A characterization of nonemptiness and boundedness of the solution set for set-valued vector equilibrium problems via scalarization and stability results

International audienceAttitude is a key concept in social psychology. The paper presents a novel agent-based model to simulate attitude formation by combining a rational and an emotional components based on cognitive, psychological and social theories. Individuals of the artificial population perceive actions taken by actors such as government or brands, they form an attitude toward them and also communicate the events through a social network. The model outputs are first studied through a functional analysis in which some unique macroscopic behaviors have emerged such as the impact of social groups, the resistance of the population toward disinformation campaigns or the social pressure. We then applied our model on a real world scenario depicting the effort of French Forces in their stabilization operations in Kapisa (Afghanistan) between 2010 and 2012. We calibrated the model parameters based on this scenario and the results of opinion polls that were conducted in the area during the same period about the sentiment of the population toward the Forces. Our model was able to reproduce polls results with a global error under 3%. Based on these results, we show the different dynamics tendencies that emerged among the population by applying a non-supervised classification algorithm

On Hölder calmness of solution mappings in parametric equilibrium problems

We consider parametric equilibrium problems in metric spaces. Sufficient conditions for the Hölder calmness of solutions are established. We also study the Hölder well-posedness for equilibrium problems in metric spaces

Advances in Optimization and Nonlinear Analysis

The present book focuses on that part of calculus of variations, optimization, nonlinear analysis and related applications which combines tools and methods from partial differential equations with geometrical techniques. More precisely, this work is devoted to nonlinear problems coming from different areas, with particular reference to those introducing new techniques capable of solving a wide range of problems. The book is a valuable guide for researchers, engineers and students in the field of mathematics, operations research, optimal control science, artificial intelligence, management science and economics

Methods for Optimization and Regularization of Generative Models

This thesis studies the problem of regularizing and optimizing generative models, often using insights and techniques from kernel methods. The work proceeds in three main themes. Conditional score estimation. We propose a method for estimating conditional densities based on a rich class of RKHS exponential family models. The algorithm works by solving a convex quadratic problem for fitting the gradient of the log density, the score, thus avoiding the need for estimating the normalizing constant. We show the resulting estimator to be consistent and provide convergence rates when the model is well-specified. Structuring and regularizing implicit generative models. In a first contribution, we introduce a method for learning Generative Adversarial Networks, a class of Implicit Generative Models, using a parametric family of Maximum Mean Discrepancies (MMD). We show that controlling the gradient of the critic function defining the MMD is vital for having a sensible loss function. Moreover, we devise a method to enforce exact, analytical gradient constraints. As a second contribution, we introduce and study a new generative model suited for data with low intrinsic dimension embedded in a high dimensional space. This model combines two components: an implicit model, which can learn the low-dimensional support of data, and an energy function, to refine the probability mass by importance sampling on the support of the implicit model. We further introduce algorithms for learning such a hybrid model and for efficient sampling. Optimizing implicit generative models. We first study the Wasserstein gradient flow of the Maximum Mean Discrepancy in a non-parametric setting and provide smoothness conditions on the trajectory of the flow to ensure global convergence. We identify cases when this condition does not hold and propose a new algorithm based on noise injection to mitigate this problem. In a second contribution, we consider the Wasserstein gradient flow of generic loss functionals in a parametric setting. This flow is invariant to the model's parameterization, just like the Fisher gradient flows in information geometry. It has the additional benefit to be well defined even for models with varying supports, which is particularly well suited for implicit generative models. We then introduce a general framework for approximating the Wasserstein natural gradient by leveraging a dual formulation of the Wasserstein pseudo-Riemannian metric that we restrict to a Reproducing Kernel Hilbert Space. The resulting estimator is scalable and provably consistent as it relies on Nystrom methods

Symmetry in the Mathematical Inequalities

This Special Issue brings together original research papers, in all areas of mathematics, that are concerned with inequalities or the role of inequalities. The research results presented in this Special Issue are related to improvements in classical inequalities, highlighting their applications and promoting an exchange of ideas between mathematicians from many parts of the world dedicated to the theory of inequalities. This volume will be of interest to mathematicians specializing in inequality theory and beyond. Many of the studies presented here can be very useful in demonstrating new results. It is our great pleasure to publish this book. All contents were peer-reviewed by multiple referees and published as papers in our Special Issue in the journal Symmetry. These studies give new and interesting results in mathematical inequalities enabling readers to obtain the latest developments in the fields of mathematical inequalities. Finally, we would like to thank all the authors who have published their valuable work in this Special Issue. We would also like to thank the editors of the journal Symmetry for their help in making this volume, especially Mrs. Teresa Yu

Control in moving interfaces and deep learning

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Matemáticas. Fecha de Lectura: 14-05-2021This thesis has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No.765579-ConFlex