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\footnotesize \sf
Computer Science and Engineering
\\
Ferguson Hall -- 0115
\\
472-2402
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\LARGE \bf
Course Specification
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{\Large \bf \begin{center} CSCE-423/823\\
Design and Analysis of Algorithms\\
\end{center} }

\paragraph{Catalogue Description:}~ \newline
423/823. Design and Analysis of Algorithms (3 cr) Prereq: CSCE 310
Mathematical preliminaries.  Strategies for algorithm design,
including divide-and-conquer, greedy, dynamic programming and
backtracking.  Mathematical analysis of algorithms.  Introduction to
NP-Completeness theory, including the classes P and NP, polynomial
transformations and NP-complete problems.

\paragraph{Textbook(s) and/or Other Required Materials:}~ \newline
Cormen, Leiserson, and Rivest, {\em Introduction to Algorithms}. \newline
Karloff, {\em Linear Programming}. \newline
Handouts on adversary lower bounds.


\paragraph{Prerequisites by Topic:}
\begin{itemize}
\item {\bf Mastery} of algorithmic approaches including greedy method
and divide-and-conquer.
A variety of common algorithms for searching and sorting,
hashing, heaps, DFS and BFS on trees and graphs.

\item {\bf Familiarity} with theoretic and
empirical complexity analysis, including solving summations and recurrences.
Problems on graphs.  
Algorithm correctness. 

\item {\bf Exposure} to dynamic programming, randomization (in algorithms and
data structures), NP Completeness, decidability.

\end{itemize}


\paragraph{Course Objectives:}
\begin{itemize}
\item {\bf Mastery} of algorithm design and analysis techniques
dynamic programming and amortized analysis.  Selecting $k$th smallest element
from an unsorted list in linear worst-case time.
Linear programming: geometric view, simplex method,
two-phase method, duality theory.  Network flow.

\item {\bf Familiarity} with adversary lower bound techniques,
NP-completeness, and approximation algorithms.

\item {\bf Exposure}
to miscellaneous topics such as parallel algorithms,
on-line algorithms, computational geometry, randomized algorithms.

\end{itemize}

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{\small ~ \hfill  \today}
\newpage
\paragraph{Topics Covered:}
\begin{enumerate} % {

\item Optimization problems: review of greedy approach (greedy choice and
optimal substructure properties).

\item Dynamic programming (optimal substructure property).

\item Amortized analysis.

\item Adversary lower bounds: finding minimum/maximum, median finding.

\item Worst-case linear-time finding of $k$th smallest of an unordered list.

\item Linear programming: geometric view, simplex method,
two-phase method, duality theory.

\item NP-completeness: definition of NP, NP-completeness, and reducibility.
Example Reductions.

\item Approximation algorithms: ratio bounds, vertex cover, set cover, TSP with
triangle inequality, inapproximability of general TSP. Poly-time approximation
schemes and fully poly-time approximation schemes.

\item Network flow.

\item Introduce miscellaneous topics as time permits: parallel algorithms,
on-line algorithms, computational geometry, randomized algorithms.

\end{enumerate} % }

\paragraph{Relationship of Course to Program Objectives:}~ \newline
\underline{{\bf Ignore for Fall 1998}. Details will 
be firmed up during Spring 1998 Semester.}


\paragraph{Contribution of Course to Meeting the Professional Component:}~ \newline
\underline{{\bf Ignore for Fall 1998}. Details will 
be firmed up during Spring 1998 Semester.}


\paragraph{Class/Laboratory Schedule:}~ \newline
Lecture: 45 hours = 3 hours/week for 15 weeks


\paragraph{Prepared by:}~ \newline
Stephen D.~Scott, Assistant Professor

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