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UPCOMING CHALLENGES:

Germanium 2018

CURRENT CHALLENGES:

Gallium 2018

PAST CHALLENGES

Zinc 2018

Cuprum 2018

Cutting Complexity

Nickel 2018

Cobaltum 2018

Ferrum 2018

Manganum 2017

Chromium 2017

Vanadium 2016

Titanium 2016

Scandium 2016

Calcium 2015

Kalium 2015

Argon 2015

Chlorum 2014

Sulphur 2014

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Silicium 2014

Aluminium 2014

Magnesium 2014

Natrium 2014

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Fluorum 2014

Oxygenium 2014

Nitrogenium 2013

Carbo 2013

Boron 2013

Beryllium 2013

Lithium 2013

Helium 2013

Hydrogenium 2013

Omega 2013

Psi 2012

Chi 2012

Phi 2012

Upsilon 2012

Tau 2012

Sigma 2012

Rho 2012

Pi 2012

Omicron 2012

Xi 2012

Nu 2011

Mu 2011

Lambda 2011

Kappa 2011

Iota 2011

Theta 2011

Eta 2011

Zeta 2011

Epsilon 2011

Delta 2011

Gamma 2011

Beta 2010

Alpha 2010

Programming language:
Spoken language:

Neverland has very interesting topography. It is a flat land, but it seems to be the surface of a torus. That is, its map is a rectangle, but its northern edge is adjacent to its southern edge and, at the same time, its western edge is adjacent to its eastern edge. That is why it is so difficult to escape from Neverland...

Recently, the land in Neverland has gone up for sale. It has been divided into a grid of unit squares, comprising M rows and N columns. Rows are numbered on the map from 0 to M − 1, from north to south, and columns are numbered on the map from 0 to N − 1, from west to east. Row M − 1 is adjacent to row 0 and column N − 1 is adjacent to column 0.

You are allowed to buy one rectangular lot, which you plan to divide into smaller lots and sell for a profit. Note that your lot can overlap the edges of the map.

A zero-indexed matrix C of integers, consisting of M rows and N columns, is given. C[I][J] equals the expected profit that you can make on the unit square in row I and column J.

Write a function:

int solution(int **C, int M, int N);

that, given such a matrix C, returns the maximum possible profit that you can make. If there is no profitable lot for you to buy, the function should return 0.

For example, consider the following matrix C, consisting of three rows and three columns:

The unit squares in row 1 and column 1 are not profitable. Only the unit squares in the corners of the map are profitable, and they form a 2 × 2 square. So, this 2 × 2 square is the optimal lot, and the profit it can earn for you is 10.

Now consider the following matrix C, consisting of two rows and two columns:

In this example there are only two single unit square lots that are profitable, and the maximum profit is 3.

Assume that:

- M and N are integers within the range [1..100];
- each element of matrix C is an integer within the range [−10,000..10,000].

Complexity:

- expected worst-case time complexity is O((M+N)
^{3});- expected worst-case space complexity is O(M*N) (not counting the storage required for input arguments).

Copyright 2009–2018 by Codility Limited. All Rights Reserved. Unauthorized copying, publication or disclosure prohibited.

Neverland has very interesting topography. It is a flat land, but it seems to be the surface of a torus. That is, its map is a rectangle, but its northern edge is adjacent to its southern edge and, at the same time, its western edge is adjacent to its eastern edge. That is why it is so difficult to escape from Neverland...

Recently, the land in Neverland has gone up for sale. It has been divided into a grid of unit squares, comprising M rows and N columns. Rows are numbered on the map from 0 to M − 1, from north to south, and columns are numbered on the map from 0 to N − 1, from west to east. Row M − 1 is adjacent to row 0 and column N − 1 is adjacent to column 0.

You are allowed to buy one rectangular lot, which you plan to divide into smaller lots and sell for a profit. Note that your lot can overlap the edges of the map.

A zero-indexed matrix C of integers, consisting of M rows and N columns, is given. C[I][J] equals the expected profit that you can make on the unit square in row I and column J.

