( Bx + f(x, y) Cy + g(x, y)

Size: px
Start display at page:

Download "( Bx + f(x, y) Cy + g(x, y)"

Transcription

1 Chapter 6 Center manifold reduction The previous chaper gave a rather detailed description of bifurcations of equilibria and fixed points in generic one-parameter families of ODEs and maps with minimal possible dimension of the state space. These results are also applicable to general n-dimensional systems because of the existence of a low-dimensional invariant manifold near the bifurcation, on which all interesting dynamics in the state space is concentrated. The present chapter is devoted to the constructive definition of this invariant Center Manifold and to a proof that reduction to it yields all the relevant information. 6.1 Center manifolds for maps Consider a C k -smooth map ( x y ) ( Bx + f(x, y) Cy + g(x, y) ), (6.1) where x R nc+nu, y R ns, and f(x, y) and g(x, y) have neither constant nor linear terms. Suppose that the (n c + n u ) (n c + n u ) matrix B has n c eigenvalues with λ = 1 and n u eigenvalues with λ > 1, while all n s eigenvalues of the n s n s matrix C satisfy λ < 1. Note, that the eigenvalues with λ = 1 are often called critical eigenvalues. Let n = n s + n c + n u. Theorem 6.1 (Existence of a Global Center-Unstable Manifold) Assume f(0, 0) = 0, g(0, 0) = 0, and that the functions f and g have sufficiently small bounds and sufficiently small Lipschitz bounds Lip(f), Lip(g) on R n. Then the map (6.1) has an invariant manifold W cu = {(x, h(x)) : x R nc+nu }, where h : R nc+nu R ns is a bounded and Lipschitz map satisfying h(0) = 0. If, in addition, f and g are C k -functions (k 1) for which all derivatives up to order k have small bounds and the k-th derivative has a small Lipschitz bound then h is a C k map satisfying h x (0) =

2 242 CHAPTER 6. CENTER MANIFOLD REDUCTION Definition 6.2 W cu is called a center unstable manifold of the fixed point (0, 0) of (6.1). Proof of Theorem 6.1: (Step 1) We can assume that the norms on, respectively, R ns and R nc+nu are such that α = C < 1, β = B 1 < 1 α. (6.2) Write (6.1) in the form u Au + R(u) = ( P(x, y) Q(x, y) ), (6.3) where u = ( x y ) R nc+nu R ns, A = ( B 0 0 C ) ( f(u), R(u) = g(u) ). On R n we use the norm u = max{ x, y }. By assumption we have constants K, L 0 such that R(u) K, R(u) R(v) L u v (6.4) for all u, v R n. We impose the following smallness conditions α + K < 1, β(α + 2L) < 1, α + (1 + β)l < 1. (6.5) (Step 2) Introduce a set M 0 of maps H : R nc+nu R ns, satisfying the following conditions: (i) H C(R nc+nu, R ns ) and H = sup H(x) 1; x R nc+nu (ii) H(x 1 ) H(x 2 ) x 1 x 2 for all x 1,2 R nc+nu ; (iii) H(0) = 0. The set M 0 is a complete metric space with respect to the distance ρ(h 1, H 2 ) = H 1 H 2. The invariance under the map (6.3)of the (Lipschitz) manifold for given H M 0 means that W = { (x, H(x)) : x R nc+nu} Q(ξ, H(ξ)) = H(P(ξ, H(ξ))) (6.6)

3 6.1. CENTER MANIFOLDS FOR MAPS 243 H T(H) H(x) [T(H)](x) H(ξ) 0 ξ x Figure 6.1: Hadamard s Graph Transform: x = P(ξ, H(ξ)) and [T(H)](x) = Q(ξ, H(ξ)). should hold for all ξ R nc+nu. Rewrite (6.6) as a fixed-point equation T(H) = H, where T is such that the graph of H is mapped to the graph of T(H) by the mapping (6.3), see Figure 6.1. Accordingly the graph of a fixed point of T is invariant under (6.3). The formal definition of T proceeds as follows. For each x R nc+nu and each H M 0, there is a unique ξ = S(x, H), such that Indeed, from H M 0 and (6.4) we have and Theorem 3.20 applies, since From Theorem 3.20 we also obtain x = P(ξ, H(ξ)) = Bξ + f(ξ, H(ξ)). (6.7) f(ξ 1, H(ξ 1 )) f(ξ 2, H(ξ 2 )) L ξ 1 ξ 2, L < β 1 = B 1 1. S(x 1, H) S(x 2, H) 1 β 1 L x 1 x 2 (6.8) for all x 1, x 2 R nc+nu. Define now the Hadamard Graph Transform H T(H) by the formula (see Figure 6.1) or, equivalently, [T(H)](x) = Q(S(x, H), H(S(x, H))) [T(H)](x) = CH(S(x, H)) + g(s(x, H), H(S(x, H))). (6.9)

4 244 CHAPTER 6. CENTER MANIFOLD REDUCTION Clearly, the equation H = T(H) is equivalent to (6.6), since they are related by the transformation x = P(ξ, H(ξ)) with the inverse ξ = S(x, H). (Step 3) We prove now that T(M 0 ) M 0. (i) Using α + K < 1, we have [T(H)](x) CH(S(x, H)) + g(s(x, H), H(S(x, H))) α H + K 1. (ii) From (6.4) and (6.8) we obtain the Lipschitz estimate [T(H)](x 1 ) [T(H)](x 2 ) α β 1 L x 1 x 2 + L max( S(x 1, H) S(x 2, H), Note that α + L β 1 L follows from (6.5). (iii) Finally, it is obvious that S(0, H) = 0 and hence H(S(x 1, H)) H(S(x 2, H)) ) α + L β 1 L x 1 x 2. [T(H)](0) = 0. (Step 4) Now we verify that T is a contraction. Indeed, for any two H 1,2 M 0 and any x R nc+nu, set Then ξ 1 = S(x, H 1 ), ξ 2 = S(x, H 2 ). ξ 1 ξ 2 = B 1 (f(ξ 1, H 1 (ξ 1 )) f(ξ 2, H 2 (ξ 2 ))) βl max( ξ 1 ξ 2, H 1 (ξ 1 ) H 1 (ξ 2 ) + H 1 (ξ 2 ) H 2 (ξ 2 ) ) ( (α + L) 1 + βl ) H 1 H 2. 1 βl Thus, With this estimate, (6.9) gives ξ 1 ξ 2 βl 1 βl H 1 H 2. T(H 1 ) T(H 2 ) (α + L)( ξ 1 ξ 2 + H 1 H 2 ) α + L 1 βl H 1 H 2. This implies that T is a contraction, since α + L 1 βl < 1

5 6.1. CENTER MANIFOLDS FOR MAPS 245 This proves the existence of the unique global Lipschitz-continuous center-unstable manifold given by the graph of h, where h M 0 is the fixed point of T. (Step 5) To prove that h C k (R nc+nu, R ns ), introduce a set: M k = { H C k (R nc+nu, R ns ) : H(0) = 0, sup x R nc+nu H x (j)(x) 1, j = 0, 1,..., k, H x (j)(x) H x (j)(y) x y for all x, y R nc+nu, j = k }, which is a complete metric space with respect to the distance corresponding to the norm H k, = max H x (j), 0 j k for each k = 0, 1, 2,.... Here x (j) = x j 1 1 x j 2 2 x jn n, j = j 1 + j j n, so that H x (j) = j H x j 1 1 x j 2 2 x jn n We do not give the proof for general k, but indicate the main steps for the case k = 1. Let L 1 = Lip(R u ) be a Lipschitz bound for the derivative. In the following we will impose several smallness conditions on the constants K, L, L 1. First, note that the Lipschitz Inverse Function Theorem 3.20 can be extended to show C k -smoothness of the inverse function if the given function is of class C k. Therefore, the function ξ = S(x, H) defined by (6.7) is C k -smooth. Differentiating (6.7) and suppressing the arguments ξ and (ξ, H(ξ)) we find (cf. (6.8)) As before this yields. I n = (B + f x + f y H x )S x. (6.10) S x γ = Then differentiation of [T(H)](x) leads to Using (6.11) this implies the bound 1 β 1 L. (6.11) T(H) x = CH x S x + g x S x + g y H x S x. (6.12) T(H) x α + L β 1 L. (6.13) Next we prove a Lipschitz estimate for S x. Consider x 1, x 2 R nc+nu and write Sx j = S x (x j, H), fx j = f x (S(x j, h), H(S(x j, H))),... for short. From (6.10) and (6.7) we then obtain the estimate = S 1 x S 2 x = B 1 [ f 1 x(s 1 x S 2 x) + (f 1 x f 2 x)s 2 x + f 1 yh 1 x(s 1 x S 2 x) + f 1 y (H1 x H2 x )S2 x + (f1 y f2 y )H2 x f2 x] β [L + L 1 γ + L 1 + Lγ x 1 x 2 + L 1 Lγ].

6 246 CHAPTER 6. CENTER MANIFOLD REDUCTION Therefore, S x (x 1, H) S x (x 2, H) L S,x x 1 x 2, (6.14) where L S,x = Lγ(β 1 L L 1 γl 1 (1 + L)) 1. From this inequality and (6.12) we obtain a Lipschitz estimate for T(H) x T(H) 1 x T(H)2 x = CH1 x (S1 x S2 x ) + C(H1 x H2 x )S2 x + g 1 x(s 1 x S 2 x) + (g 1 x g 2 x)s 2 x + g 1 yh 1 x(s 1 x S 2 x) + g 1 y (H1 x H2 x )S2 x + (g1 y g2 y )H2 x S2 x (αl S,x + αγ + LL S,x + L 1 γ 2 + LL S,x + Lγ + L 1 γ 2 ) x 1 x 2. Since αγ < 1 this gives the Lipschitz constant 1 for L, L 1 sufficiently small. In order to prove contraction we consider ξ j = S(x, H j ), j = 1, 2 for two functions H 1,2 M 1. Let us write Sx 1 = S x (x, H 1 ), fx 1 = f x (S 1, H 1 (S 1 )),... and use fx f 1 x 2 L 1 (1+γ) H 1 H 2. Then (6.10) leads to an estimate of = Sx 1 S2 x as follows hence = B 1 (f 1 x (S1 x S2 x ) + (f1 x f2 x )S2 x + f1 y H 1,x(S 1 x S2 x ) + f 1 y(h 1,x H 2,x )S 2 x + (f 1 y f 2 y)h 2,x S 2 x β [L + L 1 (1 + Lγ) H 1 H 2 + L + Lγ H 1 H 2 + L 1 (1 + Lγ)γ H 1 H 2 ], S x (x, H 1 ) S x (x, H 2 ) L S,H H 1 H 2, (6.15) where L S,H = (L 1 (1 + Lγ)(1 + γ) + Lγ) (β 1 2L) 1. Finally, we use αγ < 1 and arrive at a contraction with respect to T(H 1 ) x T(H 2 ) x = CH 1,x (S 1 x S2 x ) + C(H 1,x H 2,x )S 2 x + g1 x (S1 x S2 x ) + (g 1 x g2 x )S2 x + g1 y H 1,x(S 1 x X2 x ) + g1 y (H 1,x H 2,x )S 2 x [αl S,H + αγ + 2LL S,H + 2L 1 (1 + Lγ)γ + Lγ] H 1 H 2 1,. (Step 6) To prove that h x (0) = 0, observe that from (6.6) now follows Since σ(b) σ(c) =, h x (0) = 0. Ch x (0) h x (0)B = 0. Similar to our approach for the Grobman-Hartman Theorem in Chapter 3.3 we now set up a local version of Theorem 6.1. Theorem 6.3 (Existence of a Local Center-Unstable Manifold) Assume that the functions f and g in (6.1) are of class C k+1 for some k 1 and satisfy f(0, 0) = 0, g(0, 0) = 0, f u (0, 0) = 0, g u (0, 0) = 0. Then there exists a C k map h : R nc+nu R ns and an ε > 0 such that W cu ε = {(x, h(x)) : x R nc+nu, x ε} is conditionally invariant for the map (6.1). Moreover, h(0) = 0 and h x (0) = 0.

