Joint Research Seminar on
The research seminar serves as a platform for the presentation of current
research results of group members at WIAS and at the HU Berlin (Mathematical
Optimization) and of invited guests.
Graduate students in Mathematical Optimization with a sound background in Optimization, Numerical Analysis,
Functional Analysis and Partial Differential Equations from HU Berlin are welcome to
take part in the seminar.
The regular date of the seminar is
Mathematical Optimization / Non-smooth Variational Problems and Operator Equations
Wednesday, 3 p.m. at WIAS, Mohrenstraße 39, Erhard-Schmidt-Hörsaal. Please be aware that occasional changes in time or venue might occur, but will be announced here.
Thursday, November 21, 11:00 a.m. (WIAS-ESH)
Jo Brüggemann (WIAS)
On the existence of solutions and solution methods for elliptic obstacle-type quasi-variational inequalities with volume constraints
In this talk, an elliptic obstacle-type quasi-variational inequality (QVI) with volume constraints is studied. This type of QVI is motivated by the reformulation of a compliant obstacle problem, where two elastic membranes are subject to external forces while enclosing a constant volume. The existence of solutions to this QVI is established building on fixed-point arguments and partly on the concept of Mosco-convergence. Since Mosco-convergence of the considered feasible sets usually requires complete continuity or compactness properties of the obstacle map, a two-fold approach is explored towards generalising the available existence results for the considered QVI. Based on the analytical findings, the solution of the QVI is approached by solving a sequence of variational inequalities (VIs). Each of these VIs is tackled in function space via a path-following semismooth Newton method. An a posteriori error estimator is derived towards enhancement of the algorithm's numerical performance by using adaptive finite element methods.
Friday, November 1, 2:00 p.m. (WIAS-ESH)
Ana Djurdjevac (FU Berlin)
Random PDEs on moving hypersurfaces
It is well-known that in a variety of applications, especially in the biological modeling, PDEs that appear can be better formulated on evolving curved domains. Most of these equations contain various parameters and often there is a degree of uncertainty regarding the given data. We investigate the uncertainty which comes from unknown parameters or geometry. In the first part we study the advection-diffusion equation with random coefficients that is posed on an evolving hypersurface. We consider both cases, uniformly bounded and log-normal distributions of the coefficient. We introduce the solution space and prove the well-posedness using Banach - Nechas - Babushka theorem. Furthermore, we will introduce and analyse the evolving surface finite element discretization of the equation, introduced by Dziuk and Elliott. In the uniformly bounded case, we show unique solvability of the resulting semi-discrete problem and prove optimal error bounds for the semi-discrete solution and Monte Carlo of its expectation. In the second part we study PDEs that evolve with a given random velocity. Utilizing the domain mapping method, we transfer the problem into a PDE with random coefficients on a fixed domain and analyse this equation. Our theoretical convergence rates are confirmed by numerical experiments. This work is supported by DFG through project AA1-3 of MATH+.
Wednesday, October 23, 11:00 a.m. (WIAS-HVP11A-R 3.13)
Martin Holler (U Graz)
A variational model for learning convolutional image atoms from incomplete data
Using lifting and relaxation strategies, we present a convex variational model for learning a convolutional sparse representation of image data via a few basic atoms. Such a representation provides a model for repeating patterns, but is also of interest for classification or as structural prior. We ensure well-posedness results for the proposed model in a general inverse problems setting and provide numerical experiments, where an atom-based representation is computed from incomplete, noisy and blurry data.
Thursday, September 26, 3:00 p.m. (WIAS-R 406)
Martin Brokate (TU Munich)
Sensitivity in rate independent evolutions
As a topic in science, rate independent evolutions have appeared more than 100 years ago; their study as a mathematical subject in its own began in the 1960's. We will present some basic issues and then discuss in particular the question of differential sensitivity, that is, whether the associated solution operators possess weak derivatives.
Friday, August 2, 3:15 p.m. (WIAS-ESH)
Ya-xiang Yuan (Academy of Mathematics and Systems Science, Chinese Academy of Sciences)
Efficient optimization algorithms for large scale data analysis
In this talk, two classes of problems in large scale data analysis and their optimization algorithms will be discussed. The first class focuses on composite convex program problems, where I introduce algorithms including a regularized semi-smooth Newton method, a stochastic semi-smooth Newton method and a parallel subspace correction method. The second class is on optimization with orthogonality constraints, particularly on parallelizable approaches for linear eigenvalue problems and nonlinear eigenvalue problems, and quasi-Newton type methods. Numerical results of applications, e.g., electronic structure calculations, $l_1$-regularized logistic regression problems, Lasso problems and Hartree-Fock total energy minimization problems, will be highlighted.
