First variation. (onevariable problem) January 21, 2015


 Melanie Richard
 1 years ago
 Views:
Transcription
1 First vrition (onevrible problem) Jnury 21, 2015 Contents 1 Sttionrity of n integrl functionl Euler eqution (Optimlity conditions) First integrls: Three specil cses Vritionl problem s limit of finitedimensionl problem Sttionrity of boundry terms Vrition of boundry conditions Broken extreml nd the WeierstrssErdmn condition Functionl dependent on higher derivtives 13 4 Nonfixed intervl Trnsverslity condition Extreml broken t n unknown point Severl minimizers Euler equtions nd first integrls Vritionl boundry conditions Geometric optics Geometric optics problem Snell s lw of refrction Brchistochrone Miniml surfce of revolution
2 Since, however, the rules for isoperimetric curves or, in modern terms, extreml problems were not sufficiently generl, the fmous Euler undertook the tsk of reducing ll such investigtions to generl method which he gve in the work Essy on new method of determining the mxim nd minim of indefinite integrl formuls ; n originl work in which the profound science of the clculus shines through. Even so, while the method is ingenious nd rich, one must dmit tht it is not s simple s one might hope in work of pure nlysis. In Essy on new method of determining the mxim nd minim of indefinite integrl formuls, by Lgrnge, Sttionrity of n integrl functionl The technique ws developed by Euler, who lso introduced the nme Clculus of vritions in The method is bsed on n nlysis of infinitesiml vritions of minimizing curve. The min scheme of the vritionl method is s follows: ssuming tht the optiml curve u(x) exists mong smooth (twicedifferentible curves), we compre the optiml curve with closeby trjectories u(x) + δu(x), where vrition δu(x) is smll in some sense. Using the smllness of δu, we simplify the comprison, deriving necessry conditions for the optiml trjectory u(x). The method is quite nlogous to the computing of differentil d f(x) = f (x) dx function f(x), minimum point is solution of the eqution f (x) = 0 becuse of the rbitrriness of differentil dx. Generlly speking, vritionl methods yield to only necessry conditions of optimlity becuse it is ssumed tht the compred trjectories re close to ech other; on the other hnd, these methods re pplicble to gret vriety of extreml problems clled vritionl problems. Similrly to the clculus problems, the proof of optimlity of trjectory should be complemented by inequlities tht distinguish minimum from mximum of sddle points nd n existence theorem tht either gurntees tht the minimizer is indeed twicedifferentible function or suggests n lterntive pproch (relxting or regulriztion techniques) 1.1 Euler eqution (Optimlity conditions) Consider the problem clled the simplest problem of the clculus of vritions min u I(u), I(u) = F (x, u, u )dx, u() = u, u(b) = u b, (1) Here integrnt F is clled Lgrngin, it is ssumed to be twice differentible function of its three rguments, I(u) is clled the cost functionl. It is not known priori whether the minimizer u 0 (x) is smooth, but let us ssume tht it is twice differentible function of x. 2
3 Exmple 1.1 Consider the re of the surfce of revolution round the xes OX tht is supported by two prllel coxil circles of rdii R nd R b, the distnce between the centers of circles is b. According to the clculus, the re J of the surfce is A(r) = π r(x) 1 + r (x) 2 dx, where r(x) is the vrible distnce from the OXxes. The problem of miniml re of such surfce I = min r(x) A(u), r() = R, r(b) = R b is vritionl problem, r(x) is n unknown function. To derive necessry condition of optimlity of minimizer u 0 we use the ides of clculus, computing n nlog of the derivtive of I with respect to u (clled the functionl derivtive) nd setting it to zero. We suppose tht function u 0 = u 0 (x) is minimizer nd replce u 0 with test function u 0 + δu. The test function u 0 + δu stisfies the sme boundry conditions s u 0. If indeed u 0 is minimizer, then the increment of the cost is δi(u 0 ) = I(u 0 + δu) I(u 0 ) is nonnegtive: δi(u 0 ) = 1 0 (F (x, u 0 + δu, (u 0 + δu) ) F (x, u 0, u 0))dx 0. (2) If δu is not specified, the eqution (2) is not too informtive. However, prticulr form of the vrition δu simplifies (2) nd llows for finding n eqution for the minimizer. Clculus of vritions suggests set of tests tht differ by vrious form of vritions δu. Euler Lgrnge Equtions The sttionry vritionl condition (the Euler Lgrnge eqution) is derived ssuming tht the vrition δu is infinitesimlly smll nd loclized: { ρ(x) if x [x0, x δu = 0 + ε], (3) 0 if x is outside of [x 0, x 0 + ε]. Here ρ(x) is continuous function tht vnishes t points x 0 nd x 0 + ε nd is constrined s follows: ρ(x) < ε, ρ (x) < ε x, ρ(x 0 ) = ρ(x 0 + ε) = 0 (4) The integrnd t the perturbed trjectory cn be expnded into Tylor series, F (x, u 0 + δu, (u 0 + δu) ) = F (x, u 0, u 0) + (x, u 0, u 0) δu + (x, u 0, u 0) δu + o(δu, δu ) 3