7 6.1. CENTER MANIFOLDS FOR MAPS 247 Remark: Recall from Chapter 3 that conditional invariance means that any point (x, h(x)) in Wε cu with image (ξ, η) such that ξ ε satisfies (ξ, η) Wε cu, i.e. η = h(ξ). Definition 6.4 The set Wε cu fixed point (0, 0) of (6.1). is called a local center unstable manifold of the Proof of Theorem 6.3: Instead of (6.3), consider a map ( ) 1 u Au + χ ε u R(u), (6.16) where ε > 0 and χ C (R n, R) is a standard cut-off function with χ(u) = 1 for 0 u 1 and χ(u) = 0 for u 2. The map (6.16) coincides with (6.3) for u R n satisfying u ε. With the scaling u εu the mapping (6.16) transforms into u Au + R ε (u), (6.17) where u = ( x y ), R ε (u) = χ(u) 1 ε R(εu). We apply Theorem 6.1 to this map and verify (6.4) for constants K ε, L ε that can be made arbitrarily small. Note that l ε := sup{ R u (v) : v 2ε} 0 as ε 0 and, by the mean value theorem, R ε (u) χ(u) 1 0 R u (εtu)udt 2 χ l ε =: K ε, R ε,u (u) = χ u (u) 1 ε R(εu) + χ(u)r u(εu) ( χ u + χ )l ε =: L ε. Therefore, the conditions (6.5) are satisfied for ε sufficiently small. Finally, with h ε being the fixed point from Theorem 6.1 corresponding to R ε set h(x) = εh ε ( 1 ε x ). (6.18) Then global invariance of {(x, h ε (x)); x R nc+nu } yields conditional invariance of Wε cu. For the derivatives up to order k of R ε one obtains bounds that tend to 0 as ε 0. For example, R ε,uu (u) l ε ( χ uu + 2 χ u ) + ε χ sup{ R uu (v) : v 2ε}. Finally h x (0) = 0 directly follows from h ε,x (0) = 0. Theorem 6.5 (Existence of a Center Manifold) Under the assumptions of Theorem 6.3, the map (6.1) has a locally defined invariant manifold W c = {(ξ, h c (ξ)) : ξ R nc, ξ ǫ}, where ǫ > 0 is sufficiently small and h c : R nc R ns+nu is a C k -map satisfying h c (0) = 0, h c ξ (0) = 0.

8 248 CHAPTER 6. CENTER MANIFOLD REDUCTION Proof: Applying Theorem 6.3 to a map that is inverse to the restriction of the map (6.1) to its invariant center-unstable manifold, we get a manifold W c with all mentioned above properties. Definition 6.6 W c is called a center manifold of the fixed point (0, 0) of (6.1). Remarks: (1) While the global center-unstable manifold in Theorem 6.1 is unique, this is no longer true for the local center-unstable manifold. In general Wε cu depends on ε and on the cut-off function. However, one can show that the derivatives of the possible functions h agree at the origin up to the given order of differentiability. The same remarks apply to the center manifold. (2) It can happen that ε 0, as k. Thus, there are C maps having no C center manifolds. However, for analytic mappings analytic center manifolds do exist. (3) It is possible to close the smoothness gap in Theorem 6.3 and prove that h is in fact a C k+1 map. However, this needs a much more elaborate argument. Theorem 6.7 (Reduction Principle) Consider a map ( ) ( ) ξ Bξ + f(ξ, η),, (6.19) η Cη + g(ξ, η) where ξ R nc, η R ns+nu, the n c n c matrix B has n c eigenvalues with λ = 1, while all eigenvalues of the (n s + n u ) (n s + n u ) matrix C satisfy λ 1, and the functions f and g have neither constant nor linear terms. The map (6.19) is locally topologically conjugate near (0, 0) to the map ( ξ η ) ( Bξ + f(ξ, h c (ξ)), Cη where h c represents the center manifold W c given by Theorem 6.5. ), (6.20) The maps for ξ and η are decoupled in (6.20). Therefore, the map (6.20) is locally topologically conjugate (0, 0) to a map ( ) ( ) ξ b(ξ), η Cη where ξ b(ξ) is any map that is locally topologically conjugate near ξ = 0 to ξ Bξ+f(ξ, h c (ξ)). Indeed, the conjugating homeomorphism can be constructed as the direct product of a conjugating homeomorphism in the ξ-space and the identity map in the η-space. Proposition 6.8 If ξ = 0 is a stable fixed point of the restriction of (6.19) to its center manifold ξ Bξ + f(ξ, h c (ξ)), ξ R nc, and n u = 0 (i.e. all eigenvalues of C satisfy λ < 1), then (ξ, η) = (0, 0) is a stable fixed point of (6.19).

9 6.2. CENTER MANIFOLDS FOR ODES Center manifolds for ODEs Consider a C k -smooth system { ẋ = Bx + f(x, y), ẏ = Cy + g(x, y), (6.21) where x R nc+nu, y R ns, and f(x, y) and g(x, y) have neither constant nor linear terms. Suppose that the matrix B has n c critical eigenvalues (i.e. eigenvalues with Re λ = 0) and n u eigenvalues with Re λ > 0, while all n s eigenvalues of the matrix C satisfy Re λ < 0. Theorem 6.9 (Existence of a global Center-Unstable Manifold) Assume that f(0, 0) = 0, g(0, 0) = 0 and that f and g have sufficiently small bounds and Lipschitz bounds on R n. Then there exists an invariant manifold W cu = {(x, h(x)) : x R nc+nu }, where h : R nc+nu R ns is bounded, globally Lipschitz and satisfies h(0) = 0. Moreover, h is in C k and satisfies h x (0) = 0 if f and g are C k functions for some k 1 with small derivatives up to order k and with a small Lipschitz bound for the k-th derivatives. Proof of Theorem 6.9: (Step 1) Write (6.21) in the form u = Au + r(u), (6.22) where u = ( x y ) R nc+nu R ns, A = ( B 0 0 C ) ( f(u), r(u) = g(u) ) and assume r κ, r(u) r(v) l u v (6.23) for all u, v R nu+nc. Since r has a global Lipschitz bound, the system generates a global solution flow Φ t (u). Then define R t (u) as the difference to the linearized flow ( ) e Φ t (u) = e ta + R t tb 0 (u) = 0 e tc + R t (u). (6.24) We show that Theorem 6.1 applies to this map Φ t (u) for all 0 < t 2. (Step 2) By Lemma?? there exist Lyapunov norms 1 on R ns and 2 on R nc+nu and numbers 0 < b < a such that α(t) = e tc 2 e at, β(t) = e tb 1 e bt, t 0. (6.25)

10 250 CHAPTER 6. CENTER MANIFOLD REDUCTION Clearly, α(t) < β(t) 1 for all t > 0. In what follows we use these adapted norms and their extension ( ) x u = max( x 1, y 2 ), u =. y (Step 3) From the variation of constants formula, t R t (u) = e (t s)a r(φ s (u))ds κ o t 0 e (t s) A ds = κ et A 1 A = K(t). Therefore, for κ sufficiently small α(t) + K(t) e at + κ et A 1 A < 1, 0 < t 2. Further note that the variation of constants formula implies Φ t (u) Φ t (v) e ta (u v) + e t A u v + l which by Gronwall s Lemma leads to t 0 t 0 e (t s)a (r(φ s (u)) r(φ s (v)))ds e (t s) A Φ s (u) Φ s (v) ds, Φ t (u) Φ t (v) e t( A +l) u v. (6.26) In this way we obtain the Lipschitz estimate for R t R t (u) R t (v) = t 0 t 0 e (t s)a (r(φ s (u)) r(φ s (v)))ds e A (t s) l Φ s (u) Φ s (v) ds e t A (e tl 1) u v = L(t) u v. Then we can satisfy the second condition in (6.5) β(t)(α(t) + 2L(t)) = e (b a)t + 2e t( A +b) (e tl 1) < 1 for all 0 < t 2 and for sufficiently small l. Finally, note that the third condition in (6.5) follows from the second since β(t) > 1. (Step 4) By Theorem 6.1 the Hadamard graph transform T t corresponding to Φ t has a unique fixed point H t in M 0 for 0 < t 2. Now we prove H t = H s for all 0 < t, s 1. From the flow property of Φ t one finds T t T s = T t+s = T s T t for 0 < t, s 1. Indeed T s (H s ) = H s and, therefore, T t (H s ) = T t (T s (H s )) = T s (T t (H s )).

11 6.2. CENTER MANIFOLDS FOR ODES 251 This means that T t (H s )) is a fixed point of T s so that by uniqueness T t (H s ) = H s. Then the uniqueness of the fixed point of T t gives H s = H t. Therefore, all the functions H t, 0 < t 1 coincide. This proves that the global center-unstable manifold W = W cu defined by h = H t is invariant under the flow Φ t, 0 < t 1. Since Φ n = } Φ 1 Φ 1 {{ Φ} 1, n times the graph of h is invariant under Φ n. For arbitrary t, we write Φ t = Φ [t] Φ t [t] and note that the graph of h is invariant under both Φ t [t] and Φ [t]. We conclude that we have invariance under Φ t without any restriction on t 0. (Step 5) As in Step 5 of the proof of Theorem 6.1 we indicate how to obtain estimates for the derivative of R t (u) with respect to u. The bound R t u(u) L(t) follows from the Lipschitz estimate of R t above. It remains to establish the Lipschitz bound for R t u. Differentiating Rt (u) gives R t u(u) = t 0 e (t s)a r u (Φ s (u))φ s u(u)ds. (6.27) Let κ 1 be a bound for r u and l 1 be a Lipschitz constant for r u. Similar to (6.26) one first establishes an estimate for Φ t u Then the representation (6.27) gives R t u (u) Rt u (v) t Φ t (u) Φ t (v) (l + l 1 )e t A etl 1 u v. l 0 ( e (t s) A l 1 e s( A +l) + κ 1 (l 1 + l)e s A esl 1 l ) ( e t A e tl 1 l 1 + l l 1 + κ 1 (e tl tl 1) l l 2 u v. This estimate shows that we can achieve a small Lipschitz constant. ) ds u v Theorem 6.10 (Existence of a local Center-Unstable Manifold) Suppose that f(0, 0) = 0, g(0, 0) = 0, f u (0, 0) = 0, g u (0, 0) = 0 and that f, g are of class C k+1 with k 1 Then the system (6.21) has a conditionally invariant local center-unstable manifold W cu ε = {(x, h(x)) : x R nc+nu, x ε}, where ε > 0 is sufficiently small and h is a C k function.

