Interpolation remainder theory from taylor expansions with non-rectangular domains of influence

Sobolev norm error bounds are derived for interpolation remainders on triangles using two types of Taylor expansion. These bounds are applied to the finite element analysis of Poisson's equation on a triangulation of a polygonal region

Smooth polynomial interpolation to boundary data on triangles

Boolean sum interpolation theory is used to derive a polynomial interpolant which interpolates a function F ∈ CN(∂T), and its derivatives of order N and less, on the boundary 3T of a triangle T. A triangle with one curved side is also considered

Compatable smooth interpolation in triangles

Boolean sum smooth interpolation to boundary data on a triangle is described. Sufficient conditions are given so that the functions when pieced together form a CN-1(Ω) function over a triangular subdivision of a polygonal region Ω and the precision sets of the interpolation functions are derived. The interpolants are modified so that the compatability conditions on the function which is interpolated can be removed and a C1 interpolant is used to illustrate the theory. The generation of interpolation schemes for discrete boundary data is also discussed

Exact and approximate boundary data interpolation in the finite element method

Matching boundary data exactly in an elliptic problem avoids one of Strang's "variational crimes". (Strang and Fix (1973)). Supporting numerical evidence for this procedure is given by Marshall and Mitchell (1973), who considered the solution of Laplace's equation with Dirichlet boundary data by bilinear elements over squares and measured the errors in the L2 norm. Then Marshall and Mitchell (1978) obtained some surprising results: for certain triangular elements, matching the boundary data exactly produced worse results than the usual procedure of interpolating the boundary data

Experimental exploration over a quantum control landscape through nuclear magnetic resonance

The growing successes in performing quantum control experiments motivated the development of control landscape analysis as a basis to explain these findings.When a quantum system is controlled by an electromagnetic field, the observable as a functional of the control field forms a landscape. Theoretical analyses have revealed many properties of control landscapes, especially regarding their slopes, curvatures, and topologies. A full experimental assessment of the landscape predictions is important for future consideration of controlling quantum phenomena. Nuclear magnetic resonance (NMR) is exploited here as an ideal laboratory setting for quantitative testing of the landscape principles. The experiments are performed on a simple two-level proton system in a H$_2$O-D$_2$O sample. We report a variety of NMR experiments roving over the control landscape based on estimation of the gradient and Hessian, including ascent or descent of the landscape, level set exploration, and an assessment of the theoretical predictions on the structure of the Hessian. The experimental results are fully consistent with the theoretical predictions. The procedures employed in this study provide the basis for future multispin control landscape exploration where additional features are predicted to exist

Searching for quantum optimal controls in the presence of singular critical points

Quantum optimal control has enjoyed wide success for a variety of theoretical and experimental objectives. These favorable results have been attributed to advantageous properties of the corresponding control landscapes, which are free from local optima if three conditions are met: (1) the quantum system is controllable, (2) the Jacobian of the map from the control field to the evolution operator is full rank, and (3) the control field is not constrained. This paper explores how gradient searches for globally optimal control fields are affected by deviations from assumption (2). In some quantum control problems, so-called singular critical points, at which the Jacobian is rank-deficient, may exist on the landscape. Using optimal control simulations, we show that search failure is only observed when a singular critical point is also a second-order trap, which occurs if the control problem meets additional conditions involving the system Hamiltonian and/or the control objective. All known second-order traps occur at constant control fields, and we also show that they only affect searches that originate very close to them. As a result, even when such traps exist on the control landscape, they are unlikely to affect well-designed gradient optimizations under realistic searching conditions.Comment: 14 pages, 2 figure

Searching for quantum optimal controls under severe constraints

The success of quantum optimal control for both experimental and theoretical objectives is connected to the topology of the corresponding control landscapes, which are free from local traps if three conditions are met: (1) the quantum system is controllable, (2) the Jacobian of the map from the control field to the evolution operator is of full rank, and (3) there are no constraints on the control field. This paper investigates how the violation of assumption (3) affects gradient searches for globally optimal control fields. The satisfaction of assumptions (1) and (2) ensures that the control landscape lacks fundamental traps, but certain control constraints can still introduce artificial traps. Proper management of these constraints is an issue of great practical importance for numerical simulations as well as optimization in the laboratory. Using optimal control simulations, we show that constraints on quantities such as the number of control variables, the control duration, and the field strength are potentially severe enough to prevent successful optimization of the objective. For each such constraint, we show that exceeding quantifiable limits can prevent gradient searches from reaching a globally optimal solution. These results demonstrate that careful choice of relevant control parameters helps to eliminate artificial traps and facilitate successful optimization.Comment: 16 pages, 7 figure