Bayesian Optimization (BO) methods are often used to optimize an unknown function f(•) that is costly to evaluate. They typically work in an iterative manner. In each iteration, given a set of observation points, BO algorithms select k ≥ 1 points to be evaluated. The results of those points are...
A simplification of the proof of the maximum principle of
Pontryagin is obtained for constrained and unconstrained optimal control
problems. Two numerical methods for solving optimal control problems
with guaranteed error bounds using the maximum principle of Pontryagin
and interval analysis are derived.
Easy-first, a search-based structured prediction approach, has been applied to many NLP tasks including dependency parsing and coreference resolution. This approach employs a learned greedy policy (action scoring function) to make easy decisions first, which constrains the remaining decisions and makes them easier. This thesis studies the problem of learning...
We investigate the optimal harvesting strategies for McKendrick type population models. Models of this type endow the population with a continuous age structure. They consist of a partial differential equation with a boundary condition which involves an integral of the solution. We study two problems, the first concerns the yield...
The large wind resource off the United States West Coast has the potential to generate wind power for millions of homes, yet the high cost of energy for offshore wind power compared to traditional sources has slowed the development of offshore wind farms in the US. The four studies presented...
In this research, a bi-criteria group scheduling problem is investigated in hybrid flow shop (HFS) environments, where the parallel machines in each stage are unrelated, meaning not identical. The objective of the problem is to minimize a linear combination of the total weighted completion times as a means of complying...
Many important application problems in engineering can be formalized as nonlinear
optimization tasks. However, numerical methods for solving such problems
are brittle and do not scale well. For example, these methods depend critically
on choosing a good starting point from which to perform the optimization search.
In high-dimensional spaces, numerical...
With the need to integrate renewable energy sources into the current energy portfolio and the proximity of many population centers to an ocean coastline, it is pressing that marine energy systems, specifically wave energy converters (WECs), are evaluated as potential solutions for meeting energy needs. In order to best understand...
Cooperative multiagent systems are used as solution concepts in many application domains including air traffic control, satellite communications, and extra planetary exploration. As systems become more distributed and complex, we observe three phenomena. First, these systems cannot be accurately modeled, rendering traditional model based control methods inadequate. Second, system parameters...
Simulated Annealing and Threshold Accepting are two stochastic search algorithms that have been successfully used on a variety of complex and difficult problem sets. Due to their stochastic nature they are not guaranteed to produce the same result for each run. This means that these techniques actually produce a distribution...
How can an agent generalize its knowledge to new circumstances? To learn
effectively an agent acting in a sequential decision problem must make intelligent action selection choices based on its available knowledge. This dissertation focuses on Bayesian methods of representing learned knowledge and develops novel algorithms that exploit the represented...
Many different types of distributed batch scheduling systems have been developed in the last decade to take advantage of the decentralization of computers and the enormous investments that many companies and educational institutions have in desktop workstations. Based on the premise that the majority of desktop workstations are significantly underutilized,...
In this dissertation we develop mathematical treatment for two important applications: (i) evolution of methane in coalbeds with the associated phenomena of adsorption, and (ii) formation of methane hydrates in seabed. We use simplified models for (i) and (ii) since we are more interested in qualitative properties of the solutions...
Maintaining the sustainability of the earth’s ecosystems has attracted much attention as these ecosystems are facing more and more pressure from human activities. Machine learning can play an important role in promoting sustainability as a large amount of data is being collected from ecosystems. There are at least three important...
A significant number of historically existing wetlands that naturally stored rainwater and attenuated flood peaks have now been drained and employed as new farming areas. Beyond the water quality and flow problem, this has resulted in loss of natural habitats of diverse ecological species. Restoring wetlands have hence been proposed...
Optimization of reservoir operation is involves various competing objectives for a scarce resource (water). To find the optimal operation of reservoirs, it is essential to consider multiple objectives simultaneously. There are various sources of uncertainty associated with the reservoir operation problem that should be considered as well.
The overarching goal...
Markov Decision Processes (MDPs) are the de-facto formalism for studying sequential decision making problems with uncertainty, ranging from classical problems such as inventory control and path planning, to more complex problems such as reservoir control under rainfall uncertainty and emergency response optimization for fire and medical emergencies. Most prior research...
We investigate a search and coverage planning problem, where an area of interest has to be explored by a number of vehicles, given a fixed time budget. A good coverage plan has a low probability of a target remaining unobserved. We introduce a formal problem statement, suggest a greedy algorithm...