12 252 CHAPTER 6. CENTER MANIFOLD REDUCTION Proof of Theorem 6.10: Take the cut-off function χ from the proof of Theorem 6.3 and replace (6.22) by the scaled system u = Au + r ε (u), where r ε (u) = χ(u) 1 ε r(εu). The proof of Theorem 6.3 shows that r ε, r ε,u have small global bounds and Lipschitz bounds as well. Again the global center-unstable manifold of the cut-off system leads to a conditionally invariant local center-unstable manifold of the original system (6.6). As in the discrete-time case, we can prove the following result. Theorem 6.11 (Existence of a Center Manifold) The system (6.21) has a locally defined invariant manifold W c = {(ξ, h c (ξ)) : ξ R nc, ξ ǫ}, where ǫ > 0 is sufficiently small and h c : R nc R ns+nu is a C k -map satisfying h c (0) = 0, h c ξ(0) = 0. Finally, we formulate without proof the Reduction Principle for ODEs. Theorem 6.12 (Reduction Principle) Consider a system { ξ = Bξ + f(ξ, η), η = Cη + g(ξ, η), (6.28) where ξ R nc, η R ns+nu, the n c n c matrix B has n c critical eigenvalues with Re λ = 0, while all eigenvalues of the (n s +n u ) (n s +n u ) matrix C satisfy Re λ 0, and the functions f and g are smooth and have neither constant nor linear terms. The system (6.28) is locally topologically conjugate near (0, 0) to the system { ξ = Bξ + f(ξ, h c (ξ)), (6.29) η = Cη. The systems for ξ and η are decoupled in (6.29). Therefore, the system (6.29) is locally topologically conjugate (0, 0) to a system { ξ = b(ξ), η = Cη.

13 6.3. CRITICAL NORMAL FORMS ON CENTER MANIFOLDS 253 where ξ = b(ξ) is any system that is locally topologically conjugate near ξ = 0 to ξ = Bξ + f(ξ, h c (ξ)). Moreover, the second equation in the last system can be substituted by the standard saddle, i.e. the linear system { ηs = η s, η s R ns, (6.30) η u = +η u, η u R nu. As for the discrete-time case, we have the following result. Proposition 6.13 If ξ = 0 is a stable equilibrium of the restriction of (6.28) to its center manifold ξ = Bξ + f(ξ, h c (ξ)), ξ R nc, and all eigenvalues of C satisfy Re λ < 0, then (ξ, η) = (0, 0) is a stable equilibrium of (6.21). 6.3 Critical normal forms on center manifolds We now address the problem of computing in practice the coefficients of normal forms of restrictions of multidimensional ODEs and maps to the corresponding critical center manifolds at codim 1 bifurcations of equilibria and fixed points. The resulting formulas are simple and allow one to perform all computations in the original coordinates, without any preliminary transformation. Consider a smooth system of ODEs u = Au + F(u), u R n, (6.31) where the matrix A has n c eigenvalues with zero real parts, and write the Taylor expansion for F at u 0 = 0 as F(u) = 1 2 B(u, u) C(u, u, u) + O( u 4 ), where B : R n R n R n and C : R n R n R n R n are multilinear functions with the components: B i (p, q) = n j,k=1 2 F i (0) u j u k p j q k, C i (p, q, r) = n j,k,l=1 3 F i (0) u j u k u l p j q k r l, for i = 1, 2,..., n. In what follows, we use some results from Linear Algebra. Given an n n complex matrix L, introduce two linear subspaces of C n : the range R(L) = {v C n : v = Lu for some u C n } and the null-space N(L) = {w C n : Lw = 0}.

14 254 CHAPTER 6. CENTER MANIFOLD REDUCTION We call two vectors u, v C n orthogonal and write u v, if their scalar product vanishes: u, v ū T v = 0. Denote by L the transposed matrix to the complexconjugate of L, i.e. L = L T. If L is real, L = L T. The matrix L is called the adjoint matrix for L. We have for all u, v C n. u, Lv = L u, v Lemma 6.14 (Fredholm s Decomposition) C n = R(L) N(L ) with R(L) N(L ), i.e., any vector x C n can be uniquely decomposed as x = v + w with v R(L), w N(L ), and w, v = 0. Proof: Consider an orthogonal complement W of R(L) in C n, so that C n = R(L) W and R(L) W. We are going to prove that W = N(L ). (1) Suppose that w N(L ) meaning L w = 0. For any v = Lu R(L), holds w, v = w, Lu = L w, u = 0, u = 0. Hence, w W. (2) Suppose now that w W or w, v = 0 for v = Lu R(L) with any u C n. Take u = L w. Then we have Thus, L w = 0 and w N(L ). 0 = w, v = w, Lu = w, LL w = L w, L w = L w 2. Lemma 6.14 implies that a linear system Lu = v has a solution if and only if w, v = 0 for all w satisfying L w = 0. This is known as the Fredholm solvability condition. If L is nonsingular, then L is also nonsingular, so that N(L ) = 0 and Lu = v has a unique solution for any v R n, u = L 1 v. If L is singular, implying that both N(L) and N(L ) are nontrivial, but v satisfies the Fredholm solvability condition, then a solution u to Lu = v exists but is not unique. Indeed, u + ξ is another solution for any ξ N(L). Theorem 6.15 (Critical fold coefficient) Suppose λ 1 = 0 is a simple eigenvalue of A and assume that it has no other critical eigenvalues. Introduce vectors q, p R n, such that Aq = 0, A T p = 0, p, q = 1. Then the restriction of (6.31) to a one-dimensional center manifold W c (0) can be written in the form ξ = bξ 2 + O( ξ 3 ), ξ R, (6.32) where b = 1 p, B(q, q). (6.33) 2

15 6.3. CRITICAL NORMAL FORMS ON CENTER MANIFOLDS 255 Proof: Write (6.31) as u = Au B(u, u) + O( u 3 ), u R n. (6.34) The center manifold is one dimensional and can be represented as u = ξq ξ2 h 2 + O(ξ 3 ), ξ R, (6.35) for some h 2 R n. Using (6.34),(6.35), and (6.32), we get u = ξq + ξ ξh = bξ 2 q + ξ 3 h and u = Au B(u, u) +... = 1 2 ξ2 Ah ξ2 B(q, q) +... Comparing the ξ 2 -terms we find bq = 1 2 Ah B(q, q) 2 or Ah 2 = 2bq B(q, q). This linear system for h 2 is obviously singular but has a solution. The Fredholm solvability condition implies then that from which we obtain (6.33).. p, 2bq B(q, q) = 0, Theorem 6.16 (Critical Andronov-Hopf coefficient) Suppose λ 1,2 = ±iω 0, ω 0 > 0, is a simple pair of purely imaginary eigenvalues of A and assume that it has no other critical eigenvalues. Introduce vectors q, p C n, such that Aq = iω 0 q, A T p = iω 0 p, p, q = 1. Then the restriction of (6.31) to a two-dimensional center manifold W c (0) can be written in the form where η = iω 0 η + c 1 η η 2 + O( η 4 ), η C, (6.36) c 1 = 1 2 p, C(q, q, q) 2B(q, A 1 B(q, q)) + B( q, (2iω 0 I A) 1 B(q, q)). (6.37)

16 256 CHAPTER 6. CENTER MANIFOLD REDUCTION Proof: Write (6.31) as u = Au B(u, u) C(u, u, u) + O( u 4 ), u R n. (6.38) There is a two-dimensional center manifold that we can parametrize with η C: u = ηq + η q η2 h 20 + η ηh η2 h η2 ηh , (6.39) where the dots denote all inessential terms. Here h ij C n. Using (6.38) and (6.36), we get by collecting the η 2 -terms in (6.38): (2iω 0 I A)h 20 = B(q, q). The matrix of this system is nonsingular, since 2iω 0 is not an eigenvalue of A. Thus, h 20 = (2iω 0 I A) 1 B(q, q). Collecting the η η-terms gives another nonsingular system or Ah 11 = B(q, q) h 11 = A 1 B(q, q). The η 2 -terms lead to h 02 = h 20, while collecting the coefficients in front of the η 2 η-term yields the linear system: (iω 0 I A)h 21 = C(q, q, q) + B( q, h 20 ) + 2B(q, h 11 ) 2c 1 q. This system is singular but has a solution. Thus, the Fredholm solvability condition must be satisfied: p, C(q, q, q) + B( q, h 20 ) + 2B(q, h 11 ) 2c 1 = 0, which gives (6.37). The first Lyapunov coefficient is, therefore, l 1 = 1 ω 0 Re c 1. Consider now a smooth map u Au + F(u), u R n, (6.40) where the matrix A has n c critical eigenvalues satisfying λ = 1, and F(u) = 1 2 B(u, u) C(u, u, u) + O( u 4 ), is as in (6.31).

17 6.3. CRITICAL NORMAL FORMS ON CENTER MANIFOLDS 257 Theorem 6.17 (Critical fold coefficient for maps) Suppose λ 1 = 1 is a simple eigenvalue of A and assume that it has no other critical eigenvalues. Introduce vectors q, p R n, such that Aq = q, A T p = p, p, q = 1. Then the restriction of (6.40) to a one-dimensional center manifold W c (0) can be written in the form ξ ξ + bξ 2 + O( ξ 3 ), ξ R, (6.41) where Proof: Write b = 1 p, B(q, q). (6.42) 2 f(h) = AH B(H, H) + O( H 3 ), and locally represent the center manifold W c as the graph of a function u = H(ξ), H : R R n, where H(ξ) = ξq h 2ξ 2 + O(ξ 3 ), ξ R, h 2 R n. The restriction of (6.40) to W c (0) is ξ G(ξ), where G(ξ) = ξ + bξ 2 + O(ξ 3 ). The invariance equation for the center manifold reads as f(h(ξ)) = H(G(ξ)) A(ξq+ 1 2 h 2ξ 2 + )+ 1 2 B(ξq+, ξq+ )+ = (ξ+bξ2 + )q+ 1 2 h 2(ξ+ ) 2 + The ξ 2 -terms give the equation for h 2 : (A I)h 2 = B(q, q) + 2bq. It is singular and its solvability implies (6.42). Theorem 6.18 (Critical flip coefficient) Suppose λ 1 = 1 is a simple eigenvalue of A and assume that it has no other critical eigenvalues. Introduce vectors q, p R n, such that Aq = q, A T p = p, p, q = 1. Then the restriction of (6.40) to a one-dimensional center manifold W c (0) can be written in the normal form ξ ξ + cξ 3 + O( ξ 4 ), ξ R, (6.43) where c = 1 6 p, C(q, q, q) 1 2 p, B(q, (A I) 1 B(q, q)). (6.44)

18 258 CHAPTER 6. CENTER MANIFOLD REDUCTION Proof: Expand f(h) = AH B(H, H) C(H, H, H) + O( H 4 ), and parametrize the center manifold W c (0) with u = H(ξ), where H(ξ) = ξq h 2ξ h 3ξ 3 + O(ξ 4 ), and ξ R, h 2,3 R n. The critical normal form is ξ = G(ξ) = ξ + cξ 3 + O(ξ 4 ). The ξ 2 -terms in the invariance equation f(h(ξ)) = H(G(ξ)) give for h 2 : (A I)h 2 = B(q, q). Since λ = 1 is not an eigenvalue of A, the matrix (A I) is nonsingular. Thus, h 2 = (A I) 1 B(q, q). The ξ 3 -terms in the invariancy equation give the linear system for h 3 : (A + I)h 3 = 6cq C(q, q, q) 3B(q, h 2 ). This system is singular, since (A + I)q = 0, so it has a solution only if p, 6cq C(q, q, q) 3B(q, h 2 ) = 0, which implies c = 1 6 p, C(q, q, q) p, B(q, h 2). Taking into account h 2 = (A I) 1 B(q, q), we obtain (6.44). Theorem 6.19 (Critical Neimark-Sacker coefficient) Suppose that λ 1,2 = e ±iθ 0, where e ikθ0 1, k = 1, 2, 3, 4, is a simple pair of purely imaginary eigenvalues of A and that A has no other critical eigenvalues. Introduce vectors q, p C n, such that Aq = e iθ 0 q, A T p = e iθ 0 p, p, q = 1. Then the restriction of (6.40) to a two-dimensional center manifold W c (0) can be written in the form where η e iθ 0 η + c 1 η η 2 + O( η 4 ), η C, (6.45) c 1 = 1 2 p, C(q, q, q) + B( q, (e2iθ 0 I A) 1 B(q, q)) + 2B(q, (I A) 1 B(q, q)). (6.46)

