Cooling

Analysis of Reactivity Induced Accident for Control Rods Ejection with Loss of Cooling Analyss of Reactvty Induced Accdent for Control Rods Ejecton wth Loss of Coolng Hend Mohammed El Sayed Saad 1, Hesham Mohammed Mohammed Mansour 2 Wahab 1 1. Nuclear and Radologcal Regulatory Authorty,

More information

Kinetic Energy-Based Temperature Computation in Non-Equilibrium Molecular. Dynamics Simulation. China. Avenue, Kowloon, Hong Kong, China

Kinetic Energy-Based Temperature Computation in Non-Equilibrium Molecular. Dynamics Simulation. China. Avenue, Kowloon, Hong Kong, China Knetc Energy-Based Temperature omputaton n on-equlbrum Molecular Dynamcs Smulaton Bn Lu, * Ran Xu, and Xaoqao He AML, Department of Engneerng Mechancs, Tsnghua Unversty, Bejng 00084, hna Department of

More information

Software Alignment for Tracking Detectors

Software Alignment for Tracking Detectors Software Algnment for Trackng Detectors V. Blobel Insttut für Expermentalphysk, Unverstät Hamburg, Germany Abstract Trackng detectors n hgh energy physcs experments requre an accurate determnaton of a

More information

SUPPLIER FINANCING AND STOCK MANAGEMENT. A JOINT VIEW.

SUPPLIER FINANCING AND STOCK MANAGEMENT. A JOINT VIEW. SUPPLIER FINANCING AND STOCK MANAGEMENT. A JOINT VIEW. Lucía Isabel García Cebrán Departamento de Economía y Dreccón de Empresas Unversdad de Zaragoza Gran Vía, 2 50.005 Zaragoza (Span) Phone: 976-76-10-00

More information

Kinetic Energy-Based Temperature Computation in Non-Equilibrium Molecular Dynamics Simulation

Kinetic Energy-Based Temperature Computation in Non-Equilibrium Molecular Dynamics Simulation Copyrght 202 Amercan Scentfc Publshers All rghts reserved Prnted n the Unted States of Amerca Journal of Computatonal and Theoretcal Nanoscence Vol. 9,428 433, 202 Knetc Energy-Based Temperature Computaton

More information