Write a function:

int solution(vector< vector<int> > &C);

that, given such a matrix C, returns the maximum possible profit that you can make. If there is no profitable lot for you to buy, the function should return 0.

For example, consider the following matrix C, consisting of three rows and three columns:

The unit squares in row 1 and column 1 are not profitable. Only the unit squares in the corners of the map are profitable, and they form a 2 × 2 square. So, this 2 × 2 square is the optimal lot, and the profit it can earn for you is 10.

Now consider the following matrix C, consisting of two rows and two columns:

In this example there are only two single unit square lots that are profitable, and the maximum profit is 3.

Assume that:

- M and N are integers within the range [1..100];
- each element of matrix C is an integer within the range [−10,000..10,000].

Complexity:

- expected worst-case time complexity is O((M+N)
^{3});- expected worst-case space complexity is O(M*N) (not counting the storage required for input arguments).

Copyright 2009–2018 by Codility Limited. All Rights Reserved. Unauthorized copying, publication or disclosure prohibited.

Neverland has very interesting topography. It is a flat land, but it seems to be the surface of a torus. That is, its map is a rectangle, but its northern edge is adjacent to its southern edge and, at the same time, its western edge is adjacent to its eastern edge. That is why it is so difficult to escape from Neverland...

Recently, the land in Neverland has gone up for sale. It has been divided into a grid of unit squares, comprising M rows and N columns. Rows are numbered on the map from 0 to M − 1, from north to south, and columns are numbered on the map from 0 to N − 1, from west to east. Row M − 1 is adjacent to row 0 and column N − 1 is adjacent to column 0.

You are allowed to buy one rectangular lot, which you plan to divide into smaller lots and sell for a profit. Note that your lot can overlap the edges of the map.

A zero-indexed matrix C of integers, consisting of M rows and N columns, is given. C[I][J] equals the expected profit that you can make on the unit square in row I and column J.

Write a function:

class Solution { public int solution(int[][] C); }

that, given such a matrix C, returns the maximum possible profit that you can make. If there is no profitable lot for you to buy, the function should return 0.

For example, consider the following matrix C, consisting of three rows and three columns:

The unit squares in row 1 and column 1 are not profitable. Only the unit squares in the corners of the map are profitable, and they form a 2 × 2 square. So, this 2 × 2 square is the optimal lot, and the profit it can earn for you is 10.

Now consider the following matrix C, consisting of two rows and two columns:

In this example there are only two single unit square lots that are profitable, and the maximum profit is 3.

Assume that:

- M and N are integers within the range [1..100];
- each element of matrix C is an integer within the range [−10,000..10,000].

Complexity:

- expected worst-case time complexity is O((M+N)
^{3});- expected worst-case space complexity is O(M*N) (not counting the storage required for input arguments).

Copyright 2009–2018 by Codility Limited. All Rights Reserved. Unauthorized copying, publication or disclosure prohibited.

Write a function:

func Solution(C [][]int) int

For example, consider the following matrix C, consisting of three rows and three columns:

Now consider the following matrix C, consisting of two rows and two columns:

Assume that:

- M and N are integers within the range [1..100];
- each element of matrix C is an integer within the range [−10,000..10,000].

Complexity:

- expected worst-case time complexity is O((M+N)
^{3});

Write a function:

class Solution { public int solution(int[][] C); }

For example, consider the following matrix C, consisting of three rows and three columns:

Now consider the following matrix C, consisting of two rows and two columns:

Assume that:

- M and N are integers within the range [1..100];
- each element of matrix C is an integer within the range [−10,000..10,000].

Complexity:

- expected worst-case time complexity is O((M+N)
^{3});

Write a function:

function solution(C);

For example, consider the following matrix C, consisting of three rows and three columns:

Now consider the following matrix C, consisting of two rows and two columns:

Assume that:

- M and N are integers within the range [1..100];
- each element of matrix C is an integer within the range [−10,000..10,000].

Complexity:

- expected worst-case time complexity is O((M+N)
^{3});

Write a function:

function solution(C)

For example, consider the following matrix C, consisting of three rows and three columns:

Now consider the following matrix C, consisting of two rows and two columns:

Assume that:

- M and N are integers within the range [1..100];
- each element of matrix C is an integer within the range [−10,000..10,000].

