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This article investigates the numerical approximation of shape optimization problems with PDE constraint on classes of convex domains. The convexity constraint provides a compactness property which implies well posedness of the problem.…

Optimization and Control · Mathematics 2018-10-26 Sören Bartels , Gerd Wachsmuth

We are interested in the optimization of convex domains under a PDE constraint. Due to the difficulties of approximating convex domains in $\mathbb{R}^3$, the restriction to rotationally symmetric domains is used to reduce shape…

Optimization and Control · Mathematics 2022-06-13 Hedwig Keller , Sören Bartels , Gerd Wachsmuth

Many problems of theoretical and practical interest involve finding an optimum over a family of convex functions. For instance, finding the projection on the convex functions in $H^k(\Omega)$, and optimizing functionals arising from some…

Numerical Analysis · Mathematics 2008-04-11 Néstor E. Aguilera , Pedro Morin

We consider the problem of approximating the solution of variational problems subject to the constraint that the admissible functions must be convex. This problem is at the interface between convex analysis, convex optimization, variational…

Numerical Analysis · Mathematics 2015-03-19 Adam M. Oberman

We provide a general framework to construct finite dimensional approximations of the space of convex functions, which also applies to the space of c-convex functions and to the space of support functions of convex bodies. We give estimates…

Numerical Analysis · Mathematics 2014-03-11 Quentin Mérigot , Edouard Oudet

This article proposes a new discrete framework for approximating solutions to shape optimization problems under convexity constraints. The numerical method, based on the support function or the gauge function, is guaranteed to generate…

Optimization and Control · Mathematics 2022-03-15 Beniamin Bogosel

Polynomial approximations of functions are widely used in scientific computing. In certain applications, it is often desired to require the polynomial approximation to be non-negative (resp. non-positive), or bounded within a given range,…

Numerical Analysis · Mathematics 2024-11-12 Yuan Chen , Dongbin Xiu , Xiangxiong Zhang

We develop two adaptive discretization algorithms for convex semi-infinite optimization, which terminate after finitely many iterations at approximate solutions of arbitrary precision. In particular, they terminate at a feasible point of…

Optimization and Control · Mathematics 2022-01-14 Jochen Schmid , Miltiadis Poursanidis

An eigenvalue problem arising in optimal insulation related to the minimization of the heat decay rate of an insulated body is adapted to enforce a positive lower bound imposed on the distribution of insulating material. We prove the…

Numerical Analysis · Mathematics 2024-10-22 Sören Bartels , Giuseppe Buttazzo , Hedwig Keller

We obtain some sharp estimates for the $p$-torsion of convex planar domains in terms of their area, perimeter, and inradius. The approach we adopt relies on the use of web functions (i.e. functions depending only on the distance from the…

Optimization and Control · Mathematics 2011-12-22 Ilaria Fragalà , Filippo Gazzola , Jimmy Lamboley

We consider the problem of domain approximation in finite element methods for Maxwell equations on curved domains, i.e., when affine or polynomial meshes fail to exactly cover the domain of interest. In such cases, one is forced to…

Numerical Analysis · Mathematics 2022-01-05 Rubén Aylwin , Carlos Jerez-Hanckes

Since the 1960's the finite element method emerged as a powerful tool for the numerical simulation of countless physical phenomena or processes in applied sciences. One of the reasons for this undeniable success is the great versatility of…

Numerical Analysis · Mathematics 2018-12-05 Vitoriano Ruas

Shape optimization models with one or more shapes are considered in this chapter. Of particular interest for applications are problems in which where a so-called shape functional is constrained by a partial differential equation (PDE)…

Optimization and Control · Mathematics 2021-07-19 Caroline Geiersbach , Estefania Loayza-Romero , Kathrin Welker

We present a general shape optimisation framework based on the method of mappings in the $W^{1,\infty}$ topology. We propose steepest descent and Newton-like minimisation algorithms for the numerical solution of the respective shape…

Optimization and Control · Mathematics 2025-06-02 Klaus Deckelnick , Philip J. Herbert , Michael Hinze

In this paper error analysis for finite element discretizations of Dirichlet boundary control problems is developed. For the first time, optimal discretization error estimates are established in the case of three dimensional polyhedral and…

Numerical Analysis · Mathematics 2024-01-05 Johannes Pfefferer , Boris Vexler

In this paper we consider the problem of constructing numerical algorithms for approximating of convex compact bodies in d-dimensional Euclidean space by polytopes with any given accuracy. It is well known that optimal with respect to the…

Metric Geometry · Mathematics 2018-12-10 G. K. Kamenev

We consider the problem of approximating a two-dimensional shape contour (or curve segment) using discrete assembly systems, which allow to build geometric structures based on limited sets of node and edge types subject to edge length and…

Computational Geometry · Computer Science 2021-02-03 Andreas M. Tillmann , Leif Kobbelt

We present a general numerical approach to shape optimization with state constraints for 2-dimensional geometries, without relaxing the constraints. To do this we reformulate the problem on a fixed reference domain using a conformal…

Optimization and Control · Mathematics 2014-12-16 Christian Leithäuser , René Pinnau , Robert Feßler

Polyhedral-type approximations of convex-like domains in $\mathbb{C}^d$ have been considered recently by the second author. In particular, the decay rate of the error in optimal volume approximation as a function of the number of facets has…

Probability · Mathematics 2022-03-24 Siva Athreya , Purvi Gupta , D. Yogeshwaran

Consider convex optimization problems subject to a large number of constraints. We focus on stochastic problems in which the objective takes the form of expected values and the feasible set is the intersection of a large number of convex…

Machine Learning · Statistics 2015-11-13 Mengdi Wang , Yichen Chen , Jialin Liu , Yuantao Gu
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