Thèse de doctorat : Università della Svizzera italiana, 2021 ; 2021INFO008.
Fundamental tasks in multivariate and numerical analysis, such as sparse precision matrix estimation via graphical lasso and function approximation, are formulated in ever-increasing dimensions. Consequently, this results in a significant increase in the computational demand that quickly renders standard solution methods intractable. With this motivation, we present two scalable algorithms that...
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Thèse de doctorat : Università della Svizzera italiana, 2021 ; 2021INFO006.
Solving large sparse linear systems is at the heart of many application problems arising from scientific and engineering problems. These systems are often solved by direct factorization solvers, especially when the system needs to be solved for multiple right-hand sides or when a high numerical precision is required. Direct solvers are based on matrix factorization, which is then followed by...
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Thèse de doctorat : Università della Svizzera italiana, 2020 ; 2020INFO003.
A software library for the solution of large-scale structured nonconvex optimization problems is presented in this work, with the purpose of accelerating the solution on single- core, multicore, or massively parallel high-performance distributed memory computing infrastructures. A large class of industrial and engineering problems possesses a particular structure, motivating the development of...
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Thèse de doctorat : Università della Svizzera italiana, 2019 ; 2019INFO002.
The explicit evaluation of selected entries of the inverse of a given sparse matrix is an important process in various application fields and is gaining visibility in recent years. While a standard inversion process would require the computation of the whole inverse who is, in general, a dense matrix, state-of-the-art solvers perform a selected inversion process instead. Such approach allows...
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Thèse de doctorat : Università della Svizzera italiana, 2015 ; 2015INFO006.
Modeling problems that require the simulation of hyperbolic PDEs (wave equations) on large heterogeneous domains have potentially many bottlenecks. We attack this problem through two techniques: the massively parallel capabilities of graphics processors (GPUs) and local time stepping (LTS) to mitigate any CFL bottlenecks on a multiscale mesh. Many modern supercomputing centers are installing...
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