Add to ORKGThis thesis presents a series of algorithmic innovations in Combinatorial Compressed Sensing and Persistent Homology. The unifying strategy across these contributions is in translating structural patterns in the underlying data into specific algorithmic designs in order to achieve: better guarantees in computational complexity, the ability to operate on more complex data, highly efficient parallelisations, or any combination of these.</p
We describe a parallel algorithm that computes persistent homology, an algebraic descriptor of a fil...
We present a massively parallel algorithm for computing persistent homology, a concept within the fi...
Abstract. We approach the problem of the computation of persistent homology for large datasets by a ...
Combinatorial algorithms have long played an important role in many applications of scientific compu...
Abstract. Combinatorial algorithms have long played a crucial, albeit under-recognized role in scien...
La théorie de l'homologie généralise en dimensions supérieures la notion de connectivité dans les gr...
Persistent homology is a popular and powerful tool for capturing topological features of data. Advan...
The main problem tackled in my thesis was the efficiency of computational topology algorithms. I foc...
We show that recent results on randomized dimension reduction schemes that exploit structural proper...
157 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2001.This thesis highlighted combi...
This thesis is devoted to a range of questions in applied mathematics and signal processing motivate...
Persistent homology allows for tracking topological features, like loops, holes and their higher-dim...
Combinatorial algorithms have long played apivotal enabling role in many applications of parallel co...
Taking images is an efficient way to collect data about the physical world. It can be done fast and ...
We consider the conjectured O(N2+) time complexity of multiplying any two N × N ma-trices A and B. O...
We describe a parallel algorithm that computes persistent homology, an algebraic descriptor of a fil...
We present a massively parallel algorithm for computing persistent homology, a concept within the fi...
Abstract. We approach the problem of the computation of persistent homology for large datasets by a ...
Combinatorial algorithms have long played an important role in many applications of scientific compu...
Abstract. Combinatorial algorithms have long played a crucial, albeit under-recognized role in scien...
La théorie de l'homologie généralise en dimensions supérieures la notion de connectivité dans les gr...
Persistent homology is a popular and powerful tool for capturing topological features of data. Advan...
The main problem tackled in my thesis was the efficiency of computational topology algorithms. I foc...
We show that recent results on randomized dimension reduction schemes that exploit structural proper...
157 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2001.This thesis highlighted combi...
This thesis is devoted to a range of questions in applied mathematics and signal processing motivate...
Persistent homology allows for tracking topological features, like loops, holes and their higher-dim...
Combinatorial algorithms have long played apivotal enabling role in many applications of parallel co...
Taking images is an efficient way to collect data about the physical world. It can be done fast and ...
We consider the conjectured O(N2+) time complexity of multiplying any two N × N ma-trices A and B. O...
We describe a parallel algorithm that computes persistent homology, an algebraic descriptor of a fil...
We present a massively parallel algorithm for computing persistent homology, a concept within the fi...
Abstract. We approach the problem of the computation of persistent homology for large datasets by a ...