19 6.4. FAMILIES OF CENTER MANIFOLDS 259 Proof: The invariancy of W c (0) represented as the graph of u = H(η, η) with η C can be written in the form where and H(η, η) = ηq + η q + f(h(η, η)) = H(G(η, η)), (6.47) 1 j+k 3 1 j!k! h jkη j η k + O( η 4 ), f(h) = AH B(H, H) C(H, H, H) + O( H 4 ), Quadratic terms in (6.47) give G(η, η) = e iθ 0 η + c 1 η η 2 + O( η 4 ). h 20 h 11 = (e 2iθ 0 I A) 1 B(q, q), = (I A) 1 B(q, q). While the η 2 w-terms lead to the singular system (e iθ 0 I A)h 21 = C(q, q, q) + B( q, h 20 ) + 2B(q, h 11 ) 2c 1 q. The solvability of this system is equivalent to p, C(q, q, q) + B( q, h 20 ) + 2B(q, h 11 ) 2c 1 q = 0, so the cubic normal form coefficient can indeed be expressed by (6.46). Recall that the direction of the bifurcation of the closed invariant curve is determined by the sign of a = Re(e iθ 0 c 1 ). 6.4 Families of center manifolds Consider a smooth parameter-dependent system of ODEs { ξ = P(ξ, η, α), η = Q(ξ, η, α), (6.48) where ξ R nc, η R ns+nu, α R m, and suppose that (6.48) coincides with (6.28) at α = 0: P(ξ, η, 0) = Bξ + f(ξ, η), Q(ξ, η, 0) = Cη + g(ξ, η). Theorem 6.20 The system (6.48) has a family of invariant manifolds, locally representable for small α as W c α = {(ξ, w(ξ, α) : ξ R nc, x ε}, where ε > 0 is sufficiently small and the map w : R nc R m R ns+nu is smooth. Moreover, w(ξ, 0) = h c (ξ), i.e. W0 c c coincides with a center manifold W from Theorem 6.11.

20 260 CHAPTER 6. CENTER MANIFOLD REDUCTION Proof: Consider the following extended system: ξ = P(ξ, η, α), η = Q(ξ, η, α), α = 0, (6.49) where ξ R nc, η R ns+nu, and α R m. The equilibrium (ξ, η, α) = (0, 0, 0) of (6.49) is nonhyperbolic and has n c + m eigenvalues with Re λ = 0 (m of them are equal to zero). Theorem 6.11 guarantees local existence of a (n c + m)-dimensional invariant center manifold in (6.49). This manifold is the union of n c -dimensional manifolds Wα c located in the invariant linear subspeces α = const of (6.49). Theorem 6.21 (Shoshitaishvilly, 1975) The system (6.48) is locally topologically equivalent near (ξ, η, α) = (0, 0, 0) to the system { ξ = P(ξ, w(ξ, α), α), (6.50) η = Cη. This theorem means that all essential events near the bifurcation parameter value occur on the invariant manifold W c α and are captured by the n c-dimensional restricted system: ξ = P(ξ, w(ξ, α), α), ξ R nc, α R m. (6.51) Obviously, this system can be substiututed in Theorem 6.21 by any smooth system ξ = b(ξ, α), ξ R nc, α R m, that is locally topologically equivalent to (6.51), while the second equation in (6.50) can be replaced by the standard saddle (6.30). A theorem similar to Theorem 6.21 can be formulated for discrete-time dynamical systems generated by smooth maps. 6.5 Bifurcations of equilibria and cycles in n-dimensional ODEs. Let us apply Theorem 6.21 to the fold and Hopf bifurcations of equilibria in multidimensional systems Generic fold bifurcation in planar systems Consider a smooth planar system ẋ = f(x, α), x R 2, α R. (6.52) Assume that at α = 0 it has the equilibrium x 0 = 0 with one eigenvalue λ 1 = 0 and one eigenvalue λ 2 < 0. Theorem 6.20 gives the existence of a smooth, locally

21 6.5. BIFURCATIONS OF EQUILIBRIA AND CYCLES IN N-DIMENSIONAL ODES.261 defined, one-dimensional attracting invariant manifold Wα c At α = 0, equation (6.51) has the form for (6.52) for small α. ξ = bξ 2 + O(ξ 3 ). If b 0 and equation (6.51) depends generically on the parameter, then it is locally topologically equivalent to the normal form u = β + σu 2, where σ = sign b = ±1. Under these genericity conditions, Theorem 6.21 implies that (6.52) is locally topologically equivalent to the system { u = β + σu 2, (6.53) v = v. Equations (6.53) are decoupled. The resulting phase portraits are presented in Figure 6.2 for the case σ > 0. For β < 0, there are two hyperbolic equilibria in the β < 0 β = 0 β > 0 Figure 6.2: Fold bifurcation in the standard system (6.53) for σ = 1. β(α) < 0 β(α) = 0 β(α) > 0 Figure 6.3: Fold bifurcation in a generic planar system. u-axis: a stable node and a saddle. They collide at β = 0, forming a nonhyperbolic saddle-node point, and disappear. There are no equilibria for β > 0. The same events happen in (6.52) on some one-dimensional, parameter-dependent, invariant manifold, that is locally attracting (see Figure 6.3). All the equilibria belong to this manifold. Figures 6.2 and 6.3 explain why the fold bifurcation is often called the saddle-node bifurcation. It should be clear how to generalize these considerations to cover the case λ 2 > 0, as well as the n-dimensional case.

22 262 CHAPTER 6. CENTER MANIFOLD REDUCTION Generic Andronov-Hopf bifurcation in three-dimensional systems Consider a smooth system ẋ = f(x, α), x R 3, α R. (6.54) Assume that at α = 0 it has the equilibrium x 0 = 0 with eigenvalues λ 1,2 = ±iω 0, ω 0 > 0 and one negative eigenvalue λ 3 < 0. Theorem 6.20 gives the existence of a parameter-dependent, smooth, local two-dimensional attracting invariant manifold Wα c of (6.54) for small α. At α = 0 the restricted equation (6.51) can be written in complex form as ż = iω 0 z + g(z, z), z C, where g = O( z 2 ). If the Lyapunov coefficient l 1 (0) of this equation is nonzero and (6.51) depends generically on the parameter, then it is locally topologically equivalent to the normal form ż = (β + i)z + σz 2 z, where σ = sign l 1 (0) = ±1. Under these genericity conditions, Theorem 6.21 β < 0 β = 0 β > 0 Figure 6.4: Hopf bifurcation in the standard system (6.55) for σ = 1. β(α) < 0 β(α) = 0 β(α) > 0 Figure 6.5: Supercritical Hopf bifurcation in a generic three-dimensional system.

23 6.5. BIFURCATIONS OF EQUILIBRIA AND CYCLES IN N-DIMENSIONAL ODES.263 implies that (6.54) is locally topologically equivalent to the system { ż = (β + i)z + σz2 z, v = v. (6.55) The phase portrait of (6.55) is shown in Figure 6.2 for σ = 1. The supercritical Hopf bifurcation takes place in the invariant plane v = 0, which is attracting. The same events happen for (6.54) on some two-dimensional attracting manifold (see Figure 6.5). The construction can be generalized to arbitrary dimension n 3. A combination of the Poincaré map and the center manifold reduction allows us to describe bifurcations of limit cycles in generic n-dimensional ODEs depending on one parameter. Let L 0 be a limit cycle of a smooth system ẋ = f(x, α), x R n, α R, (6.56) at α = 0. Let P (α) denote the associated Poincaré map for nearby α; P (α) : Σ Σ, where Σ is a local cross-section to L 0. If some coordinates ξ = (ξ 1, ξ 2,...,ξ n 1 ) are introduced on Σ, then ξ = P (α) (ξ) can be defined to be the point of the next intersection with Σ of the orbit of (6.56) having initial point with coordinates ξ on Σ. The intersection of Σ and L 0 gives a fixed point ξ 0 for P (0) : P (0) (ξ 0 ) = ξ 0. As we know, map P (α) is smooth and locally invertible. Suppose that the cycle L 0 is nonhyperbolic, having n 0 multipliers on the unit circle. The center manifold theorems then give a parameter-dependent invariant manifold W c α Σ of P (α) on which the essential events take place. The Poincaré map P (α) is locally topologically equivalent to the suspension of its restriction to this manifold by the standard saddle map. Fix n = 3, for simplicity, and consider the implications of this result for the limit cycle bifurcations in R Fold bifurcation of limit cycles in R 3 Assume that at α = 0 the cycle has a simple multiplier µ 1 = 1 and its other multiplier satisfies 0 < µ 2 < 1. The restriction of P (α) to the invariant manifold W c α is a one-dimensional map, having a fixed point with µ 1 = 1 at α = 0. As has been shown, this generically implies the collision and disappearance of two fixed points of P (α) as α passes through zero. Under our assumption on µ 2, this happens on a one-dimensional attracting invariant manifold of P (α) ; thus, a stable and a saddle fixed point are involved in the bifurcation (see Figure 6.6 for an illustration). Each fixed point of the Poincaré map corresponds to a limit cycle of the continuous-time system. Therefore, two limit cycles (stable and saddle) collide and disappear in system (6.56) at this fold bifurcation of cycles Flip (period-doubling) bifurcation of limit cycles in R 3 Suppose that at α = 0 the cycle has a simple multiplier µ 1 = 1, while 1 < µ 2 < 0. Then, the restriction of P (α) to the invariant manifold will demonstrate generically

24 264 CHAPTER 6. CENTER MANIFOLD REDUCTION L 1 L 2 L0 α < 0 α = 0 α > 0 Figure 6.6: Fold bifurcation of limit cycles. L 0 L0 L 0 L 1 α < 0 α = 0 α > 0 Figure 6.7: Flip bifurcation of limit cycles. L 0 L 0 L 0 T 2 α > 0 α = 0 α > 0 Figure 6.8: Neimark-Sacker bifurcation of a limit cycle.

