arXiv subjects

Tillmann Weisser

Publications and source records attributed to Tillmann Weisser.

7 recordsLinked to original sources

arXiv2021

Semi-algebraic approximation using Christoffel-Darboux kernel

We provide a new method to approximate a (possibly discontinuous) function using Christoffel-Darboux kernels. Our knowledge about the unknown multivariate function is in terms of finitely many moments of the Young measure supported on the graph of the function. Such an input is available when approximating weak (or measure-valued) solution of optimal control problems, entropy solutions to non-linear hyperbolic PDEs, or using numerical integration from finitely many evaluations of the function. While most of the existing methods construct a piecewise polynomial approximation, we construct a semi-algebraic approximation whose estimation and evaluation can be performed efficiently. An appealing feature of this method is that it deals with non-smoothness implicitly so that a single scheme can be used to treat smooth or non-smooth functions without any prior knowledge. On the theoretical side, we prove pointwise convergence almost everywhere as well as convergence in the Lebesgue one norm under broad assumptions. Using more restrictive assumptions, we obtain explicit convergence rates. We illustrate our approach on various examples from control and approximation. In particular we observe empirically that our method does not suffer from the the Gibbs phenomenon when approximating discontinuous functions.

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arXiv2020

Exploiting Sparsity for Semi-Algebraic Set Volume Computation

We provide a systematic deterministic numerical scheme to approximate the volume (i.e. the Lebesgue measure) of a basic semi-algebraic set whose description follows a sparsity pattern. As in previous works (without sparsity), the underlying strategy is to consider an infinite-dimensional linear program on measures whose optimal value is the volume of the set. This is a particular instance of a generalized moment problem which in turn can be approximated as closely as desired by solving a hierarchy of semidefinite relaxations of increasing size. The novelty with respect to previous work is that by exploiting the sparsity pattern we can provide a sparse formulation for which the associated semidefinite relaxations are of much smaller size. In addition, we can decompose the sparse relaxations into completely decoupled subproblems of smaller size, and in some cases computations can be done in parallel. To the best of our knowledge, it is the first contribution that exploits sparsity for volume computation of semi-algebraic sets which are possibly high-dimensional and/or non-convex and/or non-connected.

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arXiv2019

Optimal control problems with oscillations, concentrations and discontinuities

Optimal control problems with oscillations (chattering controls) and concentrations (impulsive controls) can have integral performance criteria such that concentration of the control signal occurs at a discontinuity of the state signal. Techniques from functional analysis (anisotropic parametrized measures) are applied to give a precise meaning of the integral cost and to allow for the sound application of numerical methods. We show how this can be combined with the Lasserre hierarchy of semidefinite programming relaxations.

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arXiv2018

Distributionally robust polynomial chance-constraints under mixture ambiguity sets

Given $X \subset R^n$, $\varepsilon \in (0,1)$, a parametrized family of probability distributions $(μ\_{a})\_{a\in A}$ on $Ω\subset R^p$, we consider the feasible set $X^*\_\varepsilon\subset X$ associated with the {\em distributionally robust} chance-constraint \[X^*\_\varepsilon\,=\,\{x \in X :\:{\rm Prob}\_μ[f(x,ω)\,>\,0]> 1-\varepsilon,\,\forallμ\in M\_a\},\]where $M\_a$ is the set of all possibles mixtures of distributions $μ\_a$, $a\in A$.For instance and typically, the family$M\_a$ is the set of all mixtures ofGaussian distributions on $R$ with mean and standard deviation $a=(a,σ)$ in some compact set $A\subset R^2$.We provide a sequence of inner approximations $X^d\_\varepsilon=\{x\in X: w\_d(x) <\varepsilon\}$, $d\in N$, where $w\_d$ is a polynomial of degree $d$ whosevector of coefficients is an optimal solution of a semidefinite program.The size of the latter increases with the degree $d$. We also obtain the strong and highly desirable asymptotic guarantee that $λ(X^*\_\varepsilon\setminus X^d\_\varepsilon)\to0$as $d$ increases, where $λ$ is the Lebesgue measure on $X$. Same resultsare also obtained for the more intricated case of distributionally robust "joint" chance-constraints.

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arXiv2018

A moment approach for entropy solutions to nonlinear hyperbolic PDEs

We propose to solve polynomial hyperbolic partial differential equations (PDEs) with convex optimization. This approach is based on a very weak notion of solution of the nonlinear equation, namely the measure-valued (mv) solution, satisfying a linear equation in the space of Borel measures. The aim of this paper is, first, to provide the conditions that ensure the equivalence between the two formulations and, second, to introduce a method which approximates the infinite-dimensional linear problem by a hierarchy of convex, finite-dimensional, semidefinite programming problems. This result is then illustrated on the celebrated Burgers equation. We also compare our results with an existing numerical scheme, namely the Godunov scheme.

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arXiv2018

Chance-Constrained Optimization for Non-Linear Network Flow Problems

Many engineered systems, such as energy and transportation infrastructures, are networks governed by non-linear physical laws. A primary challenge for operators of these networks is to achieve optimal utilization while maintaining safety and feasibility, especially in the face of uncertainty regarding the system model. To address this problem, we formulate a Chance Constrained Optimal Physical Network Flow (CC-OPNF) problem that attempts to optimize the system while satisfying safety limits with a high probability. However, the non-linear equality constraints representing the network physics introduce modelling and optimization challenges which make the chance constraints numerically intractable in their original form. The main contribution of the paper is to present a method to obtain tractable polynomial approximations to the chance constraints using Semidefinite Programming (SDP). The method uses a combination of existing semi-algebraic techniques for projection and volume computation in combination with novel set manipulations to provide conservative inner approximations to the chance constraints. In addition, we develop a new two-step procedure to improve computational speed. While the method is applicable to general physical network flow problems with polynomial constraints, we use the AC optimal power flow problem for electric grids as an example to demonstrate the method numerically.

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arXiv2017

Sparse-BSOS: a bounded degree SOS hierarchy for large scale polynomial optimization with sparsity

We provide a sparse version of the bounded degree SOS hierarchy BSOS [7] for polynomial optimization problems. It permits to treat large scale problems which satisfy a structured sparsity pattern. When the sparsity pattern satisfies the running intersection property this Sparse-BSOS hierarchy of semidefinite programs (with semidefinite constraints of fixed size) converges to the global optimum of the original problem. Moreover, for the class of SOS-convex problems, finite convergence takes place at the first step of the hierarchy, just as in the dense version.

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