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This thesis is divided into two parts sharing the common theme of fully polynomial time approximation schemes. In the first part, we introduce a generic approach for devising fully polynomial time approximation schemes for ...

Many strategies for asset allocation involve the computation of expected returns and the covariance or correlation matrix of financial instruments returns. How much of each instrument to own is determined by an attempt to ...

We consider in this thesis the problem of pricing American Put Options in a model-free framework where we do not make any assumptions about the price dynamics of the underlying except those implied by the no-arbitrage ...

Recent decades have seen the development of more advanced sensor and communication systems, with the future certainly holding more innovation in these areas. However, current operations involve "stovepipe" systems in which ...

Combat outposts (COPs) are small, well-protected bases from which soldiers reside and conduct operations from. Used extensively during the "Surge" in Iraq, COPs are usually established in populated areas and are prevalent ...

This thesis considers the tactical reconnaissance asset allocation problem in military operations. Specifically this thesis presents methods to optimize, under uncertain conditions, tactical reconnaissance asset allocation ...

In this thesis, we examine some puzzles in finance from an evolutionary perspective. We first provide a literature review of evolutionary psychology, and discuss three main findings; the frequentist hypothesis, applications ...

The growth of Low Fare Carriers (LFCs) has encouraged many airlines to remove fare restrictions (such as advance purchase requirements and Saturday-night stays) on many of their fare class products, leading to the ...

Two principles at the forefront of modern machine learning and statistics are sparse modeling and robustness. Sparse modeling enables the construction of simpler statistical models, with examples including the Lasso and ...

For the past 30 years, decision tree methods have been one of the most widely-used approaches in machine learning across industry and academia, due in large part to their interpretability. However, this interpretability ...