Thursday, July 11, 3:00 p.m. (WIAS-405-406)
Georg Stadler (Courant Institute of Mathematical Sciences, New York university)
Sparse optimal control of PDEs with uncertain coefficients
I will discuss sparse solutions of optimal control problems governed by elliptic PDEs with uncertain coefficients. Sparsity of controls is achieved by incorporating the L^1-norm of the mean of the pointwise squared controls in the objective. The main focus is on stochastic controls that share the same sparsity structure, i.e., controls that depend on the realization of the random parameters but have identical support. We propose an iterative norm reweighting formulation, which iterates over functions defined over the physical space only and thus avoids approximation of the random space. Combining a Newton method with low-rank operator approximations, this results in an efficient solution method that avoids approximation of the uncertain parameter random space. The qualitative structure of the optimal controls and the performance of the solution algorithm are studied numerically using control problems governed by the Laplace and Helmholtz equations. This is joint work with Chen Li (NYU).
Wednesday, July 10, 1:00 p.m. (WIAS-ESH)
Georg Stadler (Courant Institute of Mathematical Sciences, New York university)
Estimation of extreme event probabilities by combining large deviation theory and PDE-constrained optimization, with application to tsunami waves
Tsunami waves are caused by a sudden change of ocean depth (bathymetry) after an earthquake below the ocean floor. Since large tsunami waves are extreme events, they correspond to the tail part of a probability distribution, whose exploration would require impractically many samples of a Monte Carlo method. We propose an alternative method to estimate extreme probabilities using large deviation theory (LDT), which relates the probabilities of extreme events to the solutions of a one-parameter family of optimization problems. We model tsunami waves with the shallow water equations and thus these equations appear as PDE-constraints in this optimization problem. The optimization objective includes a term that measures how extreme the event is, and a term corresponding to the likelihood of certain bathymetry changes, which are modeled as a Gaussian random field. Preliminary numerical results with the 1D inviscid shallow water equation are presented. This is joint work with Shanyin Tong and Eric Vanden-Eijnden (both NYU).
Monday, June 3, 11:30 a.m. (WIAS-ESH)
Guozhi Dong (Humboldt Universität zu Berlin)
Quantitative magnetic resonance imaging: From fingerprinting to integrated physics-based models
In this talk, we introduce a novel method for quantitative MRI. The proposed approach simultaneously recovers the tissue parameters as the recently developed method magnetic resonance fingerprinting (MRF). However, in comparison with MRF and many of its variants, our new model is dictionary free. Thus, its accuracy is independent of the discretization size of the dictionary. The efficiency of our new method is proved theoretically and also verified by numerical examples.
Monday, June 3, 11 a.m. (WIAS-ESH)
Selma Metzer (PTB - TU Berlin)
Approximate large-scale Bayesian inference with application to magnetic resonance fingerprinting
A class of nonlinear, large-scale regression problems is considered where the parameters model the spatial distribution of some property. Specific assumptions about the regression function are made and a homoscedastic Gaussian sampling distribution is assumed. The class of problems includes the task of Magnetic Resonance Fingerprinting (MRF), a new approach for quantitative Magnetic Resonance Imaging that allows for the estimation of absolute values of tissue related parameters like proton density and spin relaxation times. The properties of a Bayesian inference for the considered class of regression problems is investigated when different types of priors are employed, including Gaussian Markov random field priors expressing spatial smoothness. The properties of the resulting posteriors are explored and conditions for propriety of the posterior and existence of its moments established. An approximate calculation scheme is proposed and its practicability demonstrated up to dimensions of 105. Finally, the approach is applied to MRF. In using simulated data with known ground truth, it is shown that by using the prior knowledge of smoothness in the spatial distribution of the sought parameters, the results are significantly better than those achieved through maximum likelihood estimation.
Wednesday, January 23, 1 p.m. (WIAS-ESH)
Maria Soledad Aronna (EMAp/FGV - TU Berlin)Wednesday, January 16, 1 p.m. (WIAS-ESH)
On second order optimality conditions for control-affine problems: the finite and infinite dimensional case
In this talk I will present the main features of first and second order optimality conditions for optimal control problems of ordinary differential equations that are affine with respect to the control. Problems will be considered in the presence of control constraints. Both necessary and sufficient conditions for optimality will be presented. I will then show the extension of those results for a class of bilinear partial differential equations. Finally, if time allows it, I will briefly discuss the state-constrained case.
