Numerical Investigation of Soft-to-Hard Convergence: Discretization, Admissibility, and Spectral Regularity
This paper presents a numerical investigation of soft-to-hard terminal constraint convergence for optimal control problems governed by a second-order ordinary differential equation and the one-dimensional heat equation. The study complements the analytical convergence theory through comparisons with exact solutions and independently computed numerical approximations. For the heat equation, a fully discrete Crank--Nicolson finite-difference formulation is validated against exact modal solutions, exhibiting approximately second-order convergence in the terminal-state and midpoint-sampled control errors under simultaneous space-time mesh refinement. Numerical experiments demonstrate the predicted first-order penalty convergence for benchmark problems and investigate the admissibility threshold governing finite-energy exact terminal controllability. Additional modal experiments examine the dependence of terminal-error convergence rates on spectral regularity, illustrating intermediate rates consistent with the theoretical estimates and asymptotic predictions. The results provide computational evidence supporting the soft-to-hard convergence framework and clarify the roles of discretization, target admissibility, and spectral regularity.