Now showing items 71-77 of 77
Worst-case robot navigation in deterministic environments
(Georgia Institute of Technology, 2009-12-02)
We design and analyze algorithms for the following two robot navigation problems: 1. TARGET SEARCH. Given a robot located at a point s in the plane, how will a robot navigate to a goal t in the presence of unknown obstacles ...
Performance understanding and tuning of iterative computation using profiling techniques
(Georgia Institute of Technology, 2010-05-18)
Most applications spend a significant amount of time in the iterative parts of a computation. They typically iterate over the same set of operations with different values. These values either depend on inputs or values ...
Turing machine algorithms and studies in quasi-randomness
(Georgia Institute of Technology, 2011-11-09)
Randomness is an invaluable resource in theoretical computer science. However, pure random bits are hard to obtain. Quasi-randomness is a tool that has been widely used in eliminating/reducing the randomness from randomized ...
Level of detail management
(Georgia Institute of Technology, 1997-08)
Generalized N-body problems: a framework for scalable computation
(Georgia Institute of Technology, 2013-08-26)
In the wake of the Big Data phenomenon, the computing world has seen a number of computational paradigms developed in response to the sudden need to process ever-increasing volumes of data. Most notably, MapReduce has ...
Enhance the understanding of whole-genome evolution by designing, accelerating and parallelizing phylogenetic algorithms
(Georgia Institute of Technology, 2014-04-07)
The advent of new technology enhance the speed and reduce the cost for sequencing biological data. Making biological sense of this genomic data is a big challenge to the algorithm design as well as the high performance ...
New formulations for active learning
(Georgia Institute of Technology, 2014-01-10)
In this thesis, we provide computationally efficient algorithms with provable statistical guarantees, for the problem of active learning, by using ideas from sequential analysis. We provide a generic algorithmic framework ...