Reproducing kernel Hilbert spaces and variable metric algorithms in PDE constrained shape optimisation

In this paper we investigate and compare different gradient algorithms designed for the domain expression of the shape derivative. Our main focus is to examine the usefulness of kernel reproducing Hilbert spaces for PDE constrained shape optimisation problems. We show that radial kernels provide convenient formulas for the shape gradient that can be efficiently used in numerical simulations. The shape gradients associated with radial kernels depend on a so called smoothing parameter that allows a smoothness adjustment of the shape during the optimisation process. Besides, this smoothing parameter can be used to modify the movement of the shape. The theoretical findings are verified in a number of numerical experiments

Reproducing kernel Hilbert spaces and variable metric algorithms in PDE constrained shape optimisation

In this paper we investigate and compare different gradient algorithms designed for the domain expression of the shape derivative. Our main focus is to examine the usefulness of kernel reproducing Hilbert spaces for PDE constrained shape optimisation problems. We show that radial kernels provide convenient formulas for the shape gradient that can be efficiently used in numerical simulations. The shape gradients associated with radial kernels depend on a so called smoothing parameter that allows a smoothness adjustment of the shape during the optimisation process. Besides, this smoothing parameter can be used to modify the movement of the shape. The theoretical findings are verified in a number of numerical experiments

Piecewise rigid curve deformation via a Finsler steepest descent

This paper introduces a novel steepest descent flow in Banach spaces. This extends previous works on generalized gradient descent, notably the work of Charpiat et al., to the setting of Finsler metrics. Such a generalized gradient allows one to take into account a prior on deformations (e.g., piecewise rigid) in order to favor some specific evolutions. We define a Finsler gradient descent method to minimize a functional defined on a Banach space and we prove a convergence theorem for such a method. In particular, we show that the use of non-Hilbertian norms on Banach spaces is useful to study non-convex optimization problems where the geometry of the space might play a crucial role to avoid poor local minima. We show some applications to the curve matching problem. In particular, we characterize piecewise rigid deformations on the space of curves and we study several models to perform piecewise rigid evolution of curves

Probabilistic Gradients for Fast Calibration of Differential Equation Models

Calibration of large-scale differential equation models to observational or experimental data is a widespread challenge throughout applied sciences and engineering. A crucial bottleneck in state-of-the art calibration methods is the calculation of local sensitivities, i.e. derivatives of the loss function with respect to the estimated parameters, which often necessitates several numerical solves of the underlying system of partial or ordinary differential equations. In this paper we present a new probabilistic approach to computing local sensitivities. The proposed method has several advantages over classical methods. Firstly, it operates within a constrained computational budget and provides a probabilistic quantification of uncertainty incurred in the sensitivities from this constraint. Secondly, information from previous sensitivity estimates can be recycled in subsequent computations, reducing the overall computational effort for iterative gradient-based calibration methods. The methodology presented is applied to two challenging test problems and compared against classical methods

Weighted p-regular kernels for reproducing kernel Hilbert spaces and Mercer Theorem