Tobias Keil (WIAS Berlin)
Optimal control of a coupled Cahn-Hilliard-Navier-Stokes system with variable fluid densities
This talk is concerned with the optimal control of two immiscible fluids with non-matched densities. For the mathematical formulation of the fluid phases, we use a coupled Cahn- Hilliard/Navier-Stokes system by Abels, Garcke and Grün, which involves a variational ine- quality of fourth order. We verify the existence of solutions to a suitable time discretization of the system and formulate an associated optimal control problem. We further discuss the differentiability properties of the control-to-state operator and the corresponding stationari- ty concepts and present strong stationarity conditions for the problem. This enables us to provide a numerical solution algorithm which terminates at an at least C-stationary point which - in the best case - is even strongly stationary. The method is based on an adaptation of a bundle-free implicit programming approach for MPECs in function space presented by Hintermüller and Surowiec in 2016.
Wednesday, December 19, 11:00 a.m. (WIAS-ESH)
O. Huber (WIAS)
Using reformulations in nonsmooth mathematical programming: the examples of friction contact problems and optimal value functions
In this talk we illustrate how reformulations can be used to deal with nonsmoothness. In particular, we look at two specific structures arising from applications: the first one concerns finite dimensional variational inequalities with a nonsmooth functional. This is motivated by the study of contact problems with Coulomb friction. The second example involves optimization models with Optimal Value Functions (OVF) in the problem data. The OVF concept subsumes regularizers from fitting problems and coherent risk measures from Stochastic Optimization. Each reformulation scheme rely on tools from convex and variational analysis. In both instances, we obtain equivalent problems that are solvable by off-the-shelf solvers. Finally, numerical results are presented and discussed: in the OVF case, the example is a risk-averse equilibrium problem from the electricity market.
Friday, November 30, 1:00 p.m. (WIAS-ESH)
S.-M. Stengl (WIAS)
Uncertainty Quantification of the Ambrosio--Tortorelli approximation in image segmentation
In this talk we want to deal with quantification of uncertainties in image segmentation based on the Mumford--Shah model. The aim is to address the error propagation of noise and other error types in the original image to the segmentation and especially the edges. We analyze therefore the in the literature well-known Ambrosio--Tortorelli approximation and discuss the existence of measurable selections of its solutions as well as sampling-based methods. We end the talk with numerical examples.
Monday, September 10, 1:30 p.m. (WIAS-ESH)
K. Ito (North Carolina State University)
Semismooth Newton method for variational inequalities and MPEC
Wednesday, June 20, 1:00 p.m. (WIAS-Mo 39, 4th floor)
L. Goubergrits (Charité - Universitätsmedizin Berlin)
Modelling and simulation for treatment planning: CFD methods for valve treatment
In aging population, the prevalence of heart valve diseases and heart failure with more than 8 per cent of the population in their 70s (valve disease) and 10 per cent in their 80s (heart failure) is one of the most relevant diseases for the healthcare system in industrial countries. Both diseases are chronic and can amplify each other. Wrongly treated valve diseases can lead to severe heart failure and vice versa. The prognosis of heart failure is still very poor (6-year mortality rate more than 67 per cent). CFD approach promises precise diagnosis without invasive procedures. Furthermore, CFD allows predictiv modelling allowing to support clinicians with treatment decision as well as treatment planing and optimization. Finally CFD approach promises risk and cost minimization. Current CFD abilities, challenges and requirements for CFD translation into the clinical practice are presented and discussed.
Monday, June 18, 3:00 p.m. (WIAS-Mo 39, 4th floor)
J. Pfefferer (TU Munich)
hp-finite elements for fractional diffusion
In this talk we introduce and analyze a numerical scheme based on hp-finite elements to solve boundary value problems involving the spectral fractional Laplacian. The approach is based on a reformulation of the problem posed on a semi-infinite cylinder in one more spatial dimension. After a suitable truncation of this cylinder, the resulting problem is discretized with linear finite elements in the original domain and with hp-finite elements in the extended direction. The proposed approach yields a reduction of the computational complexity in terms of degrees of freedom and even has slightly improved convergence properties compared to the state-of-the-art discretization using linear finite elements for both the original domain and the extended direction. The performance of the method is illustrated by numerical experiments.
Wednesday, June 13, 1:00 p.m. (WIAS-ESH)
K. Welker (U Trier)
Constrained shape optimization problems in shape spaces
Shape optimization problems arise frequently in technological processes which are modelled in the form of partial differential equations (PDEs) or variational inequalities (VIs). In many practical circumstances, the shape under investigation is parametrized by finitely many parameters, which on the one hand allows the application of standard optimization approaches, but on the other hand limits the space of reachable shapes unnecessarily. In this talk, the theory of shape optimization is connected to the differential-geometric structure of shape spaces. In particular, efficient algorithms in terms of shape spaces and the resulting framework from infinite dimensional Riemannian geometry are presented. In this context, the space of H1/2-shapes is defined. The H1/2-shapes are a generalization of smooth shapes and arise naturally in shape optimization problems. Moreover, VI constrained shape optimization problems are treated from an analytical and numerical point of view in order to formulate approaches aiming at semi-smooth Newton methods on shape vector bundles. Shape optimization problems constrained by VIs are very challenging because of the necessity to operate in inherently non-linear and non-convex shape spaces. In classical VIs, there is no explicit dependence on the domain, which adds an unavoidable source of non-linearity and non-convexity due to the non-linear and non-convex nature of shape spaces.
