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We propose a recursive algorithm for estimating time-varying signals from a few linear measurements. The signals are assumed sparse, with unknown support, and are described by a dynamical model. In each iteration, the algorithm solves an ℓ1-ℓ1 minimization ...
The integration of weather forecasts and demand prediction into the energy management system of buildings is usually achieved using a model-based predictive control. The performance of such control techniques strongly depends on the accuracy of the thermal ...
Motivation: Large-scale evolutionary events such as genomic rearrange. ments and segmental duplications form an important part of the evolution of genomes and are widely studied from both biological and computational perspectives. A basic computational pro ...
In this paper we present some heuristic strategies to compute rapid and reliable approximations to stability factors in nonlinear, inf-sup stable parametrized PDEs. The efficient evaluation of these quantities is crucial for the rapid construction of a pos ...
Like other data sensing problems, in unlabeled sensing, the target is to solve the equation y = Φx by finding vector x given a set of sample values in vector y as well as the matrix Φ. However, the main challenge in unlabeled sensing is that the correct or ...
A sliding window top-k (top-k/w) query monitors incoming data stream objects within a sliding window of size w to identify the k highest-ranked objects with respect to a given scoring function over time. Processing of such queries is challenging because, e ...
We propose a new variational inference method based on a proximal framework that uses the Kullback-Leibler (KL) divergence as the proximal term. We make two contributions towards exploiting the geometry and structure of the variational bound. Firstly, we p ...
Advancements in CMOS technology have enabled exponential growth of computational power. However, data processing efficiency also relies on sufficient data communication bandwidth between different units of a computing system. Memory systems typically apply ...
In this work we investigate the advantages of multiscale methods in Petrov-Galerkin (PG) formulation in a general framework. The framework is based on a localized orthogonal decomposition of a high dimensional solution space into a low dimensional multisca ...
In this paper, we propose a model order reduction framework for parametrized quadratic optimization problems constrained by nonlinear stationary PDEs. Once the solutions of the optimization problem are characterized as the solutions of the corresponding op ...