25 6.6. REFERENCES 265 the period-doubling (flip) bifurcation: A cycle of period-2 appears or disappears for the map, while the fixed point changes its stability (see Figure 6.7, where the supercritical case is illustrated). Since the manifold is attracting, the stable fixed point, for example, loses stability and becomes a saddle point, while a stable cycle of period-2 appears. The fixed points correspond to limit cycles of the relevant stability. The cycle of period-two points for the map corresponds to a unique stable limit cycle in (6.56) with approximately twice the period of the basic cycle L 0. The double-period cycle makes two big excursions near L 0 before the closure. The exact bifurcation scenario is determined by the normal form coefficient of the restricted Poincaré map evaluated at α = Neimark-Sacker (torus) bifurcation of limit cycles in R 3 The last codim 1 bifurcation corresponds to the case when the multipliers are complex and simple and lie on the unit circle: µ 1,2 = e ±iθ 0. The Poincaré map P (α) then has a parameter-dependent, two-dimensional, invariant manifold on which a closed invariant curve generically bifurcates from the fixed point (see Figure 6.8, where the supercritical bifurcation is shown). This closed curve corresponds to a two-dimensional invariant torus T 2 in (6.56). The bifurcation is determined by the normal form coefficient of the restricted Poincaré map at the critical parameter value. The orbit structure on the torus T 2 depends on the restriction of the Poincaré map to this closed invariant curve. Thus, generically, there are long-period cycles of different stability types located on the torus, which appear and disappear pair-wise via fold bifurcations. 6.6 References Bifurcations of stationary points and periodic orbits in one- and two-parameter families of multidimensional ODEs and maps are treated in many textbooks, including [Arnol d 1983, Guckenheimer & Holmes 1983, Arrowsmith & Place 1990, Shilnikov et al. 2001, Wiggins 2003, Kuznetsov 2004]. A useful summary is given in [Arnol d et al. 1994], while many technical issues are clarified in [Iooss 1979, Vanderbauwhede 1989, Iooss & Adelmeyer 1992]. For an alternative approach to the bifurcation theory based on the Lyapunov-Schmidt reduction, see [Chow & Hale 1982, Iooss & Joseph 1990, Kielhöfer 2004] A direct proof of the existence of a local center manifold near a nonhyperbolic equilibrium in ODEs, that does not depend on the corresponding result for maps, is given in [Carr 1981]; a proof of Theorem 6.12 (Reduction Principle for ODEs) can be found in [Kirchgraber & Palmer 1990]. Numerical methods for bifurcations of stationary points and periodic orbits in multidimensional ODEs and maps are summarized in [Beyn, Champneys, Doedel, Govaerts, Kuznetsov & Sandstede 2002].

26 266 CHAPTER 6. CENTER MANIFOLD REDUCTION 6.7 Exercises E (Andronov-Hopf bifurcation in 3D systems) Check that each of the following feedback control systems 1 has an equilibrium that exhibits an Andronov-Hopf bifurcation at µ = 0, and compute the first Lyapunov coefficient of the restricted system on the center manifold: (a) ẋ = µx y, ẏ = µy + x + xz, ż = z + x 2. (b) ẋ = µx y xz, ẏ = µy + x, ż = z + y 2 + x 2 z. E (Pitchfork bifurcation in Lorenz system) Compute the second-order approximation to the family of one-dimensional center manifolds of the Lorenz system 2 ẋ = σx + σy, ẏ = xz + rx y, (6.57) ż = xy bz, near the origin (x, y, z) = (0, 0, 0) for fixed (σ, b) and r close to r 0 = 1. Then, calculate the restricted system up to third-order terms in ξ and analyse its bifurcation. E (Andronov-Hopf bifurcation in Lorenz system) (a) Show that for fixed b > 0, σ > b + 1, and r 1 = σ(σ + b + 3) σ b 1, (6.58) a nontrivial equilibrium of (6.57) exhibits an Andronov-Hopf bifurcation. (b) Prove that this bifurcation is subcritical and, therefore, gives rise to a unique saddle limit cycle for r < r 1 (Hints: [Shilnikov et al. 2001, pp ] (i) Write (6.57) as a single third-order equation (1 + σ)ẋ2 x +(σ + b + 1)ẍ + b(1 + σ)ẋ + bσ(1 r)x = + ẋẍ x x x2 ẋ σx 3. (ii) Translate the origin to the equilibrium by introducing the new coordinate ξ = x x 0, where x 0 = b(r 1), thus obtaining the equation where ξ +(σ + b + 1) ξ + [b(1 + σ) + x 2 0 ] ξ + [bσ(1 r) + 3σx 2 0 ]ξ = f(ξ, ξ, ξ), (6.59) f(ξ, ξ, ξ) = 3σx 0 ξ 2 2x 0 ξ ξ σ x 0 ξ2 + 1 ξ ξ σξ 3 ξ σ ξ x 0 x 2 ξ ξ x 2 ξ ξ ξ Moon, F.C. and Rand, R.H. Parametric stiffness control of flexible structures, In: Proceedings of the Workshop on Identification and Control of Flexible Space Structures, Vol. II, Jet Propulsion Laboratory Publication 85-29, Pasadena, CA, 1985, pp Lorenz, E. Deterministic non-periodic flow, J. Atmos. Sci. 20 (1963),

27 6.7. EXERCISES 267 and the dots stand for all higher-order terms in (ξ, ξ, ξ). (iii) Rewrite (6.59) as a system U = AU + F(U), U = (ξ, ξ, ξ) T R 3. (6.60) Find the eigenvector and the adjoint eigenvector of A corresponding to its purely imaginary eigenvalues (when (6.58) is satisfied). (iv) Compute the first Lyapunov coefficient l 1 for (6.60) using (6.37). Substitute σ = σ +b+1 and show that l 1 is positive for all positive σ and b.) E (No Neimark-Sacker bifurcation bifurcation in Lorenz system) Prove that the Neimark-Sacker bifurcation of a limit cycle never occurs in (6.57), provided that (σ, r, b) are all positive. (Hint: Use the formula for the multiplier product and the fact that div f = (σ + b + 1) < 0, where f is the vector field given by the right-hand side of (6.57).) E (Fold and flip bifurcations in Hénon map) Consider the Hénon map 3 : ( x y ) ( ) y α βx y 2. (6.61) (a) Find equations for the fold and flip bifurcation curves of fixed points in (6.61). (b) Prove that found fold and flip bifurcations, occuring in (6.61) under variation of parameter α, are nondegenerate for fixed β ±1. E (Duopoly model of Kopel) Consider the following Kopel map from mathematical economics 4 : ( ) ( ) x (1 ρ)x + ρµy(1 y), (6.62) y (1 ρ)y + ρµx(1 x) where (µ, ρ) are positive parameters. (a) Find equations for period-doubling and Neimark-Sacker bifurcations of nonnegative fixed points in (6.62). (b) Study the nondegeneracy of these bifurcations by computing the corresponding normal forms. (c) Compute numerically bifurcation curves of fixed points, 2- and 4-cycles in the parameter domain 2.9 µ 3.8, 0.75 ρ 1.4. E (Flip and Neimark-Sacker bifurcations in an adaptive control map) (a) Demonstrate that the fixed point (x 0, y 0, z 0 ) = (1, 1, 1 b k) of the discrete-time dynamical system 5 x y z z y bx + k + yz ky (bx + k + zy 1) c + y2 3 Hénon, M. A two-dimensional mapping with a strange attractor, J Comm. Math. Phys. 50 (1976), Kopel, M. Simple and complex adjustment dynamics in Cournot duopoly models, Chaos, Solitons & Fractals, 12 (1996), Golden, M.P. and Ydstie, B.E. Bifurcation in model reference adaptive control systems, Systems Control Lett. 11 (1988),

28 268 CHAPTER 6. CENTER MANIFOLD REDUCTION exhibits a flip bifurcation at and a Neimark-Sacker bifurcation at [ ] 1 b F = k, 4(c + 1) b NS = c + 1 c + 2. (b) Determine the direction of the period-doubling bifurcation that occurs as b increases and passes through b F. (c) Show that the Neimark-Sacker bifurcation in the system under variation of the parameter b can be either sub- or supercritical depending on the values of the parameter (c, k).

BIFURCATION PHENOMENA Lecture 4: Bifurcations in n-dimensional ODEs

BIFURCATION PHENOMENA Lecture 4: Bifurcations in n-dimensional ODEs BIFURCATION PHENOMENA Lecture 4: Bifurcations in n-dimensional ODEs Yuri A. Kuznetsov August, 2010 Contents 1. Solutions and orbits: equilibria cycles connecting orbits compact invariant manifolds strange

More information

Numerical Continuation and Normal Form Analysis of Limit Cycle Bifurcations without Computing Poincaré Maps

Numerical Continuation and Normal Form Analysis of Limit Cycle Bifurcations without Computing Poincaré Maps Numerical Continuation and Normal Form Analysis of Limit Cycle Bifurcations without Computing Poincaré Maps Yuri A. Kuznetsov joint work with W. Govaerts, A. Dhooge(Gent), and E. Doedel (Montreal) LCBIF

More information

NBA Lecture 1. Simplest bifurcations in n-dimensional ODEs. Yu.A. Kuznetsov (Utrecht University, NL) March 14, 2011

NBA Lecture 1. Simplest bifurcations in n-dimensional ODEs. Yu.A. Kuznetsov (Utrecht University, NL) March 14, 2011 NBA Lecture 1 Simplest bifurcations in n-dimensional ODEs Yu.A. Kuznetsov (Utrecht University, NL) March 14, 2011 Contents 1. Solutions and orbits: equilibria cycles connecting orbits other invariant sets

More information

BIFURCATION PHENOMENA Lecture 1: Qualitative theory of planar ODEs

BIFURCATION PHENOMENA Lecture 1: Qualitative theory of planar ODEs BIFURCATION PHENOMENA Lecture 1: Qualitative theory of planar ODEs Yuri A. Kuznetsov August, 2010 Contents 1. Solutions and orbits. 2. Equilibria. 3. Periodic orbits and limit cycles. 4. Homoclinic orbits.

More information

Mathematical Foundations of Neuroscience - Lecture 7. Bifurcations II.

Mathematical Foundations of Neuroscience - Lecture 7. Bifurcations II. Mathematical Foundations of Neuroscience - Lecture 7. Bifurcations II. Filip Piękniewski Faculty of Mathematics and Computer Science, Nicolaus Copernicus University, Toruń, Poland Winter 2009/2010 Filip

More information

B5.6 Nonlinear Systems

B5.6 Nonlinear Systems B5.6 Nonlinear Systems 4. Bifurcations Alain Goriely 2018 Mathematical Institute, University of Oxford Table of contents 1. Local bifurcations for vector fields 1.1 The problem 1.2 The extended centre

More information

Elements of Applied Bifurcation Theory

Elements of Applied Bifurcation Theory Yuri A. Kuznetsov Elements of Applied Bifurcation Theory Third Edition With 251 Illustrations Springer Introduction to Dynamical Systems 1 1.1 Definition of a dynamical system 1 1.1.1 State space 1 1.1.2

More information

5.2.2 Planar Andronov-Hopf bifurcation

5.2.2 Planar Andronov-Hopf bifurcation 138 CHAPTER 5. LOCAL BIFURCATION THEORY 5.. Planar Andronov-Hopf bifurcation What happens if a planar system has an equilibrium x = x 0 at some parameter value α = α 0 with eigenvalues λ 1, = ±iω 0, ω

More information

Elements of Applied Bifurcation Theory

Elements of Applied Bifurcation Theory Yuri A. Kuznetsov Elements of Applied Bifurcation Theory Second Edition With 251 Illustrations Springer Preface to the Second Edition Preface to the First Edition vii ix 1 Introduction to Dynamical Systems

More information

APPPHYS217 Tuesday 25 May 2010

APPPHYS217 Tuesday 25 May 2010 APPPHYS7 Tuesday 5 May Our aim today is to take a brief tour of some topics in nonlinear dynamics. Some good references include: [Perko] Lawrence Perko Differential Equations and Dynamical Systems (Springer-Verlag

More information

8.1 Bifurcations of Equilibria

8.1 Bifurcations of Equilibria 1 81 Bifurcations of Equilibria Bifurcation theory studies qualitative changes in solutions as a parameter varies In general one could study the bifurcation theory of ODEs PDEs integro-differential equations