Monday, May 28, 2:00 p.m. (WIAS-ESH)
F. J. Romero Hinrichsen (ETH Zurich)
Dynamical super-resolution with applications to ultrafast ultrasound
Recently there has been a successful development in ultrasound imaging, increasing significantly the sampling rate and therefore enhancing this imaging's capacities. In particular, for vessel imaging, the use of microbubble tracking allows us to super-resolve blood vessels, and by estimating the particles' speeds inside them, it is possible to calculate the vessels' diameters. In this context, we model the microbubble tracking problem, formulating it in terms of a sparse spike recovery problem in the phase space (the position and velocity space), that allows us to obtain simultaneously the speed of the microbubbles and their location. This leads to an L1 minimization algorithm for point source tracking, that promises to be faster than current alternatives.
Wednesday, May 23, 1:00 p.m. (WIAS-ESH)
A. Alphonse (WIAS)
Directional differentiability for elliptic QVIs of obstacle type
Quasi-variational inequalities (QVIs) are generalisations of variational inequalities where the associated constraint set is no longer explicitly given but it depends on the solution itself. In this talk, we present some work on the directional differentiability of the multi-valued mapping that takes the source term of a QVI onto the set of solutions. This result represents a first step in the study of differential sensitivity of QVIs in infinite dimensions. We also discuss an application to thermoforming and show some numerical experiments.
Wednesday, May 16, 1:00 p.m. (WIAS-ESH)
L. Banz (U Salzburg)
A posteriori error estimates for $hp$-dual mixed finite elements by implicit reconstruction
A posteriori error estimates are derived for dual mixed methods of $hp$-adaptive finite elements for variational equations as well as for variational inequalities. The error control relies on the use of a special, but never computed, $H^1$-reconstruction of the non-smooth discrete potential. Thus, no post-processing reconstruction (and therewith no additional computation) is needed. Moreover, the use of the discrete potential instead of its reconstruction improves significantly the error estimation in terms of the numerical efficiency indices which are nearly constant and close to one in the numerical experiments. Numerical experiments demonstrate the convergence rates and the efficiency indices of these a posteriori error estimates in $h$- and $hp$-adaptivity.
Wednesday, April 18, 3:00 p.m. (WIAS-Mo 39, 4th floor)
P. Dvurechensky (WIAS)
First-order optimization methods with inexact information about the objective function value and its gradient
In this talk I will discuss first-order methods with inexact oracle for finite-dimensional optimization. Oracle model of optimization methods assumes that, given a point, the oracle returns some information on the objective function at this point. In the case of first-order optimization methods, this information is the function value and its gradient at this point. I will start with convex problems, inexact oracle defined in the work by O. Devolder, F. Glineur, Yu. Nesterov, Math. Prog., 2014, and convergence rates for gradient descent and accelerated gradient descent in this case. I will also describe an extension for non-convex problems. Then I will discuss some ideas on how these methods potentially can be extended and applied for infinite-dimensional problems. If time allows, I will cover other optimization problems and methods, which I work with. Among others, optimal transport problem and an accelerated gradient descent for its solution, randomized optimization methods, such as random coordinate descent and random derivative-free method, variational inequalities, saddle-point problems and first-order methods for their solution.
Friday, February 2, 2:30 p.m. (WIAS-ESH)
Th. Ruf (U Augsburg)
On a variational approach to the nonlinear wave equation
In a 2012 paper, E. Serra and P. Tilli proved a conjecture by E. de Giorgi stating that global weak solutions of wave equations such as w'' - Delta w + w|w|^(p-2) = 0 on R^+ times R^d can be obtained as limits of minimizers of suitable variational functionals. We generalize this proof to equations of the form w'' - Delta w + f_w (t; x; w) = 0 with p-growth conditions on f, also replacing R^d by an arbitrary open set O subset R^d and suitable boundary conditions.
