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Vector Differential Equations: Defective Coefficient Matrix And Defective Coefficient Matrices and Linear DE Math 240 Defective Coefficient Vector Differential Equations: Matrices Linear DE Defective Coefficient Matrix Linear differential operators and Familiar stuff Next week Higher Order Linear Differential Equations Math 240 | Calculus III Summer 2013, Session II Thursday, August 1, 2013 Defective Coefficient Agenda Matrices and Linear DE Math 240 Defective Coefficient Matrices Linear DE Linear differential 1. Vector differential equations: defective coefficient matrix operators Familiar stuff Next week 2. Linear differential equations of order n Linear differential operators Familiar stuff A taste of what's to come Defective Coefficient Introduction Matrices and Linear DE −1 Math 240 We've learned how to find a matrix S so that S AS is almost a diagonal matrix. Recall that diagonalization allows us to solve Defective Coefficient linear systems of diff. eqs. because we can solve the equation Matrices 0 Linear DE y = ay: Linear differential operators Jordan form will give us small systems that look like Familiar stuff Next week 0 y1 = ay1 + y2; 0 y2 = ay2: Is there an obvious solution? at y1(t) = e and y2(t) = 0: A nontrivial one? Yes! at at y1(t) = te and y2(t) = e : Write this in the vector form y 1 0 1 = eat t + : y2 0 1 Defective Coefficient 2 × 2 defective systems Matrices and Linear DE Math 240 Switching back to the standard basis, these are the solutions at at Defective x1(t) = e v1 and x2(t) = e (tv1 + v2) Coefficient Matrices where v2; v1 is a chain of generalized eigenvectors. Linear DE Linear differential Example operators Familiar stuff Find the general solution to Next week 0 1 x0 = Ax;A = : −9 6 1. The single eigenvalue is λ = 3. 2. Chain of generalized e-vectors is v1 = (1; 3), v2 = (0; 1). (A − 3I)v1 = 0 and (A − 3I)v2 = v1: 3. Fundamental set of solutions is therefore 3t 3t x1(t) = e v1 and x2(t) = e (tv1 + v2) : Defective Coefficient Longer chains Matrices and Linear DE Math 240 Defective What about chains of generalized eigenvectors longer than 2? Coefficient Matrices If A is an n × n matrix with eigenvalue λ and chain of Linear DE generalized eigenvectors Linear differential p−1 p−2 operators v = (A − λI) v; v = (A − λI) v;::: Familiar stuff 1 2 Next week vp−1 = (A − λI)v; vp = v; check that the following are solutions to x0 = Ax: λt x1(t) = e v1 λt x2(t) = e (v2 + tv1) . λt 1 p−1 xp(t) = e vp + tvp−1 + ··· + (p−1)! t v1 Defective Coefficient Longer chains Matrices and Linear DE Math 240 Defective Coefficient Matrices We should also check that fx1(t);:::; xp(t)g is independent. Linear DE We know that fv1;:::; vpg is independent, that is, Linear differential operators det v1 v2 ··· vn 6= 0: Familiar stuff Next week Theorem The set fx1(t);:::; xp(t)g is a linearly independent subset of Vn(I). Thus, we can construct a fundamental set of solutions by applying the foregoing construction to each chain of generalized eigenvectors. Defective Coefficient Matrices and Linear DE Example Math 240 Find the general solution to x0 = Ax if Defective 21 2 03 Coefficient Matrices A = 41 1 25 : Linear DE 0 −1 1 Linear differential operators Familiar stuff Next week 1. Only eigenvalue is λ = 1. 2. Yesterday we found the chain v1 = (−2; 0; 1); v2 = (0; −1; 0); v3 = (−1; 0; 0): 3. Thus, solutions are t x1(t) = e v1; t x2(t) = e (v2 + tv1) ; t 1 2 x3(t) = e v3 + tv2 + 2 t v3 : Defective Coefficient Example Matrices and 0 Linear DE Find the general solution to x = Ax if Math 240 22 1 0 0 0 03 Defective 60 2 0 0 0 07 Coefficient 6 7 Matrices 60 0 5 0 0 07 A = 6 7 : Linear DE 60 0 0 5 1 07 Linear 6 7 differential 0 0 0 0 5 1 operators 4 5 Familiar stuff 0 0 0 0 0 5 Next week 1. Eigenvalues are λ1 = 2 and λ2 = 5. 