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Algorithms Articles
Page 25 of 39
Longest Palindromic Subsequence
Longest Palindromic Subsequence is the subsequence of a given sequence, and the subsequence is a palindrome.In this problem, one sequence of characters is given, we have to find the longest length of a palindromic subsequence.To solve this problem, we can use the recursive formula, If L (0, n-1) is used to store a length of longest palindromic subsequence, thenL (0, n-1) := L (1, n-2) + 2 (When 0'th and (n-1)'th characters are same).Input and OutputInput: A string with different letters or symbols. Say the input is “ABCDEEAB” Output: The longest length of the largest palindromic subsequence. Here it is ...
Read MoreMaximum Length Chain of Pairs
There is a chain of pairs is given. In each pair, there are two integers and the first integer is always smaller, and the second one is greater, the same rule can also be applied for the chain construction. A pair (x, y) can be added after a pair (p, q), only if q < x.To solve this problem, at first, we have to sort given pairs in increasing order of the first element. After that, we will compare the second element of a pair, with the first element of the next pair.Input and OutputInput: A chain of number pairs. ...
Read MoreMaximum size square submatrix with all 1s
When a binary matrix is given, our task is to find a square matrix whose all elements are 1.For this problem, we will make an auxiliary size matrix, whose order is the same as the given matrix. This size matrix will help to represent, in each entry Size[i, j], is the size of a square matrix with all 1s. From that size matrix, we will get the maximum number to get the size of the biggest square matrix.Input and OutputInput: The binary matrix. 0 1 1 0 1 1 1 0 1 0 0 1 1 1 0 1 1 ...
Read MoreMaximum profit by buying and selling a share at most twice
In a trading, one buyer buys and sells the shares, at morning and the evening respectively. If at most two transactions are allowed in a day. The second transaction can only start after the first one is completed. If stock prices are given, then find the maximum profit that the buyer can make.Input and OutputInput: A list of stock prices. {2, 30, 15, 10, 8, 25, 80} Output: Here the total profit is 100. As buying at price 2 and selling at price 30. so profit 28. Then buy at price 8 and sell it again at price 80. So ...
Read MoreMaximum Sum Increasing Subsequence\\n
Maximum Sum Increasing subsequence is a subsequence of a given list of integers, whose sum is maximum and in the subsequence, all elements are sorted in increasing order.Let there is an array to store max sum increasing subsequence, such that L[i] is the max sum increasing subsequence, which is ending with array[i].Input and OutputInput: Sequence of integers. {3, 2, 6, 4, 5, 1} Output: Increasing subsequence whose sum is maximum. {3, 4, 5}.AlgorithmmaxSumSubSeq(array, n)Input: The sequence of numbers, number of elements.Output: Maximum sum of the increasing sub sequence.Begin define array of arrays named subSeqLen of size n. add ...
Read MoreMaximum sum rectangle in a 2D matrix
A matrix is given. We need to find a rectangle (sometimes square) matrix, whose sum is maximum.The idea behind this algorithm is to fix the left and right columns and try to find the sum of the element from the left column to right column for each row, and store it temporarily. We will try to find top and bottom row numbers. After getting the temporary array, we can apply the Kadane’s Algorithm to get maximum sum sub-array. With it, the total rectangle will be formed.Input and OutputInput: The matrix of integers. 1 2 -1 -4 -20 -8 -3 4 ...
Read MoreMin Cost Path
A matrix of the different cost is given. Also, the destination cell is provided. We have to find minimum cost path to reach the destination cell from the starting cell (0, 0).Each cell of the matrix represents the cost to traverse through that cell. From a cell, we cannot move anywhere, we can move either to the right or to the bottom or to the lower right diagonal cell, to reach the destination.Input and OutputInput: The cost matrix. And the destination point. In this case the destination point is (2, 2). 1 2 3 4 8 2 1 5 3 ...
Read MoreMinimum Cost Polygon Triangulation
When nonintersecting diagonals are forming a triangle in a polygon, it is called the triangulation. Our task is to find a minimum cost of triangulation.The cost of triangulation is the sum of the weights of its component triangles. We can find the weight of each triangle by adding their sides, in other words, the weight is the perimeter of the triangle.Input and OutputInput: The points of a polygon. {(0, 0), (1, 0), (2, 1), (1, 2), (0, 2)} Output: The total cost of the triangulation. Here the cost of the triangulation is 15.3006.AlgorithmminCost(polygon, n)Here cost() will be used to calculate ...
Read MoreMinimum Initial Points to Reach Destination
To start from the top-left corner of a given grid, one has to reach the bottom-right corner. Each cell in the grid contains a number, the number may positive or negative. When the person reaches a cell (i, j) the number of tokens he has, may be increased or decreased along with the values of that cell. We have to find the minimum number of initial tokens are required to complete the journey.There are some rules −We can either move to the right or to the bottom.We cannot move to a cell (i, j) if our total token is less ...
Read MoreGenerate Fibonacci Series
The Fibonacci sequence is like this, 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, ……In this sequence, the nth term is the sum of (n-1)'th and (n-2)'th terms.To generate we can use the recursive approach, but in dynamic programming, the procedure is simpler. It can store all Fibonacci numbers in a table, by using that table it can easily generate the next terms in this sequence.Input and OutputInput: Take the term number as an input. Say it is 10 Output: Enter number of terms: 10 10th fibinacci Terms: 55AlgorithmgenFiboSeries(n)Input: max number of terms.Output − The nth Fibonacci ...
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