Friday, January 19, 2:00 p.m. (WIAS-ESH)
S. Bartels (Albert-Ludwigs-Universität Freiburg)
Finite element methods for nonsmooth problems and application to a problem in optimal insulation
Nonsmooth problems arise in the mathematical modeling of contact and obstacle problems, the description of plastic material behavior, and mathematical image processing. The unknown functions are typically characterized as minimizers of nondifferentiable functionals. Numerical schemes approximately solve these problems either via duality methods or classically by making use of appropriate regularizations. In the talk we discuss the discretization and iterative solution of a model problem defined on functions of bounded variation. The numerical analysis of finite element discretizations leads to reduced convergence rates which can be improved using adaptive mesh refinement. Suitable iterative solution procedures are ADMM schemes, for which we propose an automatic step size adjustment strategy, and gradient flows, for which we demonstrate the unconditional stability of a semi-implicit time discretization. The methods are applicable in the numerical determination of optimal insulating films for heat conducting bodies. Below a critical value of available insulation mass an unexpected break of symmetry occurs.
Thursday, January 11, 10:00 a.m. (WIAS-ESH)
T. Kluth (Universität Bremen)
Model-based magnetic particle imaging
Magnetic particle imaging (MPI) is a tracer-based imaging modality developed to detect the concentration of superparamagnetic iron oxide nanoparticles. It is highly sensitive to the nanoparticle's nonlinear response to a dynamic applied magnetic field. Model-based reconstruction techniques are still not able to reach the quality of data-based approaches in which the linear system function is determined by a time-consuming measurement process. Possible reasons include the relaxation behavior of nanoparticles in fast changing magnetic fields. However, the equilibrium model described by the Langevin function is still used to predict the system behavior. In this context we discuss the ill-posedness of the imaging problem. We further focus on the model-based MPI reconstruction problem incorporating deviations in the forward operator. This is illustrated by initial results from real data using a regularized total least squares approach.
Thursday, December 21, 2:15 p.m. (WIAS, HVP11A, 4.13)
Guozhi Dong (HU Berlin)
Regularization methods and nonlinear PDEs for solving inverse and imaging problems
In this talk, I will present some recent developments on regularization methods in image sciences. I shall show a tiny background and also mention some of the state of the art in this area. The focus will be then to discuss some non-convex regularization models for specific problems which suffer from displacement errors. The non-convex energy functionals reveal to have tight connections to some nonlinear (geometric) partial differential equations, e.g. mean curvature flows. Finally, I will show some numerical results with some discussions on the algorithms.
Monday, November 27, 9:30 a.m. (WIAS, HVP11A, 4.13)
Andrea Ceretani (HU Berlin)
Anomalous diffusion with free boundaries
Recently, time fractional Stefan-like problems have been used to model anomalous diffusion with free boundaries and long memory retention. Nevertheless, the physical foundations of this usage is still unclear. We present a model for acid water neutralization with anomalous and fast diffusion. Though this problem presents short memory retention, it is a first step in deriving mathematical models for anomalous diffusion under memory effects based on commonly accepted physical laws. The problem consists in the neutralization of an acid solution in which the hydrogen ions are transported according to Cattaneo's diffusion, and we consider the specific case of a marble slab reacting with a sulphuric acid solution in a one-dimensional geometry. The mathematical problem is reduced to a hyperbolic free boundary problem where the consumption of the slab is described by a nonlinear differential equation. We prove global well-posedness and present some numerical simulations.
Thursday, October 12, 10:30 a.m. (HU Adlershof, room 2.417)
Stephan Schmidt (University of Wuerzburg)
SQP Methods for shape optimization based on weak shape Hessians
Many PDE constrained optimization problems fall into the category of shape optimization, meaning the geometry of the domain is the unknown to be found. Most natural applications are drag minimization in fluid dynamics, but many tomography and image reconstruction problems also fall into this category. The talk introduces shape optimization as a special sub-class of PDE constraint optimization problems. The main focus here will be on generating Newton-like methods for large scale applications. The key for this endeavor is the derivation of the shape Hessian, that is the second directional derivative of a cost functional with respect to geometry changes in a weak form based on material derivatives instead of classical local shape derivatives. To avoid human errors, a computer aided derivation system is also introduced. The methodologies are tested on problem from fluid dynamics and geometric inverse problems.
Wednesday, July 26, 1:15 p.m. (WIAS-ESH)
Hasnaa Zidani (ENSTA ParisTech)
Multi-objective control problems under state constraints
In this talk, we shall present a new approach, based on Hamilton-Jacobi theory, for characterizing the Pareto front for multi-objective optimal control problems in presence of state constraints. We define an auxiliary control problem for an augmented dynamical system and show that the pareto front is a subset of the zero level set of the auxiliary value function. This characterization allows to deduce an efficient numerical procedure for computing the entire Pareto front and the corresponding optimal trajectories. Moreover, the approach allows to consider objective functions of different structures (minimum time cost, Bolza cost and/or infinite horizon objective). A numerical example will be considered to show the relevance of this approach.
