Past exam of the mathematics course of the University of Cambridge 2007 iii Paper 71 6 Solution Created 2026-10-03 Updated 2026-10-14
A collocation Runge-Kutta method approximates an ordinary differential equation by a polynomial whose derivative agrees with the vector field at selected times. Let distinct nodes be , and consider the possibly nonautonomous equation . On a step from , choose a polynomial of degree at most , with , such that . Here is scaled time; the numerical endpoint is .
Let be the Lagrange interpolation polynomial equal to one at and zero at the other nodes, and set . Interpolation of the polynomial derivative givesEvaluating at the nodes and endpoint derives the Runge-Kutta method coefficients:Thus collocation is a specific family of generally implicit RK methods, not a separate update mechanism. The identity gives and , the collocation tableau row-sum identity. For sufficiently small , a vector field with Lipschitz constant gives a contraction of the stage map when , on a suitable bounded neighborhood. The Banach fixed-point theorem then gives a unique nearby stage solution. Equivalently smooth stage equations have identity derivative in the stage variables at , so the inverse function theorem gives a smooth nearby branch. Arbitrarily large-step solvability is a separate issue for an implicit Runge-Kutta method.
Order analysis has two levels: polynomial accuracy at stages and extra accuracy at the endpoint. Integration of interpolated powers gives the stage moment identitiesAn induction in powers of , using these identities in the stage equations, shows that the collocation polynomial agrees coefficientwise with the exact Taylor polynomial through degree :For example, the coefficient of each new stage term is integrated exactly because its node dependence is a polynomial of degree at most . This proves stage order and bounded actual-time derivatives of the collocation polynomial up to degree . Assuming the vector field is sufficiently smooth on a fixed compact solution neighborhood, its composition with this polynomial has uniformly bounded derivatives of all finite orders needed below.
The endpoint weights define the interpolatory quadrature rule . Suppose it is exact through degree , but not through degree . Necessarily : interpolation integrates degrees below exactly, while the nonzero polynomial , with , has positive integral but quadrature value zero and degree . Exactness also implies the nodal-polynomial criterion for quadrature exactnessbecause the integrand has degree at most and vanishes at every node.
Here is the endpoint-error argument underlying the collocation order theorem. Let and be its residual. It vanishes at the collocation times, and if , then , where is interpolation at the nodes. Taylor expansion in actual time givesThe vector coefficients are bounded. Each bracket has the factor , with quotient degree . In particular the residual is throughout the step.
Write for the exact flow from time and state to . Differentiating gives : the derivative with respect to the starting time cancels the vector field by the flow composition identity. Integration therefore gives the exact local endpoint errorFor , the residual already gives . For , Taylor-expand the smooth matrix weight as . Every product term with integrates to zero: its polynomial factor is times a polynomial of degree . All remaining terms are before the time integration, including both remainders. HenceThis is collocation endpoint superconvergence by quadrature orthogonality. For the global assertion, the stage contraction estimate gives a one-step Lipschitz bound on a fixed solution neighborhood. Thus the propagated error satisfies , and the discrete Gronwall inequality gives on a fixed time interval. The order cannot generically exceed : for , each collocation step reduces to the same quadrature rule, whose first nonexact moment yields a nonzero local error and composite error.
The usual node families illustrate the result. Gauss collocation methods use interior Gaussian nodes and have , the maximum possible order. Right-endpoint Radau nodes give the Radau IIA method of order . Including both endpoints with Lobatto quadrature gives the Lobatto IIIA method of order for . One-stage Gaussian collocation is the second-order implicit midpoint rule, while collocation at the right endpoint is the first-order Backward Euler method. The two-stage Gaussian nodes give anda fourth-order method. Node placement, rather than just stage count, controls the superconvergent endpoint order.
There is also a structural link with Question 2. For Gaussian nodes, let . The derivative of has degree at most , so Gaussian quadrature is exact on it. The integral is , while evaluating at the nodes gives . Thus Gaussian collocation satisfies and has Runge-Kutta conservation of quadratic invariants. For the scalar test equation, any RK method has stability function . For implicit midpoint this is ; the identity proves A-stability. High order, stage solvability, invariant preservation and damping are distinct design considerations, so no stability conclusion should be inferred from collocation order alone.