Sparse Spikes Deconvolution on Thin Grids

This article analyzes the recovery performance of two popular finite dimensional approximations of the sparse spikes deconvolution problem over Radon measures. We examine in a unified framework both the L1 regularization (often referred to as Lasso or Basis-Pursuit) and the Continuous Basis-Pursuit (C-BP) methods. The Lasso is the de-facto standard for the sparse regularization of inverse problems in imaging. It performs a nearest neighbor interpolation of the spikes locations on the sampling grid. The C-BP method, introduced by Ekanadham, Tranchina and Simoncelli, uses a linear interpolation of the locations to perform a better approximation of the infinite-dimensional optimization problem, for positive measures. We show that, in the small noise regime, both methods estimate twice the number of spikes as the number of original spikes. Indeed, we show that they both detect two neighboring spikes around the locations of an original spikes. These results for deconvolution problems are based on an abstract analysis of the so-called extended support of the solutions of L1-type problems (including as special cases the Lasso and C-BP for deconvolution), which are of an independent interest. They precisely characterize the support of the solutions when the noise is small and the regularization parameter is selected accordingly. We illustrate these findings to analyze for the first time the support instability of compressed sensing recovery when the number of measurements is below the critical limit (well documented in the literature) where the support is provably stable

Optimal growth for linear processes with affine control

We analyse an optimal control with the following features: the dynamical system is linear, and the dependence upon the control parameter is affine. More precisely we consider $\dot x_\alpha(t) = (G + \alpha(t) F)x_\alpha(t)$, where $G$ and $F$ are $3\times 3$ matrices with some prescribed structure. In the case of constant control $\alpha(t)\equiv \alpha$, we show the existence of an optimal Perron eigenvalue with respect to varying $\alpha$ under some assumptions. Next we investigate the Floquet eigenvalue problem associated to time-periodic controls $\alpha(t)$. Finally we prove the existence of an eigenvalue (in the generalized sense) for the optimal control problem. The proof is based on the results by [Arisawa 1998, Ann. Institut Henri Poincar\'e] concerning the ergodic problem for Hamilton-Jacobi equations. We discuss the relations between the three eigenvalues. Surprisingly enough, the three eigenvalues appear to be numerically the same

Non-linear eigenvalue problems arising from growth maximization of positive linear dynamical systems

We study a growth maximization problem for a continuous time positive linear system with switches. This is motivated by a problem of mathematical biology (modeling growth-fragmentation processes and the PMCA protocol). We show that the growth rate is determined by the non-linear eigenvalue of a max-plus analogue of the Ruelle-Perron-Frobenius operator, or equivalently, by the ergodic constant of a Hamilton-Jacobi (HJ) partial differential equation, the solutions or subsolutions of which yield Barabanov and extremal norms, respectively. We exploit contraction properties of order preserving flows, with respect to Hilbert's projective metric, to show that the non-linear eigenvector of the operator, or the "weak KAM" solution of the HJ equation, does exist. Low dimensional examples are presented, showing that the optimal control can lead to a limit cycle.Comment: 8 page

Convergence of Entropic Schemes for Optimal Transport and Gradient Flows

Replacing positivity constraints by an entropy barrier is popular to approximate solutions of linear programs. In the special case of the optimal transport problem, this technique dates back to the early work of Schr\"odinger. This approach has recently been used successfully to solve optimal transport related problems in several applied fields such as imaging sciences, machine learning and social sciences. The main reason for this success is that, in contrast to linear programming solvers, the resulting algorithms are highly parallelizable and take advantage of the geometry of the computational grid (e.g. an image or a triangulated mesh). The first contribution of this article is the proof of the $\Gamma$-convergence of the entropic regularized optimal transport problem towards the Monge-Kantorovich problem for the squared Euclidean norm cost function. This implies in particular the convergence of the optimal entropic regularized transport plan towards an optimal transport plan as the entropy vanishes. Optimal transport distances are also useful to define gradient flows as a limit of implicit Euler steps according to the transportation distance. Our second contribution is a proof that implicit steps according to the entropic regularized distance converge towards the original gradient flow when both the step size and the entropic penalty vanish (in some controlled way)