4 Here, δu is derivtive of the vrition δu, δu = (δu), o(δu, δu ) denotes higher order terms which norms re smller thn δu nd δu when ε 0. Substituting this expression into (2) nd collecting liner (with respect to ε) terms, we rewrite (2) s ( ) δi(u 0 ) = (δu) + (δu) dx + o(ε) 0. (5) where The F is clculted t the exmined trjectory u 0. To simplify nottions, we omit index ( 0 ) below. The vritions δu nd (δu) re mutully dependent nd (δu) cn be expressed in terms of δu. Integrtion by prts of the underlined term in (5) gives nd we obtin (δu) dx = 0 δi(u 0 ) = ( d dx ) δu dx + S F (x, u, u )δu dx + δu where S F denotes the functionl derivtive, δu x=b x= x=b x= + o(ε), (6) S F (x, u, u ) = d dx +. (7) The nonintegrl term in the righthnd side of (6) is zero, becuse the boundry vlues of u re prescribed s u() = u nd u(b) = u b ; therefore their vritions δu x= nd δu x=b equl zero, δu x= = 0, δu x=b = 0 Due to the rbitrriness of δu, we rrive t the following Theorem 1.1 (Sttionrity condition) Any differentible nd bounded minimizer u 0 of the vritionl problem (1) is solution to the boundry vlue problem S F (x, u, u ) = d dx = 0 x (, b); u() = u, u(b) = u b, (8) clled the Euler Lgrnge eqution. The Euler Lgrnge eqution is lso clled the sttionry condition of optimlity becuse it expresses sttionrity of the vrition. Using the chin rule, the lefthnd side of eqution (8) cn be rewritten to the form of explicit secondrnk differentil eqution. S F (x, u, u ) = 2 F 2 u + 2 F u + 2 F x In this derivtion, it is indirectly ssumed tht the extreml u(t) is twice differentible function of x. (9) 4
5 Exmple 1.2 Compute the Euler eqution for the problem I = min u(x) 1 We compute = u, The minimizer u 0 (x) is 0 F (x, u, u )dx u(0) = 1, u(1) =, F = 1 2 (u ) u2 = u nd the Euler eqution becomes u u = 0 in (0, 1), u(0) = 1, u(1) =. u 0 (x) = cosh(x) + cosh(1) sinh(1) sinh(x). Remrk 1.1 The sttionrity test lone does not llow to conclude whether u is true minimizer or even to conclude tht solution to (8) exists. For exmple, the function u tht mximizes I(u) stisfies the sme Euler Lgrnge eqution. The tests tht distinguish miniml trjectory from other sttionry trjectories re discussed in Chpter??. Remrk 1.2 The reltion (6) request tht differentil eqution (8) is stisfied in ech infinitesiml intervl but not in in ech point. The eqution my be not defined in isolted points, if F or its prtil derivtives re not defined in these points. The minimizer cn chnge its vlues t severl points, or even t set of zero mesure without effecting the objective functionl. Such solutions re clled wek solutions?? of differentil eqution. The definition of the wek solution nturlly rises from the vritionl formultion tht does not check the behvior of the minimizer in every p[oint but only t intervls of nonzero mesure. In mbiguous cses, one should specify in wht sense (Riemnn, Lebesgue) the integrl is defined nd define of vrition ccordingly. 1.2 First integrls: Three specil cses In severl cses, the secondorder Euler eqution (8) cn be integrted t lest once. These re the cses when Lgrngin F (x, u, u ) does not depend on one of the rguments. Assume tht F = F (x, u), nd the mini Lgrngin is independent of u miztion problem is I(u) = min u(x) 1 0 F (x, u)dx (10) In this cse, Euler eqution (8) becomes n lgebric reltion for u = 0 (11) The vrition does not involve integrtion by prts, nd the minimizer does not need to be continuous. 5
6 Monimizer u(x) is determined in ech point independently of neighboring points. The boundry conditions in (8) re stisfied by possible jumps of the extreml u(x) in the end points; these conditions do not ffect the objective functionl t ll. Exmple 1.3 Consider the problem I(u) = min u(x) 1 0 (u sin x) 2 dx, u(0) = 1; u(1) = 0. The miniml vlue J(u 0 ) = 0 corresponds to the discontinuous minimizer sin x if 0 x 1 u 0 (x) = 1 if x = 0 0 if x = 1 Remrk 1.3 Formlly, the discontinuous minimizer contrdicts the ssumption posed when the Euler eqution ws derived. To be consistent, we need to repet the derivtion of the necessry condition for the problem (10) without ny ssumption on the continuity of the minimizer. This derivtion is quite obvious. Lgrngin is independent of u If Lgrngin does not depend on u, F = F (x, u ), Euler eqution (8) becomes S F (x, u, u ) = d dx = 0 x (, b); u() = u, u(b) = u b, (12) it cn be integrted once: = constnt (13) The differentil eqution (13) for u is the first integrl of eqution (8); it defines quntity tht stys constnt everywhere long n optiml trjectory. It remins to integrte the first order eqution (13) nd determine the constnts of integrtion from the boundry conditions. Exmple 1.4 Consider the problem I(u) = min u(x) 1 (compre with exmple 1.3) The first integrl is Integrting, we find the minimizer, 0 (u cos x) 2 dx, u(0) = 1; u(1) = 0. = u (x) cos x = C u(x) = sin x + C 1 x + C 2. 6