More information

Part II. Dynamical Systems. Year

Part II. Dynamical Systems. Year Part II Year 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2017 34 Paper 1, Section II 30A Consider the dynamical system where β > 1 is a constant. ẋ = x + x 3 + βxy 2, ẏ = y + βx 2

More information

Example of a Blue Sky Catastrophe

Example of a Blue Sky Catastrophe PUB:[SXG.TEMP]TRANS2913EL.PS 16-OCT-2001 11:08:53.21 SXG Page: 99 (1) Amer. Math. Soc. Transl. (2) Vol. 200, 2000 Example of a Blue Sky Catastrophe Nikolaĭ Gavrilov and Andrey Shilnikov To the memory of

More information

11 Chaos in Continuous Dynamical Systems.

11 Chaos in Continuous Dynamical Systems. 11 CHAOS IN CONTINUOUS DYNAMICAL SYSTEMS. 47 11 Chaos in Continuous Dynamical Systems. Let s consider a system of differential equations given by where x(t) : R R and f : R R. ẋ = f(x), The linearization

More information

Analysis of the Takens-Bogdanov bifurcation on m parameterized vector fields

Analysis of the Takens-Bogdanov bifurcation on m parameterized vector fields Analysis of the Takens-Bogdanov bifurcation on m parameterized vector fields Francisco Armando Carrillo Navarro, Fernando Verduzco G., Joaquín Delgado F. Programa de Doctorado en Ciencias (Matemáticas),

More information

Introduction to Bifurcation and Normal Form theories

Introduction to Bifurcation and Normal Form theories Introduction to Bifurcation and Normal Form theories Romain Veltz / Olivier Faugeras October 9th 2013 ENS - Master MVA / Paris 6 - Master Maths-Bio (2013-2014) Outline 1 Invariant sets Limit cycles Stable/Unstable

More information

Problem: A class of dynamical systems characterized by a fast divergence of the orbits. A paradigmatic example: the Arnold cat.

Problem: A class of dynamical systems characterized by a fast divergence of the orbits. A paradigmatic example: the Arnold cat. À È Ê ÇÄÁ Ë ËÌ ÅË Problem: A class of dynamical systems characterized by a fast divergence of the orbits A paradigmatic example: the Arnold cat. The closure of a homoclinic orbit. The shadowing lemma.

More information

BIFURCATIONS AND STRANGE ATTRACTORS IN A CLIMATE RELATED SYSTEM

BIFURCATIONS AND STRANGE ATTRACTORS IN A CLIMATE RELATED SYSTEM dx dt DIFFERENTIAL EQUATIONS AND CONTROL PROCESSES N 1, 25 Electronic Journal, reg. N P23275 at 7.3.97 http://www.neva.ru/journal e-mail: diff@osipenko.stu.neva.ru Ordinary differential equations BIFURCATIONS

More information

Main topics and some repetition exercises for the course MMG511/MVE161 ODE and mathematical modeling in year Main topics in the course:

Main topics and some repetition exercises for the course MMG511/MVE161 ODE and mathematical modeling in year Main topics in the course: Main topics and some repetition exercises for the course MMG5/MVE6 ODE and mathematical modeling in year 04. Main topics in the course:. Banach fixed point principle. Picard- Lindelöf theorem. Lipschitz

More information

CHALMERS, GÖTEBORGS UNIVERSITET. EXAM for DYNAMICAL SYSTEMS. COURSE CODES: TIF 155, FIM770GU, PhD

CHALMERS, GÖTEBORGS UNIVERSITET. EXAM for DYNAMICAL SYSTEMS. COURSE CODES: TIF 155, FIM770GU, PhD CHALMERS, GÖTEBORGS UNIVERSITET EXAM for DYNAMICAL SYSTEMS COURSE CODES: TIF 155, FIM770GU, PhD Time: Place: Teachers: Allowed material: Not allowed: August 22, 2018, at 08 30 12 30 Johanneberg Jan Meibohm,

More information

EE222 - Spring 16 - Lecture 2 Notes 1

EE222 - Spring 16 - Lecture 2 Notes 1 EE222 - Spring 16 - Lecture 2 Notes 1 Murat Arcak January 21 2016 1 Licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Essentially Nonlinear Phenomena Continued

More information

UNIVERSIDADE DE SÃO PAULO

UNIVERSIDADE DE SÃO PAULO UNIVERSIDADE DE SÃO PAULO Instituto de Ciências Matemáticas e de Computação ISSN 010-577 GLOBAL DYNAMICAL ASPECTS OF A GENERALIZED SPROTT E DIFFERENTIAL SYSTEM REGILENE OLIVEIRA CLAUDIA VALLS N o 41 NOTAS

More information

10 Back to planar nonlinear systems

10 Back to planar nonlinear systems 10 Back to planar nonlinear sstems 10.1 Near the equilibria Recall that I started talking about the Lotka Volterra model as a motivation to stud sstems of two first order autonomous equations of the form

More information

The fold-flip bifurcation

The fold-flip bifurcation The fold-flip bifurcation Yu.A. Kuznetsov, H.G.E. Meijer, and L. van Veen March 0, 003 Abstract The fold-flip bifurcation occurs if a map has a fixed point with multipliers + and simultaneously. In this

More information

Homoclinic Orbits of Planar Maps: Asymptotics and Mel nikov Functions

Homoclinic Orbits of Planar Maps: Asymptotics and Mel nikov Functions Department of Mathematics Mathematical Sciences Homoclinic Orbits of Planar Maps: Asymptotics and Mel nikov Functions A thesis submitted for the degree of Master of Science Author: Dirk van Kekem Project

More information

Summary of topics relevant for the final. p. 1

Summary of topics relevant for the final. p. 1 Summary of topics relevant for the final p. 1 Outline Scalar difference equations General theory of ODEs Linear ODEs Linear maps Analysis near fixed points (linearization) Bifurcations How to analyze a

More information

An introduction to Birkhoff normal form

An introduction to Birkhoff normal form An introduction to Birkhoff normal form Dario Bambusi Dipartimento di Matematica, Universitá di Milano via Saldini 50, 0133 Milano (Italy) 19.11.14 1 Introduction The aim of this note is to present an

More information

Lecture 5. Numerical continuation of connecting orbits of iterated maps and ODEs. Yu.A. Kuznetsov (Utrecht University, NL)

Lecture 5. Numerical continuation of connecting orbits of iterated maps and ODEs. Yu.A. Kuznetsov (Utrecht University, NL) Lecture 5 Numerical continuation of connecting orbits of iterated maps and ODEs Yu.A. Kuznetsov (Utrecht University, NL) May 26, 2009 1 Contents 1. Point-to-point connections. 2. Continuation of homoclinic

More information

Half of Final Exam Name: Practice Problems October 28, 2014

Half of Final Exam Name: Practice Problems October 28, 2014 Math 54. Treibergs Half of Final Exam Name: Practice Problems October 28, 24 Half of the final will be over material since the last midterm exam, such as the practice problems given here. The other half

More information

Lectures on Dynamical Systems. Anatoly Neishtadt

Lectures on Dynamical Systems. Anatoly Neishtadt Lectures on Dynamical Systems Anatoly Neishtadt Lectures for Mathematics Access Grid Instruction and Collaboration (MAGIC) consortium, Loughborough University, 2007 Part 3 LECTURE 14 NORMAL FORMS Resonances

More information

7 Planar systems of linear ODE

7 Planar systems of linear ODE 7 Planar systems of linear ODE Here I restrict my attention to a very special class of autonomous ODE: linear ODE with constant coefficients This is arguably the only class of ODE for which explicit solution

More information

Elements of Applied Bifurcation Theory

Elements of Applied Bifurcation Theory Yuri A. Kuznetsov Elements of Applied Bifurcation Theory Third Edition With 251 Illustrations Springer Yuri A. Kuznetsov Department of Mathematics Utrecht University Budapestlaan 6 3584 CD Utrecht The

More information

Connecting Orbits with Bifurcating End Points

Connecting Orbits with Bifurcating End Points Connecting Orbits with Bifurcating End Points Thorsten Hüls Fakultät für Mathematik Universität Bielefeld Postfach 100131, 33501 Bielefeld Germany huels@mathematik.uni-bielefeld.de June 24, 2004 Abstract

More information

One Dimensional Dynamical Systems

One Dimensional Dynamical Systems 16 CHAPTER 2 One Dimensional Dynamical Systems We begin by analyzing some dynamical systems with one-dimensional phase spaces, and in particular their bifurcations. All equations in this Chapter are scalar

More information

Tangent spaces, normals and extrema

Tangent spaces, normals and extrema Chapter 3 Tangent spaces, normals and extrema If S is a surface in 3-space, with a point a S where S looks smooth, i.e., without any fold or cusp or self-crossing, we can intuitively define the tangent

More information

Constructing a chaotic system with any number of equilibria

Constructing a chaotic system with any number of equilibria Nonlinear Dyn (2013) 71:429 436 DOI 10.1007/s11071-012-0669-7 ORIGINAL PAPER Constructing a chaotic system with any number of equilibria Xiong Wang Guanrong Chen Received: 9 June 2012 / Accepted: 29 October

More information

Alligood, K. T., Sauer, T. D. & Yorke, J. A. [1997], Chaos: An Introduction to Dynamical Systems, Springer-Verlag, New York.

Alligood, K. T., Sauer, T. D. & Yorke, J. A. [1997], Chaos: An Introduction to Dynamical Systems, Springer-Verlag, New York. Bibliography Alligood, K. T., Sauer, T. D. & Yorke, J. A. [1997], Chaos: An Introduction to Dynamical Systems, Springer-Verlag, New York. Amann, H. [1990], Ordinary Differential Equations: An Introduction

More information

The Higgins-Selkov oscillator

The Higgins-Selkov oscillator The Higgins-Selkov oscillator May 14, 2014 Here I analyse the long-time behaviour of the Higgins-Selkov oscillator. The system is ẋ = k 0 k 1 xy 2, (1 ẏ = k 1 xy 2 k 2 y. (2 The unknowns x and y, being

More information

computer session iv: Two-parameter bifurcation analysis of equilibria and limit cycles with matcont

computer session iv: Two-parameter bifurcation analysis of equilibria and limit cycles with matcont computer session iv: Two-parameter bifurcation analysis of equilibria and limit cycles with matcont Yu.A. Kuznetsov Department of Mathematics Utrecht University Budapestlaan 6 3508 TA, Utrecht May 16,

More information

B5.6 Nonlinear Systems

B5.6 Nonlinear Systems B5.6 Nonlinear Systems 5. Global Bifurcations, Homoclinic chaos, Melnikov s method Alain Goriely 2018 Mathematical Institute, University of Oxford Table of contents 1. Motivation 1.1 The problem 1.2 A

More information

7 Two-dimensional bifurcations

7 Two-dimensional bifurcations 7 Two-dimensional bifurcations As in one-dimensional systems: fixed points may be created, destroyed, or change stability as parameters are varied (change of topological equivalence ). In addition closed

More information

Lecture 6. Lorenz equations and Malkus' waterwheel Some properties of the Lorenz Eq.'s Lorenz Map Towards definitions of:

Lecture 6. Lorenz equations and Malkus' waterwheel Some properties of the Lorenz Eq.'s Lorenz Map Towards definitions of: Lecture 6 Chaos Lorenz equations and Malkus' waterwheel Some properties of the Lorenz Eq.'s Lorenz Map Towards definitions of: Chaos, Attractors and strange attractors Transient chaos Lorenz Equations

