Maximum subarray problem: Difference between revisions

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Genomic sequence analysis employs maximum subarray algorithms to identify important biological segments of protein sequences.{{citation needed|date=March 2020}} These problems include conserved segments, GC-rich regions, tandem repeats, low-complexity filter, DNA binding domains, and regions of high charge.{{citation needed|date=October 2017}}

In [[computer vision]], bitmap images generally consist only of positive values, for which the maximum subarray problem is trivial: the result is always the whole array. However, after subtracting a threshold value (such as the average pixel value) from each pixel, so that above-average pixels will be positive and below-average pixels will be negative, the maximum subarray problem can be applied to the modified image to detect bright areas within it.<ref>{{harvtxt|Bae|Takaoka|2006}}; {{harvtxt|Weddell|Read|Thaher|Takaoka|2013}}</ref>

In [[computer vision]], maximum-subarray algorithms are used on bitmap images to detect the brightest area in an image.

==Kadane's algorithm==

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| s2cid=2681670

}}.

*{{citation

| last1 = Bae | first1 = Sung Eun

| last2 = Takaoka | first2 = Tadao

| doi = 10.1093/COMJNL/BXL007

| issue = 3

| journal = The Computer Journal

| pages = 358–374

| title = Improved Algorithms for the \emph{K}-Maximum Subarray Problem

| volume = 49

| year = 2006}}

*{{citation

| last1=Bengtsson

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| access-date = November 17, 2018

}}

*{{citation

| last1 = Weddell | first1 = Stephen John

| last2 = Read | first2 = Tristan

| last3 = Thaher | first3 = Mohammed

| last4 = Takaoka | first4 = Tadao

| doi = 10.1117/1.JEI.22.4.043011

| issue = 4

| journal = Journal of Electronic Imaging

| page = 043011

| title = Maximum subarray algorithms for use in astronomical imaging

| volume = 22

| year = 2013}}

== External links ==