Thursday, June 22, 9:30 a.m. (HU Berlin, Adlershof, RUD25, 2.417)
Caroline Löbhard (WIAS)
An adaptive space-time discretization for parabolic optimal control problem with state constraints
We present a space-time discretization which is based on a reformulation of the stationarity conditions of a Moreau-Yosida regularized parabolic optimal control problem, which involves only the state variable. The resulting nonlinear partial differential equation is of fourth order in space, and of second order in time. In order to cope with the disbalance of regularity of the respective solutions, we develop a taylored discontinuous Galerkin scheme and derive convergence rates in the mesh size as well as an integrated update strategy for the regularization parameter related to the state constraints. We also propose an adaptive mesh refinement strategy and illustrate the performance of our method in numerical test cases.
Wednesday, June 21, 3:30 p.m. (WIAS, HVP11A, 4.13)
Mattia Bongini (Barcelona Graduate School of Economics)
Optimal Control Problems in Transport Dynamics
In this talk we shall discuss several results concerning the "indirect control of populations", i.e., how to influence a group of individuals by means of external agents with a directly controlled dynamics. By using the general theory of functionals defined on spaces of measures, we give sufficient conditions for the existence of optimal control strategies and then we present a Pontryagin Maximum Principle for such controls in the form of an Hamiltonian flow in the Wasserstein space of probability measure. Finally, we present an application of the above framework to the evacuation problem of a crowd from an unknown environment with the help of undercover stewards.
Wednesday, Mai 31, 1:15 p.m. (WIAS, HVP11A, 4.13)
Jo Andrea Brüggemann (HU Berlin / WIAS)
Solution methods for a contact model motivated by the human heart's pericardium
Introducing a function space description of the contact within the human heart's pericardium, in this talk, we will study a relaxation of the latter. The quasi-variational inequality (QVI) in the focus of interest is attacked with a fixed-point approach and the hereby arising sequence of variational problems can be efficiently solved with a path-following semi-smooth Newton method.
Wednesday, Mai 24, 1:15 p.m. (WIAS-ESH)
Hongpeng Sun (Renmin University of China)
Weak convergence of proximal ADMM and its relaxations in Hilbert spaces
ADMM (Alternating Direction Method of Multipliers) is a popular first order method for mathematical imaging and inverse problems. However, the weak convergence of ADMM in infinite dimensional spaces is not clear yet, which is different from the classical Augmented Lagrangian Method. We will discuss the weak convergence of ADMM and its proximal variants with relaxations in Hilbert spaces.
Tuesday, Mai 9, 3:30 p.m. (WIAS, HVP11A, 4.13)
Kazufumi Ito (North Carolina State University, USA)
Value function calculus and applications
In this talk the sensitivity analysis is discussed for the parameter-dependent optimization. The sensitivity of the optimal value function with respect to the change in parameters plays a significant role in the optimization theory, including economics, finance, the Hamilton-Jacobi theory, the inf-sup duality and the structural design and the bi-level optimization. We develop the calculus for the value function and present its applications in the variational calculus, the bi-level optimization and the optimal control and optimal design and inverse problems.
Friday, Mai 5, 2:15 p.m. (WIAS-ESH)
Behzad Azmi (Univ. Graz, Austria)
On the stabilizability of infinite dimensional systems via receding horizon control
One efficient strategy for dealing with optimal control problems on an infinite time horizon is the receding horizon framework. In this approach, an infinite horizon optimal control problem is approximated by a sequence of finite horizon problems in a receding horizon fashion. Stability is not generally ensured due to the use of a finite prediction horizon. Thus, in order to ensure the asymptotic stability of the controlled system, additional terminal cost functions and/or terminal constraints are often needed to add to the finite horizon problems. In this presentation, we are concerned with the stabilization of several classes of infinite-dimensional controlled systems by means of a Receding Horizon Control (RHC) scheme. In this scheme, no terminal costs or terminal constraints are used to ensured the stability. The key assumption is the stabilizability of the underlying system. Based on this condition the suboptimality and stability of RHC are investigated. To justify the applicability of this framework, we consider controlled systems governed by different types of partial differential equations. Numerical examples are presented as well.
Thursday, Mai 4, 9:30 a.m. (HU Berlin, Adlershof, RUD25, 2.417)
Axel Kröner (HU Berlin / CMAP, Ecole Polytechnique, Paris-Saclay)
Optimal control of infinite dimensional systems
In this talk we analyze second order optimality conditions for a bilinear optimal control problem governed by a strongly continuous semigroup operator, the control entering linearly in the cost function. We derive first and second order optimality conditions, taking advantage of the Goh transform. The general framework allows the application to heat, wave, and Schr\"odinger equation. This is joint work with S. Aronna und F. Bonnans.
