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In [[computer science]], the '''maximum subarray problem''' is the task of finding the contiguous subarray within a one-dimensional [[array data structure|array]] of numbers (containing at least one positive number) which has the largest sum. For example, for the sequence of values &minus;2, 1, &minus;3, 4, &minus;1, 2, 1, &minus;5, 4; the contiguous subarray with the largest sum is 4, &minus;1, 2, 1, with sum 6.
 
The problem was first posed by [[Ulf Grenander]] of [[Brown University]] in 1977, as a simplified model for [[maximum likelihood]] estimation of patterns in digitized images. A [[linear time]] [[algorithm]] was found soon afterwards by [[Jay Kadane]] of [[Carnegie-Mellon University]] {{harv|Bentley|1984}}.
 
==Kadane's algorithm==
Kadane's algorithm consists of a scan through the array values, computing at each position the maximum subarray ending at that position. This subarray is either empty (in which case [[empty sum|its sum is zero]]) or consists of one more element than the maximum subarray ending at the previous position. Thus, the problem can be solved with the following code, expressed here in [[Python (programming language)|Python]]:
 
<source lang="python">
def max_subarray(A):
    max_ending_here = max_so_far = 0
    for x in A:
        max_ending_here = max(0, max_ending_here + x)
        max_so_far = max(max_so_far, max_ending_here)
    return max_so_far
</source>
 
A variation of the problem that does not allow zero-length subarrays to be returned in the case that the entire array consists of negative numbers can be solved with the following code:
 
<source lang="python">
def max_subarray(A):
    max_ending_here = max_so_far = A[0]
    for x in A[1:]:
        max_ending_here = max(x, max_ending_here + x)
        max_so_far = max(max_so_far, max_ending_here)
    return max_so_far
</source>
 
Here is a version in [[C++]] which can optionally return the beginning and ending indices of the maximum subarray:
 
<source lang="cpp">
int sequence(std::vector<int> const & numbers)
{
        // Initialize variables here
        int max_so_far  = numbers[0], max_ending_here = numbers[0];
 
        // OPTIONAL: These variables can be added in to track the position of the subarray
        // size_t begin = 0;
        // size_t begin_temp = 0;
        // size_t end = 0;
 
        // Find sequence by looping through
        for(size_t i = 1; i < numbers.size(); i++)
        {
                // calculate max_ending_here
                if(max_ending_here < 0)
                {
                        max_ending_here = numbers[i];
                       
                        // begin_temp = i;
                }
                else
                {
                        max_ending_here += numbers[i];
                }
 
                // calculate max_so_far
                if(max_ending_here >= max_so_far )
                {
                        max_so_far  = max_ending_here;
                       
                        // begin = begin_temp;
                        // end = i;
                }
        }
        return max_so_far ;
}
</source>
 
The algorithm can also be easily modified to keep track of the starting and ending indices of the maximum subarray (see commented code).
 
Because of the way this algorithm uses optimal substructures (the maximum subarray ending at each position is calculated in a simple way from a related but smaller and overlapping subproblem, the maximum subarray ending at the previous position) this algorithm can be viewed as a simple example of [[dynamic programming]].
 
The runtime complexity of Kadane's algorithm is <math>\mathcal{O}(n)</math>.
 
==Divide and conquer==
A [[divide and conquer algorithm]] is introduced in (Bentley, Jon (1984)); its time complexity is <math>\mathcal{O}(n \log n)</math>.
 
==Generalizations==
Similar problems may be posed for higher dimensional arrays, but their solution is more complicated; see, e.g., {{harvtxt|Takaoka|2002}}. {{harvtxt|Brodal|Jørgensen|2007}} showed how to find the ''k'' largest subarray sums in a one-dimensional array, in the optimal time bound <math>\mathcal{O}(n + k)</math>.
 
== See also ==
 
* [[Subset sum problem]]
 
==References==
*{{citation
| first = Jon | last = Bentley | authorlink = Jon Bentley
| title = Programming pearls: algorithm design techniques
| journal = [[Communications of the ACM]]
| volume = 27 | issue = 9 | year = 1984 | doi = 10.1145/358234.381162 | pages = 865–873}}.
*{{citation
| first1 = Gerth Stølting | last1 = Brodal | first2 = Allan Grønlund | last2 = Jørgensen
| contribution = A linear time algorithm for the ''k'' maximal sums problem
| title = Mathematical Foundations of Computer Science 2007
| publisher = Springer-Verlag
| series = Lecture Notes in Computer Science
| volume = 4708 | year = 2007 | pages = 442–453 | doi = 10.1007/978-3-540-74456-6_40}}.
*{{citation
| first = T. | last = Takaoka
| title = Efficient algorithms for the maximum subarray problem by distance matrix multiplication
| journal = Electronic Notes in Theoretical Computer Science | volume = 61 | year = 2002
| url = http://www.cosc.canterbury.ac.nz/tad.takaoka/cats02.pdf}}.
 
