Rice University Phd Thesis

Rice University Phd Thesis-81
If the initial set of constraints is expressed as Disjunctive Normal Form (DNF), Approx MC is the only known Fully Polynomial Randomized Approximation Scheme (FPRAS) that does not involve Monte Carlo steps.By exploiting the connection between definability of formulas and variance of the distribution of solutions in a cell defined by 3-universal hash functions, we introduced an algorithmic technique, MIS, that reduced the size of XOR constraints employed in the underlying universal hash functions by as much as two orders of magnitude.

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In constrained counting, the task is to compute the total weight, subject to a given weighting function, of the set of solutions of the given constraints.

In constrained sampling, the task is to sample randomly, subject to a given weighting function, from the set of solutions to a set of given constraints.

Colleagues at the CRF have sorely missed Kevin, whom they admired for his scientific brilliance and treasured for his kind heart.

At the time of his death, the CRF’s Dave Chandler said, “He was one of the best and brightest—just a sponge for knowledge.

Duhalt has spoken about energy issues at various conferences and events in the U. Prior to joining UDLAP in 2013, he completed his Ph. For his conceptual and empirical contributions, he received the Best Ph. Thesis Award from the Economic Geography Specialty Group of the Association of American Geographers in 2012.

Constrained counting and sampling are two fundamental problems in Computer Science with numerous applications, including network reliability, privacy, probabilistic reasoning, and constrained-random verification.

At his core, Kevin wanted to understand the world and make a difference. People gravitated toward him because of his talents and approachability.

Ariel Fernandez, who was an Endowed Chair Professor in Bioengineering at Rice University at the time He Jiankui was a graduate student.

In this thesis, we introduce a novel hashing-based algorithmic framework for constrained sampling and counting that combines the classical algorithmic technique of universal hashing with the dramatic progress made in combinatorial reasoning tools, in particular, SAT and SMT, over the past two decades.

The resulting frameworks for counting (Approx MC2) and sampling (Uni Gen) can handle formulas with up to million variables representing a significant boost up from the prior state of the art tools' capability to handle few hundreds of variables.

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