Wednesday, Mar 29, 1:15 p.m. (WIAS-ESH)
Christian Clason (Universität Duisburg-Essen)
Convex relaxation of hybrid discrete-continuous control problems
We consider control problems for partial differential equations where the distributed control should take on values only from a given discrete and hence non-convex set. Such problems occur for example in parameter identification or topology optimization. Similar to their use in sparse optimization, L1-type norms can be used to formulate a convex relaxation which can be solved by semi-smooth Newton methods. We illustrate this approach using linear model problems and discuss the extension to vector-valued and nonlinear control problems.
Tuesday, Feb 7, 10:15 a.m. (WIAS-ESH)
Sara Merino-Aceituno (Imperial College London)
Kinetic theory to study emergent phenomena in biology: an example on swarming
Classical methods in kinetic theory are challenged when studying emergent phenomena in the biological and social sciences. New methodologies are needed to study the problems at hand which typically involve many agents that interact locally. The aim of this talk is to introduce the general framework of kinetic theory and some of the new challenges of applying it to biological systems. We illustrate it in the case of the so-called collective motion of self-propelled particles, like swarming of birds. Particularly, based on the Vicsek model, we study systems of agents (birds) that move at a constant speed while trying to align their body orientation with those of their neighbors. Starting from a particle description, we find the macroscopic dynamics.
Tuesday, Jan 17, 10:15 a.m. (WIAS-ESH)
Tim Sullivan (FU Berlin)
Well-Posedness of Bayesian Inverse Problems - Stable Priors on Quasi-Banach Spaces
The Bayesian perspective on inverse problems has attracted much mathematical attention in recent years, and particular attention has been paid to Bayesian inverse problems (BIPs) in which the parameter to be inferred lies in an infinite-dimensional space, a typical example being a scalar or tensor field coupled to some observed data via an ordinary or partial differential equation. Numerical solution of such infinite-dimensional BIPs must necessarily be performed in an approximate manner on a finite-dimensional subspace, but it is profitable to delay discretisation to the last possible moment and consider the original infinite-dimensional problem as the primary object of study, since infinite-dimensional well-posedness results and algorithms descend to any finite-dimensional subspace in a discretisation-independent way, whereas careless early discretisation may lead to a sequence of well-posed finite-dimensional BIPs or algorithms whose stability properties degenerate as the discretisation dimension increases.
This presentation will give an introduction to the framework of well-posed BIPs in infinite-dimensional parameter spaces, as advocated by Stuart (Acta Numer. 19:451-559, 2010) and others. Recently, this framework has been extended to the case of a heavy-tailed prior measure in the family of stable distributions, such as an infinite-dimensional Cauchy distribution, for which polynomial moments are infinite or undefined. It is shown that analogues of the Karhunen-Loeve expansion for square-integrable random variables can be used to sample such measures on quasi-Banach spaces. Furthermore, under weaker regularity assumptions than those used to date, the Bayesian posterior measure is shown to depend Lipschitz continuously in the Hellinger and total variation metrics upon perturbations of the misfit function and observed data.
Tuesday, Jan 10, 10:15 a.m. (WIAS-ESH)
Andrew Lam (Uni Regensburg)
Diffuse interface models of tumor growth and optimizing cancer treatment times
There has been a recent focus in modeling tumor growth with diffuse interface models, due to their ability to capture topological transitions and the nature of the equations allows for further mathematical treatment. In the first part of this talk I will introduce a class of Cahn-Hilliard systems that are used to capture the basic dynamics of tumor growth. Then, we will discuss an optimal control problem for chemotherapy, which is a cancer treatment using drugs to eliminate tumor cells. Treatments are usually conducted in cycles, and long treatment times may cause harm to the patient. Thus, it is important to optimize both the treatment time and drug dosage to minimize patient suffering. In the second part of this talk, we will analyze an optimal control problem with an objective functional depending on a free time variable, which represents the unknown treatment time to be optimized.
Tuesday, Dec 13, 10:15 a.m. (WIAS-ESH)
Amal Alphonse (WIAS)
Existence for a fractional porous medium equation on an evolving surface
In this talk, which is based on joint work with Prof. Charlie Elliott, I will present an existence theory for a porous medium equation with a fractional diffusion on an evolving surface. The nonlocal nature of the fractional diffusion (which in our case is the square root of the Laplacian) in combination with the nonlinearity and the moving domain makes the problem interesting. After defining the fractional Laplacian and giving a Dirichlet-to-Neumann characterisation of it in a general setting of closed Riemannian manifolds, I will define what we mean by a weak solution and then proceed with the proof of existence. This will involve harmonic extensions on semi-infinite and truncated cylinders, convergence/decay estimates and some technical results in order to deal with the time-evolving surface. I will finish by discussing some ideas for further work.