2. Eigenvectors and generalized eigenvectors are Ae1 = 2e1; Ae2 = 2e2 + e1; Ae3 = 5e3; Ae4 = 5e4; Ae5 = 5e5 + e4; Ae6 = 5e6 + e5: 3. Our fundamental set of solutions is 2t 2t 5t x1(t) = e e1; x2(t) = e (e2 + te1) ; x3(t) = e e3; 5t 5t x4(t) = e e4; x5(t) = e (e5 + te4) ; 5t 1 2 x6(t) = e e6 + te5 + 2 t e6 : Defective Coefficient Introduction Matrices and Linear DE Math 240 Defective Coefficient Matrices We now turn our attention to solving linear differential Linear DE equations of order n. The general form of such an equation is Linear differential (n) (n−1) 0 operators a0(x)y + a1(x)y + ··· + an−1(x)y + an(x)y = F (x); Familiar stuff Next week where a0; a1; : : : ; an; and F are functions defined on an interval I. The general strategy is to reformulate the above equation as Ly = F; where L is an appropriate linear transformation. In fact, L will be a linear differential operator. Defective Coefficient Linear differential operators Matrices and Linear DE 1 0 Math 240 Recall that the mapping D : C (I) ! C (I) defined by 0 Defective D(f) = f is a linear transformation. This D is called the Coefficient derivative operator. Higher order derivative operators Matrices k k 0 Linear DE D : C (I) ! C (I) are defined by composition: Linear differential k k−1 operators D = D ◦ D ; Familiar stuff Next week so that dkf Dk(f) = : dxk A linear differential operator of order n is a linear combination of derivative operators of order up to n, n n−1 L = D + a1D + ··· + an−1D + an; defined by (n) (n−1) 0 Ly = y + a1y + ··· + an−1y + any; where the ai are continous functions of x. L is then a linear transformation L : Cn(I) ! C0(I). (Why?) Defective Coefficient Examples Matrices and Linear DE Math 240 Defective Coefficient Example Matrices If L = D2 + 4xD − 3x, then Linear DE Linear 00 0 differential Ly = y + 4xy − 3xy: operators Familiar stuff We have Next week L (sin x) = − sin x + 4x cos x − 3x sin x; L x2 = 2 + 8x2 − 3x3: Example If L = D2 − e3xD; determine 1. L 2x − 3e2x = −12e2x − 2e3x + 6e5x 2. L 3 sin2 x = −3e3x sin 2x − 6 cos 2x Defective Coefficient Homogeneous and nonhomogeneous equations Matrices and Linear DE Math 240 Consider the general n-th order linear differential equation Defective Coefficient (n) (n−1) 0 Matrices a0(x)y + a1(x)y + ··· + an−1(x)y + an(x)y = F (x); Linear DE where a 6= 0 and a ; a ; : : : ; a ; and F are functions on an Linear 0 0 1 n differential operators interval I. Familiar stuff Next week If a0(x) is nonzero on I, then we may divide by it and relabel, obtaining (n) (n−1) 0 y + a1(x)y + ··· + an−1(x)y + an(x)y = F (x); which we rewrite as Ly = F (x); n n−1 where L = D + a1D + ··· + an−1D + an. If F (x) is identically zero on I, then the equation is homogeneous, otherwise it is nonhomogeneous. Defective Coefficient The general solution Matrices and Linear DE Math 240 If we have a homogeneous linear differential equation Defective Coefficient Ly = 0; Matrices Linear DE its solution set will coincide with Ker(L). In particular, the Linear differential kernel of a linear transformation is a subspace of its domain. operators Familiar stuff Next week Theorem The set of solutions to a linear differential equation of order n is a subspace of Cn(I). It is called the solution space. The dimension of the solutions space is n. Being a vector space, the solution space has a basis fy1(x); y2(x); : : : ; yn(x)g consisting of n solutions. Any element of the vector space can be written as a linear combination of basis vectors y(x) = c1y1(x) + c2y2(x) + ··· + cnyn(x): This expression is called the general solution. Defective Coefficient The Wronskian Matrices and Linear DE Math 240 We can use the Wronskian Defective Coefficient y1(x) y2(x) ··· yn(x) Matrices 0 0 0 y1(x) y2(x) ··· yn(x) Linear DE W [y1; y2; : : : ; yn](x) = . Linear . .. differential . operators (n−1) (n−1) (n−1) Familiar stuff y (x) y (x) ··· yn (x) Next week 1 2 to determine whether a set of solutions is linearly independent. Theorem Let y1; y2; : : : ; yn be solutions to the n-th order differential equation Ly = 0 whose coefficients are continuous on I. If W [y1; y2; : : : ; yn](x) = 0 at any single point x 2 I, then fy1; y2; : : : ; yng is linearly dependent. To summarize, the vanishing or nonvanishing of the Wronskian on an interval completely characterizes the linear dependence or independence of a set of solutions to Ly = 0. Defective Coefficient The Wronskian Matrices and Linear DE Math 240 Defective Coefficient Matrices Example Linear DE 2 Linear Verify that y1(x) = cos 2x and y2(x) = 3(1 − 2 sin x) are differential operators 00 Familiar stuff solutions to the differential equation y + 4y = 0 on (−∞; 1). Next week Determine whether they are linearly independent on this interval. 2 cos 2x 3(1 − 2 sin x) W [y1; y2](x) = −2 sin 2x −12 sin x cos x = −6 sin 2x cos 2x + 6 sin 2x cos 2x = 0 They are linearly dependent. In fact, 3y1 − y2 = 0. Defective Coefficient Nonhomogeneous equations Matrices and Linear DE Math 240 Consider the nonhomogeneous linear differential equation Ly = F .
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