== External links ==
* [http://www.algorithmist.com/index.php/Kadane's_Algorithm www.algorithmist.com]
* [http://alexeigor.wikidot.com/kadane alexeigor.wikidot.com]
* [http://www.cs.waikato.ac.nz/Teaching/COMP317B/Week_1/AlgorithmDesignTechniques.pdf Algorithm Design Techniques]
 
[[Category:Optimization algorithms and methods]]
[[Category:Dynamic programming]]
[[Category:Articles with example Python code]]

Revision as of 03:56, 19 January 2014

In computer science, the maximum subarray problem is the task of finding the contiguous subarray within a one-dimensional array of numbers (containing at least one positive number) which has the largest sum. For example, for the sequence of values −2, 1, −3, 4, −1, 2, 1, −5, 4; the contiguous subarray with the largest sum is 4, −1, 2, 1, with sum 6.

The problem was first posed by Ulf Grenander of Brown University in 1977, as a simplified model for maximum likelihood estimation of patterns in digitized images. A linear time algorithm was found soon afterwards by Jay Kadane of Carnegie-Mellon University Template:Harv.

Kadane's algorithm

Kadane's algorithm consists of a scan through the array values, computing at each position the maximum subarray ending at that position. This subarray is either empty (in which case its sum is zero) or consists of one more element than the maximum subarray ending at the previous position. Thus, the problem can be solved with the following code, expressed here in Python:

def max_subarray(A):
    max_ending_here = max_so_far = 0
    for x in A:
        max_ending_here = max(0, max_ending_here + x)
        max_so_far = max(max_so_far, max_ending_here)
    return max_so_far

A variation of the problem that does not allow zero-length subarrays to be returned in the case that the entire array consists of negative numbers can be solved with the following code:

def max_subarray(A):
    max_ending_here = max_so_far = A[0]
    for x in A[1:]:
        max_ending_here = max(x, max_ending_here + x)
        max_so_far = max(max_so_far, max_ending_here)
    return max_so_far

Here is a version in C++ which can optionally return the beginning and ending indices of the maximum subarray:

int sequence(std::vector<int> const & numbers)
{
        // Initialize variables here
        int max_so_far  = numbers[0], max_ending_here = numbers[0];

        // OPTIONAL: These variables can be added in to track the position of the subarray
        // size_t begin = 0;
        // size_t begin_temp = 0;
        // size_t end = 0;

        // Find sequence by looping through
        for(size_t i = 1; i < numbers.size(); i++)
        {
                // calculate max_ending_here
                if(max_ending_here < 0)
                {
                        max_ending_here = numbers[i];
                        
                        // begin_temp = i;
                }
                else
                {
                        max_ending_here += numbers[i];
                }

                // calculate max_so_far
                if(max_ending_here >= max_so_far )
                {
                        max_so_far  = max_ending_here;
                        
                        // begin = begin_temp;
                        // end = i;
                }
        }
        return max_so_far ;
}

The algorithm can also be easily modified to keep track of the starting and ending indices of the maximum subarray (see commented code).

Because of the way this algorithm uses optimal substructures (the maximum subarray ending at each position is calculated in a simple way from a related but smaller and overlapping subproblem, the maximum subarray ending at the previous position) this algorithm can be viewed as a simple example of dynamic programming.

The runtime complexity of Kadane's algorithm is 𝒪(n).

Divide and conquer

A divide and conquer algorithm is introduced in (Bentley, Jon (1984)); its time complexity is 𝒪(nlog⁡n).

Generalizations

Similar problems may be posed for higher dimensional arrays, but their solution is more complicated; see, e.g., Template:Harvtxt. Template:Harvtxt showed how to find the k largest subarray sums in a one-dimensional array, in the optimal time bound 𝒪(n+k).

See also

References

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    15.6.1 As the agent is an intermediary, generally, as soon as the principal and third party are introduced right into a contractual relationship, the agent drops out of the image, subject to any problems with remuneration or indemnification that he could have against the principal, and extra exceptionally, against the third occasion. Generally, agents are entitled to be indemnified for all liabilities reasonably incurred within the execution of the brokers´ authority.

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    Once you place down the one per cent choice price to carry down a non-public property, it's important to accept its situation as it is whenever you move in – faulty air-con, choked rest room and all. Get round this by asking your agent to incorporate a ultimate inspection clause within the possibility-to-buy letter. HDB flat patrons routinely take pleasure in this security net. "There's a ultimate inspection of the property two days before the completion of all HDB transactions. If the air-con is defective, you can request the seller to repair it," says Kelvin.

    15.6.1 As the agent is an intermediary, generally, as soon as the principal and third party are introduced right into a contractual relationship, the agent drops out of the image, subject to any problems with remuneration or indemnification that he could have against the principal, and extra exceptionally, against the third occasion. Generally, agents are entitled to be indemnified for all liabilities reasonably incurred within the execution of the brokers´ authority.

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