7 The constnts C 1 nd C 2 re found from nd the boundry conditions: C 2 = 1, C 1 = 1 sin 1, minimizer u 0 nd the cost of the problem become, respectively u 0 (x) = sin x (sin 1 + 1)x + 1 I(u 0 ) = (sin 1 + 1) 2. Notice tht the Lgrngin in the exmple (1.3) is the squre of difference between the minimizer u nd function sin x, nd the Lgrngin in the exmple (1.4) is the squre of difference of their derivtives. In the problem (1.3), the minimizer coincides with sin x, nd jumps to stisfy the prescribed boundry vlues. The minimizer u in the exmple (1.4) cnnot jump. Indeed, continuous pproximtion of derivtive u of discontinuous functionis unfunded in the proximity of the point of discontinuity, such behvior increses the objective functionl, nd therefore it is nonoptiml. Lgrngin is independent of x If F = F (u, u ), eqution (8) hs the first integrl: W (u, u ) = constnt (14) where W (u, u ) = u F Indeed, compute the xderivtive of W (u, u ) which must be equl to zero by virtue of (14): d dx W (u, u ) = [ ( 2 u + F u u + 2 F u 2 )] u u = 0 where the expression in squre brckets is the derivtive of the first term of W (u, u ). Cncelling the equl terms, we bring this eqution to the form ( u 2 F 2 u + 2 F u ) = 0 (15) The expression in prenthesis coincides with the lefthndside term S(x, u, u ) of the Euler eqution in the form (9), simplified for the considered cse (F is independent of x, F = F (u, u )). W is constnt t ny solution u(x) of Euler eqution. Insted of solving the Euler eqution, we my solve the firstorder eqution W = C. Exmple 1.5 Consider the Lgrngin for hrmonic oscilltor: F = 1 2 [ (u ) 2 ω 2 u 2] 7
8 The Euler eqution is The first integrl is u + ω 2 u = 0 W = 1 2 ( ω 2 u 2 + (u ) 2) = C 2 = constnt Let us check the constncy of the first integrl. The solution u of the Euler eqution is equl u = A cos(ωx) + B sin(ωx) where A nd B re constnts. Substituting the solution into the expression for the first integrl, we compute W = (u ) 2 + ω 2 u 2 = [ Aω sin(cx) + Bω cos(ωx)] 2 +ω 2 [A cos(ωx) + B sin(ωx)] 2 = ω 2 (A 2 + B 2 ) We hve shown tht W is constnt t the optiml trjectory. Notice tht W is the totl energy (sum of the potentil nd kinetic energy) of the oscilltor. 1.3 Vritionl problem s limit of finitedimensionl problem Here, we derive Euler eqution for finitedimensionl problem tht pproximte the simplest vritionl problem min I(u), I(u) = u(x) F (x, u, u )dx Consider clss of piecewise constnt discontinuous functions U N : ū(x) U N, if ū(x) = u i x [ + in ] (b ) A function ū in U N is defined by n Ndimensionl vector {u 1,... u N }. Rewriting the vritionl problem for this clss of minimizers, we replce the derivtive u (x) with finite difference Diff (u i ) Diff (u i ) = 1 (u i u i 1 ), = b N ; (16) when N, this opertor tends to the derivtive. The vritionl problem is replced by finitedimensionl optimiztion problem: min u 1,...,u N 1 I N I N = N F i (u i, z i ), z i = Diff (z i ) = 1 (z i z i 1 ) (17) i=1 Compute the sttionry conditions for the minimum of I N (u) I N i = 0, i = 1...., N. 8
9 Only two terms, F i nd F i+1, in the bove sum depend on u i : the first depends on u i directly nd lso through the opertor z i = Diff (u i ), nd the second only through z i+1 = Diff (u i+1 ): df i = i + i 1 du i i z i, df i+1 = i+1 1 du i z i+1. df k = 0, k i, k i + 1 du i Collecting the terms, we write the sttionry condition with respect to u i : I N = i + 1 ( i i i z ) i+1 = 0 z or, reclling the definition (16) of Diff opertor, the form I N = ( ) i i+1 Diff = 0. i i z The initil nd the finl point u 0 nd u N enter the difference scheme only once, therefore the optimlity conditions re different. They re, respectively, N+1 Diff (u N+1 ) = 0; o Diff (u 0 ) = 0. Formlly pssing to the limit N, Diff d dx, z u replcing the index ( i ) with continuous vrible x, vector of vlues {u k } of the piecewise constnt function with the continuous function u(x), difference opertor Diff with the derivtive d dx ; then nd N F i (u i, Diff u i ) i=1 i i Diff ( i+1 z ) F (x, u, u )dx. d d x The conditions for the end points become the nturl vritionl conditions: (0) = 0, (T ) = 0, So fr, we followed the forml scheme of necessry conditions, thereby tcitly ssuming tht ll derivtives of the Lgrngin exist, the increment of the functionl is correctly represented by the first term of its power expnsion, nd the limit of the sequence of finitedimensionl problems exist nd does not depend on the prtition {x 1,... x N } if only x k x k 1 0 for ll k. We lso indirectly ssume tht the Euler eqution hs t lest one solution consistent with boundry conditions. 9