Complexity:

- expected worst-case time complexity is O((M+N)
^{3});

Write a function:

int solution(NSMutableArray *C);

For example, consider the following matrix C, consisting of three rows and three columns:

Now consider the following matrix C, consisting of two rows and two columns:

Assume that:

- M and N are integers within the range [1..100];
- each element of matrix C is an integer within the range [−10,000..10,000].

Complexity:

- expected worst-case time complexity is O((M+N)
^{3});

Assume that the following declarations are given:

type TMatrix = array of array of longint;

Write a function:

function solution(C: TMatrix; M: longint; N: longint): longint;

For example, consider the following matrix C, consisting of three rows and three columns:

Now consider the following matrix C, consisting of two rows and two columns:

Assume that:

- M and N are integers within the range [1..100];
- each element of matrix C is an integer within the range [−10,000..10,000].

Complexity:

- expected worst-case time complexity is O((M+N)
^{3});

Write a function:

function solution($C);

For example, consider the following matrix C, consisting of three rows and three columns:

Now consider the following matrix C, consisting of two rows and two columns:

Assume that:

- M and N are integers within the range [1..100];
- each element of matrix C is an integer within the range [−10,000..10,000].

Complexity:

- expected worst-case time complexity is O((M+N)
^{3});

Write a function:

sub solution { my (@C)=@_; ... }

For example, consider the following matrix C, consisting of three rows and three columns:

Now consider the following matrix C, consisting of two rows and two columns:

Assume that:

- M and N are integers within the range [1..100];
- each element of matrix C is an integer within the range [−10,000..10,000].

Complexity:

- expected worst-case time complexity is O((M+N)
^{3});

Write a function:

def solution(C)

For example, consider the following matrix C, consisting of three rows and three columns:

Now consider the following matrix C, consisting of two rows and two columns:

Assume that:

- M and N are integers within the range [1..100];
- each element of matrix C is an integer within the range [−10,000..10,000].

Complexity:

- expected worst-case time complexity is O((M+N)
^{3});

Write a function:

def solution(c)

For example, consider the following matrix C, consisting of three rows and three columns:

Now consider the following matrix C, consisting of two rows and two columns:

Assume that:

- M and N are integers within the range [1..100];
- each element of matrix C is an integer within the range [−10,000..10,000].

Complexity:

- expected worst-case time complexity is O((M+N)
^{3});

Write a function:

object Solution { def solution(c: Array[Array[Int]]): Int }

For example, consider the following matrix C, consisting of three rows and three columns:

Now consider the following matrix C, consisting of two rows and two columns:

Assume that:

- M and N are integers within the range [1..100];
- each element of matrix C is an integer within the range [−10,000..10,000].

Complexity:

- expected worst-case time complexity is O((M+N)
^{3});

Write a function:

public func solution(inout C : [[Int]]) -> Int

For example, consider the following matrix C, consisting of three rows and three columns:

Now consider the following matrix C, consisting of two rows and two columns:

Assume that:

- M and N are integers within the range [1..100];
- each element of matrix C is an integer within the range [−10,000..10,000].

Complexity:

- expected worst-case time complexity is O((M+N)
^{3});

Write a function:

public func solution(_ C : inout [[Int]]) -> Int

For example, consider the following matrix C, consisting of three rows and three columns:

Now consider the following matrix C, consisting of two rows and two columns:

Assume that:

- M and N are integers within the range [1..100];
- each element of matrix C is an integer within the range [−10,000..10,000].

Complexity:

- expected worst-case time complexity is O((M+N)
^{3});

Write a function:

Private Function solution(C As Integer()()) As Integer

For example, consider the following matrix C, consisting of three rows and three columns:

Now consider the following matrix C, consisting of two rows and two columns:

Assume that:

- M and N are integers within the range [1..100];
- each element of matrix C is an integer within the range [−10,000..10,000].

Complexity:

- expected worst-case time complexity is O((M+N)
^{3});

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