Tuesday, Dec 6, 10:15 a.m. (WIAS-ESH)
Luca Calatroni (Ecole Polytechnique, Paris)
Infimal convolution of data discrepancies for mixed noise removal in images
In several real-world imaging applications such as microscopy, astronomy and medical imaging, transmission and/or acquisition faults result in a combination of multiple noise statistics in the observed image. Variational data discrepancy models designed to deal with such mixtures linearly combine standard data fidelities used for single-noise removal or make use of either approximated and cheap or exact but computationally expensive log-likelihood functionals. Via a joint MAP estimation, we derive a statistically consistent variational model combining data fidelities associated to single noise distributions in a handy infimal convolution fashion by which individual noise components corrupting the data are modeled appropriately and decomposed from each other. After showing the well-posedness of the model in suitable function spaces, we propose a semi-smooth Newton-type scheme to compute its numerical solution efficiently.
Tuesday, Nov 29, 10:15 a.m. (WIAS, HVP11A 4.13)
Martin Kanitsar (University of Graz)
Numerical shape optimization for an industrial application
For industrial applications in fluid dynamics, finding an optimal shape with respect to some cost functional is important. Moreover, constraints due to inflow and outflow boundaries as well as a respecified construction space need to be taken care of. For the implementation of a gradient descent scheme, the shape sensitivity calculus is used to derive the shape gradient of the cost functional with respect to changes in the shape. The underlying physics are described by the stationary Navier-Stokes equation, the primal equation, and an adjoint equation is used for calculating the shape derivative. Highlighting some details on the numerical realization for a 3D application is the main part of this talk, but results on existence of an optimal shape will be pointed out too. In addition to illustrating interests of the industry, hints are given for appropriate usage of the software OpenFOAM and Star-CD CCM+.
Tuesday, Nov 22, 10:15 a.m. (WIAS-ESH)
Soheil Hajian (WIAS/HU Berlin)
Total variation diminishing RK methods for the optimal control of conservation laws
Optimal control problems subject to conservation laws are among challenging optimization problems due to difficulties that arise when shocks are present in the solution. Such optimization problems have application, for instance, in gas networks. Beside theoretical difficulties at the continuous level, discretization of such optimal control problems should be done with care in order to guarantee convergence. In this talk, we present stability results of the total variation diminishing (TVD) Runge-Kutta (RK) methods for such optimal control problems. In particular we show that enforcing strong stability preserving (SSP) for both forward and adjoint problem results to a first order time-discretization. However, requiring SSP only for forward problem is sufficient to obtain a stable discrete adjoint. We also present order-conditions for the TVD-RK methods in the optimal control context.
Tuesday, Nov 15, 10:15 a.m. (WIAS-ESH)
M. Hintermüller (WIAS/HU Berlin)5
Bilevel optimization with applications to image processing
Friday, Oct 21, 3:00 p.m (WIAS-ESH)
M. Holler (Karl-Franzens-Universität Graz, Austria)
Higher order regularization and applications to medical image processing and data decompression
Variational methods are a powerful tool for tackling ill-posed problems in image processing. As such, they rely heavily on appropriate regularization terms which render a stable recovery possible and strongly influence qualitative solution properties. In this talk, we consider regularization concepts for both static and dynamic data that are based on higher order differentiation. Beginning with the static setting, we first discuss analytical properties of Total Generalized Variation (TGV) regularization which allow for well-posedness results for standard inverse problems. We then consider the application of TGV in the context of a variational model for image decompression, being in particular applicable to JPEG or JPEG 2000 compressed images. As second application, we introduce a nuclear-norm-based vectorial TGV functional for joint MR-PET reconstruction that exploits structural similarities between the two modalities. Moving to the dynamic setting, we motivate and introduce a suitable extension of derivative based regularization for spatio-temporal data. After establishing essential analytical properties, we deal with applications to the reconstruction of highly subsampled dynamic MR data and the decompression of MPEG compressed movies.
Wednesday, Oct 12, 2:00 p.m. (WIAS-HVP11A 4.01)
C. Geldhauser (WIAS)
Scaling limits of interacting diffusions
Abstract: In this talk we consider a system of N coupled stochastic differential equations, which we interpret as a system of N particles evolving according to the dynamics given by the SDEs. Due to the properties of the driving force and the noise, the limit as N goes to infinity does not lead in general to a well-posed equation. We develop conditions on the interaction strength between the particles to ensure existence of solutions to the limiting stochastic PDE. Moreover, we investigate the long-time behaviour of the solution. This is joint work with Anton Bovier.
Carina Geldhauser studied Mathematics, Protestant Theology and Philosophy in Tübingen, Pisa and Paris. She received her Ph.D. from the Institute for Applied Mathematics Universität Bonn. Since September 2016, she is a Postdoc at the Weierstrass Institute.