10 If ll the mde ssumptions re correct, we obtin curve tht might be minimizer becuse it cnnot be disproved by the sttionry test. In other terms, we find tht is there is no other closeby clssicl curve tht correspond to smller vlue of the functionl. Remrk 1.4 This sttement bout the optimlity seems to be rther wek but this is exctly wht the clculus of vrition cn give us. On the other hnd, the vritionl conditions re universl nd, being ppropritely used nd supplemented by other conditions, led to very detiled description of the extreml s we show lter in the course. Remrk 1.5 In the bove procedure, we ssume tht the limits of the components of the vector {u k } represent vlues of smooth function in the closeby points x 1,..., x N. At the other hnd, u k re solutions of optimiztion problems with the coefficients tht slowly vry with the number k. We need to nswer the question whether the solution of minimiztion problem tends to is differentible function of x; tht is whether the limit u k u k 1 lim k x k x k 1 exists nd this is not lwys the cse. We ddress this question lter in Chpter?? 2 Sttionrity of boundry terms 2.1 Vrition of boundry conditions Vritionl conditions nd nturl conditions The vlue of minimizer my not be specified on one or both ends of the intervl [, b]. In this cse, these vlues re clculted by the minimiztion of the gol functionl together with the minimizer. Consider vritionl problem where the boundry vlue t the right end b of the intervl is not defined nd the functionl directly depends on this vlue, min I(u), I(u) = u(x):u()=u F (x, u, u )dx + f(u(b)) (18) The Euler eqution for the problem remin the sme, S(x, u, u ) = 0, but this time it must be supplemented by vritionl boundry condition tht comes from the requirement of the sttionrity of the minimizer with respect to vrition of the boundry term. This term is ( + f ) δu(b) x=b The first term comes from the integrtion by prt in the derivtion of Euler eqution, see (6), nd the second is the vrition of the lst term in the objective 10
11 functionl (18): δf(u) = f δu. Becuse the sign of the vrition δu(b) is rbitrry, the sttionrity condition hs the form ( + f ) = 0. (19) This equlity provides the missing boundry condition t the endpoint x = b for the second order Euler eqution. Similr condition cn be derived for the point x = if the vlue t this point is not prescribed. Exmple 2.1 Minimize the functionl x=b 1 1 I(u) = min u 0 2 (u ) 2 dx + Au(1), u(0) = 0 Here, we wnt to minimize the endpoint vlue nd we do not wnt the trjectory be too steep. The Euler eqution u = 0 must be integrted with boundry conditions u(0) = 0 nd (see (19)) u (1) + A = 0 The extreml is stright line, u = Ax. The cost of the problem is I = 1 2 A2. If f = 0, the condition (19) becomes = 0 (20) x=b nd it is clled the nturl boundry condition. Exmple 2.2 Consider the Lgrngin F = (x)(u ) 2 + φ(x, u) where (x) 0 The nturl boundry condition is u x=b = Broken extreml nd the WeierstrssErdmn condition The clssicl derivtion of the Euler eqution requires the existence of ll second prtils of F, nd the solution u of the secondorder differentil eqution is required to be twicedifferentible. In some problems, F is only piecewise twice differentible; in this cse, the extreml consists of severl curves solutions of the Euler eqution tht re computed t the intervls of smoothness. We consider the question: How to join these pieces together? The first continuity condition is continuity of the (differentible) minimizer u(x) [u] + = 0 long the optiml trjectory u(x) (21) Here [z] + = z + z denotes the jump of the vrible z. The extreml u is differentible, the first derivtive u exists t ll points of the trjectory. This derivtive does not need to be continuous. Insted, Euler eqution requests the differentibility of to ensure the existence of the term d in the Euler eqution. dx 11
12 Integrting the sttionrity condition (8), we obtin sttionrity in the integrl form x x ( d S F (x, u, u (x, u, u ) )dx = dx (x, u, ) u ) dx = 0 or If (x, u, u ) x = (x, u, u ) x 0 dx + (x, u, u ) (22) x= is bounded t the optiml trjectory, the righthnd side is continuous function of x, nd so is the lefthnd side. This requirement of continuity of n optiml trjectory is clled the WeierstrssErdmn condition on broken extreml. Theorem 2.1 At ny point of the optiml trjectory, the WeierstrssErdmn condition must be stisfied: [ ] + = 0 long the optiml trjectory u(x). (23) Exmple 2.3 (Broken extreml) Consider the Lgrngin F = 1 2 c(x)(u ) { c1 if x [, x 2 u2, c(x) = ), c 2 if x (x, b] where x is point in (, b). The Euler eqution is held everywhere in (, b) except of the point x, d dx [c 1u ] u = 0 if x [, x ) d dx [c 2u ] u = 0 if x (x, b], At x = x, the continuity conditions hold, u(x 0) = u(x + 0), c 1 u (x 0) = c 2 u (x + 0). The derivtive u (x) itself is discontinuous; its jump is determined by the jump in the coefficients: u (x + 0) u (x 0) = c 1 c 2 These conditions together with the Euler eqution nd boundry conditions determine the optiml trjectory. 12
