Tomoya Takeuchi

Identification of source locations in two-dimensional heat equations

In this paper, we show the uniqueness of the identification of unknown source locations in two-dimensional heat equations from scattered measurements. Based on the assumption that the unknown source function is a sum of some known functions, we prove that one measurement point is sufficient to identify the number of sources and three measurement points are sufficient to determine all unknown source locations. For verification, we propose a numerical reconstruction scheme for recovering the number of unknown sources and all source locations.

A Regularization Parameter for Nonsmooth Tikhonov Regularization

In this paper we develop a novel rule for choosing regularization parameters in nonsmooth Tikhonov functionals. It is solely based on the value function and applicable to a broad range of nonsmooth models, and it extends one known criterion. A posteriori error estimates of the approximations are derived. An efficient numerical algorithm for computing the minimizer is developed, and its convergence properties are discussed. Numerical results for several common nonsmooth models are presented, including deblurring natural images. The numerical results indicate the rule can yield results comparable with those achieved with the discrepancy principle and the optimal choice, and the algorithm merits a fast and steady convergence.

Tikhonov Regularization by a Reproducing Kernel Hilbert Space for the Cauchy Problem for an Elliptic Equation

We propose a discretized Tikhonov regularization for a Cauchy problem for an elliptic equation by a reproducing kernel Hilbert space. We prove the convergence of discretized regularized solutions to an exact solution. Our numerical results demonstrate that our method can stably reconstruct solutions to the Cauchy problems even in severe cases of geometric configurations.

Discretized Tikhonov regularization by reproducing kernel Hilbert space for backward heat conduction problem

In this paper we propose a numerical reconstruction method for solving a backward heat conduction problem. Based on the idea of reproducing kernel approximation, we reconstruct the unknown initial heat distribution from a finite set of scattered measurement of transient temperature at a fixed final time. Standard Tikhonov regularization technique using the norm of reproducing kernel is adopt to provide a stable solution when the measurement data contain noises. Numerical results indicate that the proposed method is stable, efficient, and accurate.

On the sectorial property of the Caputo derivative operator

In this note, we establish the sectorial property of the Caputo fractional derivative operator of order α∈(1,2) with a zero Dirichlet boundary condition.

On a Legendre Tau method for fractional boundary value problems with a Caputo derivative

Abstract In this paper, we revisit a Legendre-tau method for two-point boundary value problems with a Caputo fractional derivative in the leading term, and establish an L2 error estimate for smooth solutions. Further, we apply the method to the Sturm-Liouville problem. Numerical experiments indicatethat for the source problem, it converges steadily at an algebraic rate even for nonsmooth data, and the convergence rate enhances with problem data regularity, whereas for the Sturm-Liouville problem, it always yields excellent convergence for eigenvalue approximations.

Optimal control laws for traffic flow

Abstract Optimal “on–off” laws for the traffic signals are developed based on the bilinear control problem with the binary constraints. A Lyapunov function based feedback law for regulating traffic congestions is developed. Also, a real-time optimal signal law is developed using a novel binary optimization method. Both methods are tested and compared, and our tests demonstrate that the both methods provide very effective and efficient traffic control laws.

Lagrange optimality system for a class of nonsmooth convex optimization

In this paper, we revisit the augmented Lagrangian method for a class of nonsmooth convex optimization. We present the Lagrange optimality system of the augmented Lagrangian associated with the problems, and establish its connections with the standard optimality condition and the saddle point condition of the augmented Lagrangian, which provides a powerful tool for developing numerical algorithms: we derive a Lagrange–Newton algorithm for the nonsmooth convex optimization, and establish the nonsingularity of the Newton system and the local convergence of the algorithm.

Immersed Interface CIP for One Dimensional Hyperbolic Equations

We propose a multi-moment method for one-dimensional hyperbolic equations with smooth coefficient and piecewise constant coefficient. The method is entirely based on the backward characteristic method and uses the solution and its derivative as unknowns and cubic Hermite interpolation for each computational cell. The exact update formula for solution and its derivative is derived and used for an efficient time integration. At points of discontinuity of wave speed we define a piecewise cubic Hermite interpolation based on immersed interface method. The method is extended to the one-dimensional Maxwell’s equations with variable material properties.