A non-conservative Harris ergodic theorem

We consider non-conservative positive semigroups and obtain necessary and sufficient conditions for uniform exponential contraction in weighted total variation norm. This ensures the existence of Perron eigenelements and provides quantitative estimates of the spectral gap, complementing Krein-Rutman theorems and generalizing probabilistic approaches. The proof is based on a non-homogenous $h$-transform of the semigroup and the construction of Lyapunov functions for this latter. It exploits then the classical necessary and sufficient conditions of Harris's theorem for conservative semigroups and recent techniques developed for the study of absorbed Markov processes. We apply these results to population dynamics. We obtain exponential convergence of birth and death processes conditioned on survival to their quasi-stationary distribution, as well as estimates on exponential relaxation to stationary profiles in growth-fragmentation PDEs

The police in different voices: Isaac Newton and his programme of purification.

This work positions Isaac Newton's three areas of inquiry---Natural Philosophy, alchemy, and theology---as three inter-locked "literacies, " each with its own corrupt text and purifying method of reading. Newton's natural philosophical literacy, a method of purifying reading the book of nature, is driven by coded concepts, including crypticity, Oneness, and purification, drawn from Newton's heretical Christianity. Those concepts also drive his interactions with the Royal Society and his contemporary Enlightenment scientists. Newton's alchemical literacy, a transmutative method of reading the book of self, is expressive of both Newton's will to superiority and his ambivalent and complex placement of the female in his system of representation. Newton's theological literacy, a purifying method of reading scriptures, employs a hermeneutics using criteria of Enlightenment science to purge scripture of idolatrous complexity. That theological literacy Newton extends to the world of politics in his work at the London mint, where he purifies the mint of inefficiency and the underworld of counterfeiters. Newton's overall method of working in seemingly opposed systems of representation is juxtaposed to Niels Bohr's "Unity of Knowledge, " with both demonstrating a Kierkegaardian "dance of the absurd" in their productive use of contradiction. However, Bohr's complementarity accounts for and goes beyond the limits of Newton's approach. Employing Bohr's complementarity as meta-epistemological frame, Walter Benjamin's method of constellation, Werner Heisenberg's uncertainty principle, and Kurt Godel's incompleteness theorem are positioned as three post Enlightenment responses to Newton's characteristics of science outlined in his "Rules of Reasoning." Mutually exclusive yet interdependent, these epistemological complementarities are framed as possibilities for construction of a human(e) science

A Generalized Nash-Cournot Model for the North-Western European Natural Gas Markets with a Fuel SubstitutionDemand Function: The GaMMES Model

This article presents a dynamic Generalized Nash-Cournot model to describe the evolution of the natural gas markets. The aim of this work is to provide a theoretical framework that would allow us to analyze future infrastructure and policy developments, while trying to answer some of the main criticisms addressed to Cournot-based models of natural gas markets. The major gas chain players are depicted including: producers, consumers, storage and pipeline operators, as well as intermediate local traders. Our economic structure description takes into account market power and the demand representation tries to capture the possible fuel substitution that can be made between the consumption of oil, coal and natural gas in the overall fossil energy consumption. We also take into account the long-term aspects inherent to some markets, in an endogenous way. This particularity of our description makes the model a Generalized Nash Equilibrium problem that needs to be solved using specialized mathematical techniques. Our model has been applied to represent the European natural gas market and forecast, until 2030, after a calibration process, consumption, prices, production and natural gas dependence. A comparison between our model, a more standard one that does not take into account energy substitution, and the European Commission natural gas forecasts is carried out to analyze our results. Finally, in order to illustrate the possible use of fuel substitution, we studied the evolution of the natural gas price as compared to the coal and oil prices. This paper mostly focuses on the model description.Energy markets modeling, Game theory, Generalized Nash-Cournot equilibria, Quasi-Variational Inequality