13 3 Functionl dependent on higher derivtives Consider more generl type vritionl problem with the Lgrngin tht depends on the minimizer nd its first nd second derivtive, J = F (x, u, u, u )dx The Euler eqution is derived similrly to the simplest cse: The vrition of the gol functionl is δj = ( δu + δu + δu ) dx Integrting by prts the second term nd twice the third term, we obtin ( δj = d ) dx + d2 dx 2 δu dx [ + δu + δu d ] x=b δu dx The sttionrity condition becomes the fourthorder differentil eqution x= (24) d 2 dx 2 d dx + = 0 (25) supplemented by two boundry conditions on ech end, δu [ = 0, δu d ] dx = 0 t x = nd x = b (26) or by the correspondent min conditions posed on the minimizer u nd its derivtive u t the end points. Exmple 3.1 The equilibrium of n elstic bending bem correspond to the solution of the vritionl problem min u(x) L 0 ( 1 2 (E(x)u ) 2 q(x)u)dx (27) where u(x) is the deflection of the point x of the bem, E(x) is the elstic stiffness of the mteril tht cn vry with x, q(x) is the lod tht bends the bem. Any of the following kinemtic boundry conditions cn be considered t ech end of the bem. (1) A clmped end: u() = 0, u () = 0 (2) simply supported end u() = 0. (3) free end (no kinemtic conditions). 13
14 Let us find eqution for equilibrium nd the missing boundry conditions in the second nd third cse. The Euler eqution (25) becomes The equtions (26) become (Eu ) q = 0 (, b) δu (Eu ) = 0, δu ((Eu ) ) = 0 In the cse (2) (simply supported end), the complementry vritionl boundry condition is Eu = 0, it expresses vnishing of the bending momentum t the simply supported end. In the cse (3), the vritionl conditions re Eu = 0 nd (Eu ) = 0; the lst expresses vnishing of the bending force t the free end (the bending momentum vnishes here s well). Generliztion The Lgrngin F (x, u, u,..., u (n)) dependent on first k derivtives of udependent on higher derivtives of u is considered similrly. The sttionry condition is the 2korder differentil eqution d dx dk ( 1)k dx k (k) = 0 supplemented t ech end x = nd x = b of the trjectory by k boundry conditions [ ] δu (k 1) (k) x=,b = 0 [ d ] δu (k 2) (k 1) dx (k) x=,b = 0... [ d dx d(k 1) ( 1)k dx (k 1) ] δu (k) x=,b = 0 If u is vector minimizer, u cn be replced by vector but the structure of the necessry conditions sty the sme. 4 Nonfixed intervl 4.1 Trnsverslity condition Free boundry Consider now the cse when the intervl [, b] is not fixed, but the end point is to be chosen so tht it minimizes the functionl. Let us 14
15 compute the difference between the two functionls over two different intervls = δi = +δx (F (x, u + δu, u + δu ) F (x, u, u )) dx + The second integrl is estimted s +δx b F (x, u + δu, u + δu )dx +δx b F (x, u, u )dx F (x, u + δu, u + δu )dx F (x, u + δu, u + δu )dx = F (x, u, u ) x=b δx + o( δu, δx ) nd the first integrl is computed s before with integrtion by prts: S F (x, u, u )δu dx + δu(b) = 0 x=b 1. Suppose tht no boundry conditions re imposed t the minimizer t the point x = b. Becuse of rbitrriness of δx nd δu, we rrive t the conditions: S F (x, u, u ) = 0 x (, b), = 0, x=b nd F (x, u, u ) x=b = 0. (28) Euler eqution for the extreml stisfies n extr boundry condition (28), but hs lso n dditionl degree of freedom: unknown coordinte b. Exmple 4.1 Consider the problem s ( ) 1 min u(x),s 2 u 2 u + x dx u(0) = 0. 0 The Euler eqution u + 1 = 0 nd the condition t u(0) = 0 corresponds to the extreml u = 1 2 x2 + Ax, u = x + A where A is prmeter. The condition = u = 0 t the unknown right end x = s gives s = A. The trnsverslity condition F = 0 or ( u + x) x=a=s = 1 2 s2 s 2 + s = s (1 12 ) s = 0 We find s = 2, u = 1 2 x2 + 2x. 2. Next, consider the problem in which the boundry dt t x = b is prescribed, u = β, but the vlue of b is not known. In the perturbed trjectory, the 15
16 boundry condition is u(b + δx) = β. The vlue of u(b + δx) is n extrpoltion of u(x) s follows u(b + δx) = u(b) + u (b)δx + o( δu, δx ) Therefore, the vlue (u + δu) x=b depends on δx, u(b) = β u (b)δx or δu(b) = u (b)δx. Combining the depending on δx terms, we obtin the condition ( F (b)) x=b u δx Becuse δx is rbitrry, the boundry conditions re: u = β nd ( F (x, u, u ) u ) x=b = 0. (29) Remrk 4.1 Notice tht the lefthnd side expression in (29) t the unknown end is identicl to the expression for the first integrl (14) of the problem in the cse when F (u, u ) is independent of x. This integrl is constnt long n optiml trjectory which mens tht the problem with Lgrngin F (u, u ) does not stisfy (29) t n isolted point. 3. Finlly, consider the problem when the rjectory ends t curve. If the boundry vlue depends on b, u(b) = φ(b) (30) then the vritions δu nd δx re bounded: δu = φ δx. The two sttionrity conditions t the end point ( δu = 0 nd F (x, u, u ) u ) δx = 0 together (30) gives the conditions ( F (u φ ) ) x=b = 0 nd u(b) = φ(b). (31) The next exmple dels with constrint t the unknown length of the intervl nd the boundry dt. Exmple 4.2 Find the shortest pth between the origin nd curve φ(x). The pth length is given by I = min y(x),s s y 2 dx, u(0) = 0 At the end point x the pth meets the curve, therefore y(s) = φ(s) or δy = φ (s)δs (32) 16
