615.05 - Sorting Sorting Algorithms Biostatistics 615/815...

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Sorting Algorithms Biostatistics 615/815 ecture 5 Lecture 5
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815 Projects z 33% of your overall grade z Hand-out details choice of 6 projects MCMC evaluation of contingency table p-values Rapid fitting of logistic regression models Classify texts according to word distribution Search for similar phrases in two texts Fit a multivariate normal mixture distribution lign short sequence reads Align short sequence reads
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815 Projects – Next Step z Rank project options z E-mail me your choices by Friday y address: oncalo@umich edu My address: goncalo@umich.edu Subject: 815 Project z Projects should be completed in pairs you have a partner preference let me know! If you have a partner preference, let me know!
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Last Lecture … z Recursive Functions Natural expression for many algorithms z Dynamic Programming Automatic strategy for generating efficient versions of recursive algorithms
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Today … z Properties of Sorting Algorithms z Elementary Sorting Algorithms election Sort Selection Sort Insertion Sort ubble Sort Bubble Sort
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Applications of Sorting z Facilitate searching Building indices z Identify quantiles of a distribution z Identify unique values z Browsing data
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Elementary Methods z Suitable for Small datasets Specialized applications z Prelude to more complex methods Illustrate ideas Introduce terminology Sometimes useful complement
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... but beware! z Elementary sorts are very inefficient Typically, time requirements are O(N 2 ) z Probably, most common inefficiency in scientific computing Make programs “break” with large datasets
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Aim z Rearrange a set of keys Using some predefined order Integers Doubles Indices for records in a database z Keys stored as array in memory More complex sorts when we can only load part of the data
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Basic Building Blocks z An type for each element #define Item int z Compare two elements z Exchange two elements z Compare and exchange two elements
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615.05 - Sorting Sorting Algorithms Biostatistics 615/815...

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