[EN] Let (X, Sigma, mu) be a finite measure space and consider a Banach function space Y(mu). Motivated by some previous papers and current applications, we provide a general framework for representing reproducing kernel Hilbert spaces as subsets of Kothe Bochner (vectorvalued) function spaces. We analyze operator-valued kernels Gamma that define integration maps L-Gamma between Kothe-Bochner spaces of Hilbert-valued functions Y(mu; kappa). We show a reduction procedure which allows to find a factorization of the corresponding kernel operator through weighted Bochner spaces L-P(gd mu; kappa) and L-P (hd mu; kappa) - where 1/p + 1/p' = 1 - under the assumption of p-concavity of Y(mu). Equivalently, a new kernel obtained by multiplying Gamma by scalar functions can be given in such a way that the kernel operator is defined from L-P (mu; kappa) to L-P (mu; kappa) in a natural way. As an application, we prove a new version of Mercer Theorem for matrix-valued weighted kernels.The second author acknowledges the support of the Ministerio de Economia y Competitividad (Spain), under project MTM2014-53009-P (Spain). The third author acknowledges the support of the Ministerio de Ciencia, Innovacion y Universidades (Spain), Agencia Estatal de Investigacion, and FEDER under project MTM2016-77054-C2-1-P (Spain).Agud Albesa, L.; Calabuig, JM.; Sánchez Pérez, EA. (2020). Weighted p-regular kernels for reproducing kernel Hilbert spaces and Mercer Theorem. Analysis and Applications. 18(3):359-383. https://doi.org/10.1142/S0219530519500179S359383183Agud, L., Calabuig, J. M., & Sánchez Pérez, E. A. (2011). The weak topology on q-convex Banach function spaces. Mathematische Nachrichten, 285(2-3), 136-149. doi:10.1002/mana.201000030CARMELI, C., DE VITO, E., & TOIGO, A. (2006). VECTOR VALUED REPRODUCING KERNEL HILBERT SPACES OF INTEGRABLE FUNCTIONS AND MERCER THEOREM. Analysis and Applications, 04(04), 377-408. doi:10.1142/s0219530506000838CARMELI, C., DE VITO, E., TOIGO, A., & UMANITÀ, V. (2010). VECTOR VALUED REPRODUCING KERNEL HILBERT SPACES AND UNIVERSALITY. Analysis and Applications, 08(01), 19-61. doi:10.1142/s0219530510001503Cerdà, J., Hudzik, H., & Mastyło, M. (1996). Geometric properties of Köthe–Bochner spaces. Mathematical Proceedings of the Cambridge Philosophical Society, 120(3), 521-533. doi:10.1017/s0305004100075058Chavan, S., Podder, S., & Trivedi, S. (2018). Commutants and reflexivity of multiplication tuples on vector-valued reproducing kernel Hilbert spaces. Journal of Mathematical Analysis and Applications, 466(2), 1337-1358. doi:10.1016/j.jmaa.2018.06.062Christmann, A., Dumpert, F., & Xiang, D.-H. (2016). On extension theorems and their connection to universal consistency in machine learning. Analysis and Applications, 14(06), 795-808. doi:10.1142/s0219530516400029Defant, A. (2001). Positivity, 5(2), 153-175. doi:10.1023/a:1011466509838Defant, A., & Sánchez Pérez, E. A. (2004). Maurey–Rosenthal factorization of positive operators and convexity. Journal of Mathematical Analysis and Applications, 297(2), 771-790. doi:10.1016/j.jmaa.2004.04.047De Vito, E., Umanità, V., & Villa, S. (2013). An extension of Mercer theorem to matrix-valued measurable kernels. Applied and Computational Harmonic Analysis, 34(3), 339-351. doi:10.1016/j.acha.2012.06.001Eigel, M., & Sturm, K. (2017). Reproducing kernel Hilbert spaces and variable metric algorithms in PDE-constrained shape optimization. Optimization Methods and Software, 33(2), 268-296. doi:10.1080/10556788.2017.1314471Fasshauer, G. E., Hickernell, F. J., & Ye, Q. (2015). Solving support vector machines in reproducing kernel Banach spaces with positive definite functions. Applied and Computational Harmonic Analysis, 38(1), 115-139. doi:10.1016/j.acha.2014.03.007Galdames Bravo, O. (2014). Generalized Kӧthe $p$-dual spaces. Bulletin of the Belgian Mathematical Society - Simon Stevin, 21(2). doi:10.36045/bbms/1400592625Lin, P.-K. (2004). Köthe-Bochner Function Spaces. doi:10.1007/978-0-8176-8188-3Lindenstrauss, J., & Tzafriri, L. (1979). Classical Banach Spaces II. doi:10.1007/978-3-662-35347-9Meyer-Nieberg, P. (1991). Banach Lattices. Universitext. doi:10.1007/978-3-642-76724-1Okada, S., Ricker, W. J., & Sánchez Pérez, E. A. (2008). Optimal Domain and Integral Extension of Operators. doi:10.1007/978-3-7643-8648-1Zhang, H., & Zhang, J. (2013). Vector-valued reproducing kernel Banach spaces with applications to multi-task learning. Journal of Complexity, 29(2), 195-215. doi:10.1016/j.jco.2012.09.00

Smoothing under Diffeomorphic Constraints with Homeomorphic Splines

In this paper we introduce a new class of diffeomorphic smoothers based on general spline smoothing techniques and on the use of some tools that have been recently developed in the context of image warping to compute smooth diffeomorphisms. This diffeomorphic spline is defined as the solution of an ordinary differential equation governed by an appropriate time-dependent vector field. This solution has a closed form expression which can be computed using classical unconstrained spline smoothing techniques. This method does not require the use of quadratic or linear programming under inequality constraints and has therefore a low computational cost. In a one dimensional setting incorporating diffeomorphic constraints is equivalent to impose monotonicity. Thus, as an illustration, it is shown that such a monotone spline can be used to monotonize any unconstrained estimator of a regression function, and that this monotone smoother inherits the convergence properties of the unconstrained estimator. Some numerical experiments are proposed to illustrate its finite sample performances, and to compare them with another monotone estimator. We also provide a two-dimensional application on the computation of diffeomorphisms for landmark and image matching