17 The Euler eqution y = y = C 1 + y 2 shows tht y = constnt, therefore the pth is stright line, y = Ax s expected. At the point s, the vrition is ( u ) y F δx + y y δy = 1 δx + y δu 1 + y y 2 The sttionrity gives the reltion δx + y δu = 0. Compring it with the constrint (32), we conclude tht y (s)φ (s) = 1, or tht the shortest pth is stright line orthogonl to the curve φ(x), s it is expected. 4.2 Extreml broken t n unknown point Combining the techniques, we my ddress the problem of en extreml broken in n unknown point. The position of this point is determined from the minimiztion requirement. Assume tht Lgrngin hs the form { F (x, u, u F (x, u, u ) = ) if x (, ξ) F + (x, u, u ) if x (ξ, b) where ξ is n unknown point in the intervl (, b) of the integrtion. The Euler eqution is { SF (u) if x (, ξ) S F (u) = S F+ (u) if x (ξ, b) The sttionrity conditions t the unknown point ξ consist of sttionrity of the trjectory + = (33) nd sttionrity of the position of the trnsit point F + (u) u + + = F (u) u. (34) or F + (u) F (u) = (u + u ). (35) They re derived by the sme procedure s the conditions t the end point. The vrition δx of the trnsit point δx = δx + = δx increses the first prt of the trjectory nd decreses the second prt, or vise vers, which explins the structure of the sttionrity conditions. In prticulr, if the Lgrngin is independent of x, the condition (34) expresses the constncy of the first integrl (14) t the point ξ. 17
18 Exmple 4.3 Consider the problem with Lgrngin { F (x, u, u + u ) = 2 + b + u 2 if x (, ξ) u 2 if x (ξ, b) nd boundry conditions The Euler eqution is S F (u) = u() = 0, u(b) = 1 { + u b + u = 0 if x (, ξ) u = 0 if x (ξ, b) The solution to this eqution tht stisfies the boundry conditions is ( ) u + (x) = C 1 sinh b+ + (x ) if x (, ξ) u (x) = C 2 (x b) + 1 if x (ξ, b) ; it depends on three constnts ξ, C 1, nd C 2 (Notice tht the coefficient does not enter the Euler equtions). These constnts re determined from three conditions t the unknown point ξ which express (1) continuity of the extreml (2) WeierstrssErdmn condition (3) trnsverslity condition u + (ξ) = u (ξ), + u +(ξ) = u (ξ), + (u +(ξ)) 2 + b + u(ξ) 2 = (u (ξ)) 2. The trnsverslity condition sttes the equlity of two first integrl. It is simplified to C 2 1b + = C 2 2 From the WeierstrssErdmn condition, we find C 1 + b + cosh q = C 2, where q = b + + (ξ ) The first condition nd the definition of q llows for determintion of ξ: cosh q = +, ξ = + + b + cosh 1 + Finlly, we define constnts C 1 nd C 2 from the continuity C 1 sinh q = 1 + C 2 (ξ b) nd trnsverslity conditions: C 1 = sinh q b + (ξ b), C b+ 2 = sinh q b + (ξ b), 18
19 5 Severl minimizers 5.1 Euler equtions nd first integrls The Euler eqution cn be nturlly generlized to the problem with the vectorvlued minimizer I(u) = min u F (x, u, u )dx, (36) where x is point in the intervl [, b] nd u = (u 1 (x),..., u n (x)) is vector function. We suppose tht F is twice differentible function of its rguments. Let us compute the vrition δi(u) equl to I(u + δu) I(u), ssuming tht the vrition of the extreml nd its derivtive is smll nd loclized. To compute the Lgrngin t the perturbed trjectory u+δu, we use the expnsion F (x, u + δu, u + δu ) = F (x, u, u ) + n i=1 i δu i + n δu i i=1 i We cn perform n independent vritions of ech component of vector u pplying vritions δ i u = (0,..., δu i..., 0). The increment of the objective functionl should be zero for ech of these vrition, otherwise the functionl cn be decresed by one of them. The sttionrity condition for ny of considered vritions coincides with the oneminimizer cse. δ i I(u) = ( δu i + δu i i i ) dx 0 i = 1,..., n. Proceeding s before, we obtin the system of n secondorder differentil equtions, d = 0, i = 1,... n (37) dx i nd the boundry term i n x=b δu i = 0 (38) i=1 i If the vlue of u i () or u i (b) is not prescribed, the nturl boundry conditions x= or i x=b, respectively, must be stisfied. i The vector form of the system (37), S F (u) = d dx x=b = 0, δut = 0 (39) is identicl to the sclr Euler eqution. This system corresponds to n definition of differentition with respect to vector rgument u. x= Exmple 5.1 Consider the problem with the integrnd x= F = 1 2 u u 2 2 u 1 u u2 1 (40) 19
20 The system of sttionrity conditions is computed to be d dx d dx = u 1 + u 2 u 1 = 0 2 = (u 2 u 1 ) = 0. If consists of two differentil equtions of second order for two unknowns u 1 (x) nd u 2 (x). First integrls The first integrls tht re estblished for the specil cses of the sclr Euler eqution, cn lso be derived for the vector eqution. 1. If F is independent of u k, then one of the Euler equtions degenertes into lgebric reltion: = 0 k nd the one of differentil eqution in (37) becomes n lgebric one. The vrible u k (x) cn be discontinuous function of x in n optiml solution. Since the Lgrngin is independent of u k, the discontinuities of u k(x) my occur long the optiml trjectory. 2. If F is independent of u k, the first integrl exists: k = constnt For instnce, the second eqution in Exmple 5.1 cn be integrted nd replced by u 2 u 1 = constnt 3. If F is independent of x, F = F (u, u ) then first integrl exist Here W = u T F = constnt (41) T u = n i=1 u i i For the Exmple 5.1, this first integrl is computed to be ( 1 W = u u 2 (u 2 u 1) 2 u u 2 2 u 1 u ) 2 u2 1 = 1 ( u u 2 2 u 2 1) = constnt These three cses do not exhust ll possible first integrls for vector cse. For exmple, if the functionl depends only on, sy (u 1 + u 2 ), one cn hope to find new invrints by chnging the vribles. We discuss this mtter below in Sections?? nd??. 20