More information

MATH 205C: STATIONARY PHASE LEMMA

MATH 205C: STATIONARY PHASE LEMMA MATH 205C: STATIONARY PHASE LEMMA For ω, consider an integral of the form I(ω) = e iωf(x) u(x) dx, where u Cc (R n ) complex valued, with support in a compact set K, and f C (R n ) real valued. Thus, I(ω)

More information

Computational Methods in Dynamical Systems and Advanced Examples

Computational Methods in Dynamical Systems and Advanced Examples and Advanced Examples Obverse and reverse of the same coin [head and tails] Jorge Galán Vioque and Emilio Freire Macías Universidad de Sevilla July 2015 Outline Lecture 1. Simulation vs Continuation. How

More information

Dynamics of Modified Leslie-Gower Predator-Prey Model with Predator Harvesting

Dynamics of Modified Leslie-Gower Predator-Prey Model with Predator Harvesting International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:13 No:05 55 Dynamics of Modified Leslie-Gower Predator-Prey Model with Predator Harvesting K. Saleh Department of Mathematics, King Fahd

More information

LMI Methods in Optimal and Robust Control

LMI Methods in Optimal and Robust Control LMI Methods in Optimal and Robust Control Matthew M. Peet Arizona State University Lecture 15: Nonlinear Systems and Lyapunov Functions Overview Our next goal is to extend LMI s and optimization to nonlinear

More information

Nonlinear Autonomous Dynamical systems of two dimensions. Part A

Nonlinear Autonomous Dynamical systems of two dimensions. Part A Nonlinear Autonomous Dynamical systems of two dimensions Part A Nonlinear Autonomous Dynamical systems of two dimensions x f ( x, y), x(0) x vector field y g( xy, ), y(0) y F ( f, g) 0 0 f, g are continuous

More information

THE HARTMAN-GROBMAN THEOREM AND THE EQUIVALENCE OF LINEAR SYSTEMS

THE HARTMAN-GROBMAN THEOREM AND THE EQUIVALENCE OF LINEAR SYSTEMS THE HARTMAN-GROBMAN THEOREM AND THE EQUIVALENCE OF LINEAR SYSTEMS GUILLAUME LAJOIE Contents 1. Introduction 2 2. The Hartman-Grobman Theorem 2 2.1. Preliminaries 2 2.2. The discrete-time Case 4 2.3. The

More information

Problem List MATH 5173 Spring, 2014

Problem List MATH 5173 Spring, 2014 Problem List MATH 5173 Spring, 2014 The notation p/n means the problem with number n on page p of Perko. 1. 5/3 [Due Wednesday, January 15] 2. 6/5 and describe the relationship of the phase portraits [Due

More information

1 The Observability Canonical Form

1 The Observability Canonical Form NONLINEAR OBSERVERS AND SEPARATION PRINCIPLE 1 The Observability Canonical Form In this Chapter we discuss the design of observers for nonlinear systems modelled by equations of the form ẋ = f(x, u) (1)

More information

1 Lyapunov theory of stability

1 Lyapunov theory of stability M.Kawski, APM 581 Diff Equns Intro to Lyapunov theory. November 15, 29 1 1 Lyapunov theory of stability Introduction. Lyapunov s second (or direct) method provides tools for studying (asymptotic) stability

More information

UNIVERSIDADE DE SÃO PAULO

UNIVERSIDADE DE SÃO PAULO UNIVERSIDADE DE SÃO PAULO Instituto de Ciências Matemáticas e de Computação ISSN 0103-2577 GLOBAL DYNAMICAL ASPECTS OF A GENERALIZED CHEN-WANG DIFFERENTIAL SYSTEM REGILENE D. S. OLIVEIRA CLAUDIA VALLS

More information

The Invariant Curve in a Planar System of Difference Equations

The Invariant Curve in a Planar System of Difference Equations Advances in Dynamical Systems and Applications ISSN 0973-5321, Volume 13, Number 1, pp. 59 71 2018 http://campus.mst.edu/adsa The Invariant Curve in a Planar System of Difference Equations Senada Kalabušić,

More information

An Introduction to Numerical Continuation Methods. with Application to some Problems from Physics. Eusebius Doedel. Cuzco, Peru, May 2013

An Introduction to Numerical Continuation Methods. with Application to some Problems from Physics. Eusebius Doedel. Cuzco, Peru, May 2013 An Introduction to Numerical Continuation Methods with Application to some Problems from Physics Eusebius Doedel Cuzco, Peru, May 2013 Persistence of Solutions Newton s method for solving a nonlinear equation

More information

Simplest Chaotic Flows with Involutional Symmetries

Simplest Chaotic Flows with Involutional Symmetries International Journal of Bifurcation and Chaos, Vol. 24, No. 1 (2014) 1450009 (9 pages) c World Scientific Publishing Company DOI: 10.1142/S0218127414500096 Simplest Chaotic Flows with Involutional Symmetries

More information

MCE693/793: Analysis and Control of Nonlinear Systems

MCE693/793: Analysis and Control of Nonlinear Systems MCE693/793: Analysis and Control of Nonlinear Systems Systems of Differential Equations Phase Plane Analysis Hanz Richter Mechanical Engineering Department Cleveland State University Systems of Nonlinear

More information

1. (i) Determine how many periodic orbits and equilibria can be born at the bifurcations of the zero equilibrium of the following system:

1. (i) Determine how many periodic orbits and equilibria can be born at the bifurcations of the zero equilibrium of the following system: 1. (i) Determine how many periodic orbits and equilibria can be born at the bifurcations of the zero equilibrium of the following system: ẋ = y x 2, ẏ = z + xy, ż = y z + x 2 xy + y 2 + z 2 x 4. (ii) Determine

More information

Lecture 11 Hyperbolicity.

Lecture 11 Hyperbolicity. Lecture 11 Hyperbolicity. 1 C 0 linearization near a hyperbolic point 2 invariant manifolds Hyperbolic linear maps. Let E be a Banach space. A linear map A : E E is called hyperbolic if we can find closed

More information

Problem Set Number 5, j/2.036j MIT (Fall 2014)

Problem Set Number 5, j/2.036j MIT (Fall 2014) Problem Set Number 5, 18.385j/2.036j MIT (Fall 2014) Rodolfo R. Rosales (MIT, Math. Dept.,Cambridge, MA 02139) Due Fri., October 24, 2014. October 17, 2014 1 Large µ limit for Liénard system #03 Statement:

More information

27. Topological classification of complex linear foliations

27. Topological classification of complex linear foliations 27. Topological classification of complex linear foliations 545 H. Find the expression of the corresponding element [Γ ε ] H 1 (L ε, Z) through [Γ 1 ε], [Γ 2 ε], [δ ε ]. Problem 26.24. Prove that for any

More information

tutorial ii: One-parameter bifurcation analysis of equilibria with matcont

tutorial ii: One-parameter bifurcation analysis of equilibria with matcont tutorial ii: One-parameter bifurcation analysis of equilibria with matcont Yu.A. Kuznetsov Department of Mathematics Utrecht University Budapestlaan 6 3508 TA, Utrecht February 13, 2018 1 This session

More information

A short introduction with a view toward examples. Short presentation for the students of: Dynamical Systems and Complexity Summer School Volos, 2017

A short introduction with a view toward examples. Short presentation for the students of: Dynamical Systems and Complexity Summer School Volos, 2017 A short introduction with a view toward examples Center of Research and Applications of Nonlinear (CRANS) Department of Mathematics University of Patras Greece sanastassiou@gmail.com Short presentation

More information

NOTES ON LINEAR ODES

NOTES ON LINEAR ODES NOTES ON LINEAR ODES JONATHAN LUK We can now use all the discussions we had on linear algebra to study linear ODEs Most of this material appears in the textbook in 21, 22, 23, 26 As always, this is a preliminary

More information

DYNAMICAL SYSTEMS. I Clark: Robinson. Stability, Symbolic Dynamics, and Chaos. CRC Press Boca Raton Ann Arbor London Tokyo

DYNAMICAL SYSTEMS. I Clark: Robinson. Stability, Symbolic Dynamics, and Chaos. CRC Press Boca Raton Ann Arbor London Tokyo DYNAMICAL SYSTEMS Stability, Symbolic Dynamics, and Chaos I Clark: Robinson CRC Press Boca Raton Ann Arbor London Tokyo Contents Chapter I. Introduction 1 1.1 Population Growth Models, One Population 2

More information

CHALMERS, GÖTEBORGS UNIVERSITET. EXAM for DYNAMICAL SYSTEMS. COURSE CODES: TIF 155, FIM770GU, PhD

CHALMERS, GÖTEBORGS UNIVERSITET. EXAM for DYNAMICAL SYSTEMS. COURSE CODES: TIF 155, FIM770GU, PhD CHALMERS, GÖTEBORGS UNIVERSITET EXAM for DYNAMICAL SYSTEMS COURSE CODES: TIF 155, FIM770GU, PhD Time: Place: Teachers: Allowed material: Not allowed: January 14, 2019, at 08 30 12 30 Johanneberg Kristian

More information

On dynamical properties of multidimensional diffeomorphisms from Newhouse regions: I

On dynamical properties of multidimensional diffeomorphisms from Newhouse regions: I IOP PUBLISHING Nonlinearity 2 (28) 923 972 NONLINEARITY doi:.88/95-775/2/5/3 On dynamical properties of multidimensional diffeomorphisms from Newhouse regions: I S V Gonchenko, L P Shilnikov and D V Turaev

More information

Homoclinic saddle to saddle-focus transitions in 4D systems

Homoclinic saddle to saddle-focus transitions in 4D systems Faculty of Electrical Engineering, Mathematics & Computer Science Homoclinic saddle to saddle-focus transitions in 4D systems Manu Kalia M.Sc. Thesis July 2017 Assessment committee: Prof. Dr. S. A. van

More information

ONE-PARAMETER BIFURCATIONS IN PLANAR FILIPPOV SYSTEMS

ONE-PARAMETER BIFURCATIONS IN PLANAR FILIPPOV SYSTEMS International Journal of Bifurcation and Chaos, Vol. 3, No. 8 (23) 257 288 c World Scientific Publishing Company ONE-PARAMETER BIFURCATIONS IN PLANAR FILIPPOV SYSTEMS YU. A. KUZNETSOV, S. RINALDI and A.

More information

Stable cycles in a Cournot duopoly model of Kopel

Stable cycles in a Cournot duopoly model of Kopel Journal of Computational and Applied Mathematics 18 (008 47 58 www.elsevier.com/locate/cam Stable cycles in a Cournot duopoly model of Kopel W. Govaerts a,, R. Khoshsiar Ghaziani a,b a Department of Applied

More information

Lecture Notes for Math 524

Lecture Notes for Math 524 Lecture Notes for Math 524 Dr Michael Y Li October 19, 2009 These notes are based on the lecture notes of Professor James S Muldowney, the books of Hale, Copple, Coddington and Levinson, and Perko They

More information

The Conley Index and Rigorous Numerics of Attracting Periodic Orbits

The Conley Index and Rigorous Numerics of Attracting Periodic Orbits The Conley Index and Rigorous Numerics of Attracting Periodic Orbits Marian Mrozek Pawe l Pilarczyk Conference on Variational and Topological Methods in the Study of Nonlinear Phenomena (Pisa, 2000) 1

More information

Stability of Feedback Solutions for Infinite Horizon Noncooperative Differential Games

Stability of Feedback Solutions for Infinite Horizon Noncooperative Differential Games Stability of Feedback Solutions for Infinite Horizon Noncooperative Differential Games Alberto Bressan ) and Khai T. Nguyen ) *) Department of Mathematics, Penn State University **) Department of Mathematics,

More information

LECTURE 7, WEDNESDAY

LECTURE 7, WEDNESDAY LECTURE 7, WEDNESDAY 25.02.04 FRANZ LEMMERMEYER 1. Singular Weierstrass Curves Consider cubic curves in Weierstraß form (1) E : y 2 + a 1 xy + a 3 y = x 3 + a 2 x 2 + a 4 x + a 6, the coefficients a i

More information

UNIQUENESS OF POSITIVE SOLUTION TO SOME COUPLED COOPERATIVE VARIATIONAL ELLIPTIC SYSTEMS

UNIQUENESS OF POSITIVE SOLUTION TO SOME COUPLED COOPERATIVE VARIATIONAL ELLIPTIC SYSTEMS TRANSACTIONS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 00, Number 0, Pages 000 000 S 0002-9947(XX)0000-0 UNIQUENESS OF POSITIVE SOLUTION TO SOME COUPLED COOPERATIVE VARIATIONAL ELLIPTIC SYSTEMS YULIAN

More information

ẋ = f(x, y), ẏ = g(x, y), (x, y) D, can only have periodic solutions if (f,g) changes sign in D or if (f,g)=0in D.

