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Daniel Lu

Publications and source records attributed to Daniel Lu.

3 recordsLinked to original sources

Dynamic Many Valued Logic Systems in Theoretical Economics

This paper is an original attempt to understand the foundations of economic reasoning. It endeavors to rigorously define the relationship between subjective interpretations and objective valuations of such interpretations in the context of theoretical economics. This analysis is substantially expanded through a dynamic approach, where the truth of a valuation results in an updated interpretation or changes in the agent's subjective belief regarding the effectiveness of the selected action as well as the objective reality of the effectiveness of all other possible actions (i.e. consequence realization). Complications arise when the economic agent is presented with a set of actions that render ambiguous preference, or when the effectiveness of an action cannot be perceived upon its selection, thereby necessitating a different theory of choice and consequence realization.

cs.LO

Planar lower envelope of monotone polygonal chains

A simple linear search algorithm running in $O(n+mk)$ time is proposed for constructing the lower envelope of $k$ vertices from $m$ monotone polygonal chains in 2D with $n$ vertices in total. This can be applied to output-sensitive construction of lower envelopes for arbitrary line segments in optimal $O(n\log k)$ time, where $k$ is the output size. Compared to existing output-sensitive algorithms for lower envelopes, this is simpler to implement, does not require complex data structures, and is a constant factor faster.

cs.CG

A fast 25/6-approximation for the minimum unit disk cover problem

Given a point set P in 2D, the problem of finding the smallest set of unit disks that cover all of P is NP-hard. We present a simple algorithm for this problem with an approximation factor of 25/6 in the Euclidean norm and 2 in the max norm, by restricting the disk centers to lie on parallel lines. The run time and space of this algorithm is O(n log n) and O(n) respectively. This algorithm extends to any Lp norm and is asymptotically faster than known alternative approximation algorithms for the same approximation factor.

cs.CG