On the Robustness of Most Probable Explanations
On the Robustness of Most Probble Explntions Hei Chn School of Electricl Engineering nd Computer Science Oregon Stte University Corvllis, OR 97330 chnhe@eecs.oregonstte.edu Adnn Drwiche Computer Science
More informationWhen Simulation Meets Antichains (on Checking Language Inclusion of NFAs)
When Simultion Meets Antichins (on Checking Lnguge Inclusion of NFAs) Prosh Aziz Abdull 1, YuFng Chen 1, Lukáš Holík 2, Richrd Myr 3, nd Tomáš Vojnr 2 1 Uppsl University 2 Brno University of Technology
More informationAllAtom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins
3586 J. Phys. Chem. B 1998, 102, 35863616 AllAtom Empiricl Potentil for Moleculr Modeling nd Dynmics Studies of Proteins A. D. McKerell, Jr.,*,, D. Bshford,, M. Bellott,, R. L. Dunbrck, Jr.,, J. D. Evnseck,,
More informationLIFE AS POLYCONTEXTURALITY *)
Ferury 2004 LIFE AS POLYCONTEXTURALITY *) y Gotthrd Günther Kein Leendiges ist ein Eins, Immer ist's ein Vieles. (Goethe) Prt I : The Concept of Contexture A gret epoch of scientific trdition is out to
More informationThe Tradeoff Between Inequality and Growth
ANNALS OF ECONOMICS AND FINANCE 4, 329 345 2003 The Trdeoff Between Inequlity nd Growth Jess Benhbib Deprtment of Economics, New York University 269 Mercer Street, 7th floor, New York, NY 10003, USA. Emil:
More informationSome Techniques for Proving Correctness of Programs which Alter Data Structures
Some Techniques for Proving Correctness of Progrms which Alter Dt Structures R. M. Burstll Deprtment of Mchine Intelligence University of Edinburgh 1. INTRODUCTION Consider the following sequence of instructions
More informationMATH 150 HOMEWORK 4 SOLUTIONS
MATH 150 HOMEWORK 4 SOLUTIONS Section 1.8 Show tht the product of two of the numbers 65 1000 8 2001 + 3 177, 79 1212 9 2399 + 2 2001, nd 24 4493 5 8192 + 7 1777 is nonnegtive. Is your proof constructive
More informationMagnetism from Conductors, and Enhanced NonLinear Phenomena
Mnetism from Conductors, nd Enhnced NonLiner Phenomen JB Pendry, AJ Holden, DJ Roins, nd WJ Stewrt Astrct  We show tht microstructures uilt from nonmnetic conductin sheets exhiit n effective mnetic
More informationTheImpactoftheNation smost WidelyUsedInsecticidesonBirds
TheImpctoftheNtion smost WidelyUsedInsecticidesonBirds Neonicotinoid Insecticides nd Birds The Impct of the Ntion s Most Widely Used Insecticides on Birds Americn Bird Conservncy, Mrch 2013 Grsshopper
More informationEach copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission.
Do Artifcts Hve Politics? Author(s): Lngdon Winner Source: Dedlus, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? (Winter, 1980), pp. 121136 Published by: The MIT Press on behlf Americn Acdemy
More informationNAEYC Early Childhood Program Standards and Accreditation Criteria & Guidance for Assessment
NAEYC Erly Childhood Progrm Stndrds nd Accredittion Criteri & Guidnce for Assessment This document incorportes the lnguge of ll NAEYC Erly Childhood Progrm Stndrds nd Accredittion Criteri, including 39
More informationDoes the chimpanzee have a theory of mind? 30 years later
Review Does the chimpnzee hve theory of mind? 30 yers lter Josep Cll nd Michel Tomsello Mx Plnck Institute for Evolutionry Anthropology, Deutscher Pltz 6, D04103 Leipzig, Germny On the 30th nniversry
More informationTHE INSCRIPTIONS FROM TEMPLE XIX AT PALENQUE
DAVID STUART THE INSCRIPTIONS FROM TEMPLE XIX AT PALENQUE The Inscriptions fromtemple XIX t Plenque A Commentry The Inscriptions from TempleXIX t Plenque A Commentry By Dvid Sturt Photogrphs y Jorge Pérez
More informationControllability and Observability of Partial Differential Equations: Some results and open problems
Controllability and Observability of Partial Differential Equations: Some results and open problems Enrique ZUAZUA Departamento de Matemáticas Universidad Autónoma 2849 Madrid. Spain. enrique.zuazua@uam.es
More informationSpaceTime Approach to NonRelativistic Quantum Mechanics
R. P. Feynman, Rev. of Mod. Phys., 20, 367 1948 SpaceTime Approach to NonRelativistic Quantum Mechanics R.P. Feynman Cornell University, Ithaca, New York Reprinted in Quantum Electrodynamics, edited
More informationINTRODUCTION TO THE THEORY OF BLACK HOLES
ITPUU09/11 SPIN09/11 INTRODUCTION TO THE THEORY OF BLACK HOLES internet: Gerard t Hooft Institute for Theoretical Physics Utrecht University and Spinoza Institute Postbox 80.195 3508 TD Utrecht, the
More informationWhen action is not least
When action is not least C. G. Gray a GuelphWaterloo Physics Institute and Department of Physics, University of Guelph, Guelph, Ontario N1GW1, Canada Edwin F. Taylor b Department of Physics, Massachusetts
More informationFullMouth Adhesive Rehabilitation of a Severely Eroded Dentition: The ThreeStep Technique. Part 1.