ẋ = f(x, y), ẏ = g(x, y), (x, y) D, can only have periodic solutions if (f,g) changes sign in D or if (f,g)=0in D. 4 Periodic Solutions We have shown that in the case of an autonomous equation the periodic solutions correspond with closed orbits in phase-space. Autonomous two-dimensional systems with phase-space R

More information

Application demonstration. BifTools. Maple Package for Bifurcation Analysis in Dynamical Systems

Application demonstration. BifTools. Maple Package for Bifurcation Analysis in Dynamical Systems Application demonstration BifTools Maple Package for Bifurcation Analysis in Dynamical Systems Introduction Milen Borisov, Neli Dimitrova Department of Biomathematics Institute of Mathematics and Informatics

More information

Lotka Volterra Predator-Prey Model with a Predating Scavenger

Lotka Volterra Predator-Prey Model with a Predating Scavenger Lotka Volterra Predator-Prey Model with a Predating Scavenger Monica Pescitelli Georgia College December 13, 2013 Abstract The classic Lotka Volterra equations are used to model the population dynamics

More information

Szalai, R., & Osinga, H. M. (2007). Unstable manifolds of a limit cycle near grazing.

Szalai, R., & Osinga, H. M. (2007). Unstable manifolds of a limit cycle near grazing. Szalai, R., & Osinga, H. M. (2007). Unstable manifolds of a limit cycle near grazing. Early version, also known as pre-print Link to publication record in Explore Bristol Research PDF-document University

More information

Andronov Hopf and Bautin bifurcation in a tritrophic food chain model with Holling functional response types IV and II

Andronov Hopf and Bautin bifurcation in a tritrophic food chain model with Holling functional response types IV and II Electronic Journal of Qualitative Theory of Differential Equations 018 No 78 1 7; https://doiorg/10143/ejqtde018178 wwwmathu-szegedhu/ejqtde/ Andronov Hopf and Bautin bifurcation in a tritrophic food chain

More information

2 Qualitative theory of non-smooth dynamical systems

2 Qualitative theory of non-smooth dynamical systems 2 Qualitative theory of non-smooth dynamical systems In this chapter, we give an overview of the basic theory of both smooth and non-smooth dynamical systems, to be expanded upon in later chapters. In

More information

x R d, λ R, f smooth enough. Goal: compute ( follow ) equilibrium solutions as λ varies, i.e. compute solutions (x, λ) to 0 = f(x, λ).

x R d, λ R, f smooth enough. Goal: compute ( follow ) equilibrium solutions as λ varies, i.e. compute solutions (x, λ) to 0 = f(x, λ). Continuation of equilibria Problem Parameter-dependent ODE ẋ = f(x, λ), x R d, λ R, f smooth enough. Goal: compute ( follow ) equilibrium solutions as λ varies, i.e. compute solutions (x, λ) to 0 = f(x,

More information

Stability and Hopf Bifurcation for a Discrete Disease Spreading Model in Complex Networks

Stability and Hopf Bifurcation for a Discrete Disease Spreading Model in Complex Networks International Journal of Difference Equations ISSN 0973-5321, Volume 4, Number 1, pp. 155 163 (2009) http://campus.mst.edu/ijde Stability and Hopf Bifurcation for a Discrete Disease Spreading Model in

More information

Vector Field Topology. Ronald Peikert SciVis Vector Field Topology 8-1

Vector Field Topology. Ronald Peikert SciVis Vector Field Topology 8-1 Vector Field Topology Ronald Peikert SciVis 2007 - Vector Field Topology 8-1 Vector fields as ODEs What are conditions for existence and uniqueness of streamlines? For the initial value problem i x ( t)

More information

Dynamical Systems Generated by ODEs and Maps: Final Examination Project

Dynamical Systems Generated by ODEs and Maps: Final Examination Project Dynamical Systems Generated by ODEs and Maps: Final Examination Project Jasmine Nirody 26 January, 2010 Contents 1 Introduction and Background 1 2 Equilibria 1 3 Attracting Domain 2 4 Overview of Behavior

More information

CENTER MANIFOLD AND NORMAL FORM THEORIES

CENTER MANIFOLD AND NORMAL FORM THEORIES 3 rd Sperlonga Summer School on Mechanics and Engineering Sciences 3-7 September 013 SPERLONGA CENTER MANIFOLD AND NORMAL FORM THEORIES ANGELO LUONGO 1 THE CENTER MANIFOLD METHOD Existence of an invariant

More information

Chapter 3. Local behavior of nonlinear systems. 3.1 Principle of linearized stability Linearized stability of fixed points of maps

Chapter 3. Local behavior of nonlinear systems. 3.1 Principle of linearized stability Linearized stability of fixed points of maps Chapter 3 Local behavior of nonlinear systems In Chapter 2 we explained how, for linear dynamical systems, the qualitative as well as the quantitative behaviour can be described in terms of eigenvalues

More information

1.7. Stability and attractors. Consider the autonomous differential equation. (7.1) ẋ = f(x),

1.7. Stability and attractors. Consider the autonomous differential equation. (7.1) ẋ = f(x), 1.7. Stability and attractors. Consider the autonomous differential equation (7.1) ẋ = f(x), where f C r (lr d, lr d ), r 1. For notation, for any x lr d, c lr, we let B(x, c) = { ξ lr d : ξ x < c }. Suppose

More information

Phase Synchronization

Phase Synchronization Phase Synchronization Lecture by: Zhibin Guo Notes by: Xiang Fan May 10, 2016 1 Introduction For any mode or fluctuation, we always have where S(x, t) is phase. If a mode amplitude satisfies ϕ k = ϕ k

More information

Bifurcation of Fixed Points

Bifurcation of Fixed Points Bifurcation of Fixed Points CDS140B Lecturer: Wang Sang Koon Winter, 2005 1 Introduction ẏ = g(y, λ). where y R n, λ R p. Suppose it has a fixed point at (y 0, λ 0 ), i.e., g(y 0, λ 0 ) = 0. Two Questions:

More information

i=1 α i. Given an m-times continuously

i=1 α i. Given an m-times continuously 1 Fundamentals 1.1 Classification and characteristics Let Ω R d, d N, d 2, be an open set and α = (α 1,, α d ) T N d 0, N 0 := N {0}, a multiindex with α := d i=1 α i. Given an m-times continuously differentiable

More information

TWO DIMENSIONAL FLOWS. Lecture 5: Limit Cycles and Bifurcations

TWO DIMENSIONAL FLOWS. Lecture 5: Limit Cycles and Bifurcations TWO DIMENSIONAL FLOWS Lecture 5: Limit Cycles and Bifurcations 5. Limit cycles A limit cycle is an isolated closed trajectory [ isolated means that neighbouring trajectories are not closed] Fig. 5.1.1

More information

Friedrich symmetric systems

Friedrich symmetric systems viii CHAPTER 8 Friedrich symmetric systems In this chapter, we describe a theory due to Friedrich [13] for positive symmetric systems, which gives the existence and uniqueness of weak solutions of boundary

More information

CHAPTER 6 HOPF-BIFURCATION IN A TWO DIMENSIONAL NONLINEAR DIFFERENTIAL EQUATION

CHAPTER 6 HOPF-BIFURCATION IN A TWO DIMENSIONAL NONLINEAR DIFFERENTIAL EQUATION CHAPTER 6 HOPF-BIFURCATION IN A TWO DIMENSIONAL NONLINEAR DIFFERENTIAL EQUATION [Discussion on this chapter is based on our paper entitled Hopf-Bifurcation Ina Two Dimensional Nonlinear Differential Equation,

More information

MATH 614 Dynamical Systems and Chaos Lecture 24: Bifurcation theory in higher dimensions. The Hopf bifurcation.

MATH 614 Dynamical Systems and Chaos Lecture 24: Bifurcation theory in higher dimensions. The Hopf bifurcation. MATH 614 Dynamical Systems and Chaos Lecture 24: Bifurcation theory in higher dimensions. The Hopf bifurcation. Bifurcation theory The object of bifurcation theory is to study changes that maps undergo

More information

are Banach algebras. f(x)g(x) max Example 7.4. Similarly, A = L and A = l with the pointwise multiplication

are Banach algebras. f(x)g(x) max Example 7.4. Similarly, A = L and A = l with the pointwise multiplication 7. Banach algebras Definition 7.1. A is called a Banach algebra (with unit) if: (1) A is a Banach space; (2) There is a multiplication A A A that has the following properties: (xy)z = x(yz), (x + y)z =

More information

THREE DIMENSIONAL SYSTEMS. Lecture 6: The Lorenz Equations

THREE DIMENSIONAL SYSTEMS. Lecture 6: The Lorenz Equations THREE DIMENSIONAL SYSTEMS Lecture 6: The Lorenz Equations 6. The Lorenz (1963) Equations The Lorenz equations were originally derived by Saltzman (1962) as a minimalist model of thermal convection in a

More information

Introduction to Applied Nonlinear Dynamical Systems and Chaos

Introduction to Applied Nonlinear Dynamical Systems and Chaos Stephen Wiggins Introduction to Applied Nonlinear Dynamical Systems and Chaos Second Edition With 250 Figures 4jj Springer I Series Preface v L I Preface to the Second Edition vii Introduction 1 1 Equilibrium

More information

Chapter 7. Extremal Problems. 7.1 Extrema and Local Extrema

Chapter 7. Extremal Problems. 7.1 Extrema and Local Extrema Chapter 7 Extremal Problems No matter in theoretical context or in applications many problems can be formulated as problems of finding the maximum or minimum of a function. Whenever this is the case, advanced

More information

Bifurcation Analysis, Chaos and Control in the Burgers Mapping

Bifurcation Analysis, Chaos and Control in the Burgers Mapping ISSN 1749-3889 print, 1749-3897 online International Journal of Nonlinear Science Vol.4007 No.3,pp.171-185 Bifurcation Analysis, Chaos and Control in the Burgers Mapping E. M. ELabbasy, H. N. Agiza, H.

More information

Hadamard and Perron JWR. October 18, On page 23 of his famous monograph [2], D. V. Anosov writes

Hadamard and Perron JWR. October 18, On page 23 of his famous monograph [2], D. V. Anosov writes Hadamard and Perron JWR October 18, 1999 On page 23 of his famous monograph [2], D. V. Anosov writes Every five years or so, if not more often, someone discovers the theorem of Hadamard and Perron proving

More information