CLINICAL APPLICATION Puliction FullMouth Adhesive Rehilittion of Severely Eroded Dentition: The ThreeStep Technique. Prt 1. Frncesc Vilti, MD, DMD, MSc Senior Lecturer, Deprtment of Fixed Prosthodontics
More informationIntroduction to Differential Calculus. Christopher Thomas
Mathematics Learning Centre Introduction to Differential Calculus Christopher Thomas c 1997 University of Sydney Acknowledgements Some parts of this booklet appeared in a similar form in the booklet Review
More informationONEDIMENSIONAL RANDOM WALKS 1. SIMPLE RANDOM WALK
ONEDIMENSIONAL RANDOM WALKS 1. SIMPLE RANDOM WALK Definition 1. A random walk on the integers with step distribution F and initial state x is a sequence S n of random variables whose increments are independent,
More informationFinancial Ties between DSMIV Panel Members and the Pharmaceutical Industry
Regulr Article DOI: 10.1159/000091772 Finncil Ties between DSMIV Pnel Members nd the Phrmceuticl Industry Lis Cosgrove b Sheldon Krimsky Mnish Vijyrghvn Lis Schneider University of Msschusetts, Boston,
More informationSynthesis of Transportation Fuels from Biomass: Chemistry, Catalysts, and Engineering
4044 Chem. Rev. 2006, 106, 4044 4098 Synthesis of Trnsporttion Fuels from Biomss: Chemistry, Ctlysts, nd Engineering George W. Huber, Sr Iborr, nd Avelino Corm* Instituto de Tecnologí Químici, UPVCSIC,
More informationDIFFERENT GENETIC CHARACTERISTICS OF PLASMODIUM FALCZPARUM ISOLATES COLLECTED DURING SUCCESSIVE CLINICAL MALARIA EPISODES IN SENEGALESE CHILDREN
Am. J. Trp. Med. Hyg., 5(6), 1996, pp. 626 Copyright Q 1996 by The Americn Society of Tropicl Medicine nd Hygiene ' DIFFERENT GENETIC CHARACTERISTICS F PLASMDIUM FALCZPARUM ISLATES CLLECTED DURING SUCCESSIVE
More informationARB THERE TImRB COUNTEREXAMPLES TO THE TIm CU)SURE CLOSURE PRINCIPLE?
JONATHAN VOGEL ARB THERE TmRB COUNTEREXAMPLES TO THE Tm CU)SURE CLOSURE PRNCPLE? PRNCPLB7 Very ten, person cn't know proposition propositon without wilhout knowing vrious logicl consequences tht ht proposition.
More informationMUSTHAVE MATH TOOLS FOR GRADUATE STUDY IN ECONOMICS
MUSTHAVE MATH TOOLS FOR GRADUATE STUDY IN ECONOMICS William Neilson Department of Economics University of Tennessee Knoxville September 29 289 by William Neilson web.utk.edu/~wneilson/mathbook.pdf Acknowledgments
More informationChapter I. Concerning the Nature and Propagation of Sound.
Euler's Disserttion De Sono : E002. Trnslted & Annotted by In Bruce 1. Chter I. Concerning the Nture nd Progtion of Sound. 1. The exlntion of sound by the old hilosohers ws very obscure nd confused, so
More informationThe Backpropagation Algorithm
7 The Backpropagation Algorithm 7. Learning as gradient descent We saw in the last chapter that multilayered networks are capable of computing a wider range of Boolean functions than networks with a single
More informationErgodicity and Energy Distributions for Some Boundary Driven Integrable Hamiltonian Chains
Ergodicity and Energy Distributions for Some Boundary Driven Integrable Hamiltonian Chains Peter Balint 1, Kevin K. Lin 2, and LaiSang Young 3 Abstract. We consider systems of moving particles in 1dimensional
More informationAn Elementary Introduction to Modern Convex Geometry
Flavors of Geometry MSRI Publications Volume 3, 997 An Elementary Introduction to Modern Convex Geometry KEITH BALL Contents Preface Lecture. Basic Notions 2 Lecture 2. Spherical Sections of the Cube 8
More informationHow many numbers there are?
How many numbers there are? RADEK HONZIK Radek Honzik: Charles University, Department of Logic, Celetná 20, Praha 1, 116 42, Czech Republic radek.honzik@ff.cuni.cz Contents 1 What are numbers 2 1.1 Natural
More information