Algorithms-in-Java | Commonly used algorithms including : sorting algorithms | Learning library
kandi X-RAY | Algorithms-in-Java Summary
kandi X-RAY | Algorithms-in-Java Summary
Commonly used algorithms including: sorting algorithms, two pointers, dynamic programming, backtracking, KMP, bit operation, etc
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Top functions reviewed by kandi - BETA
- Merge two arrays
- Merge arrays
- Internal merge sort method
- Merge sort
- Utility function to print a matrix
- Print a matrix
- Print matrix
- Merge arrays
- Inserts an element into the array
- Entry point to the BFS
- Entry point
- Construct a linked list from a comma - separated list
- Simple test for testing
- Heap sort the elements of the array
- Solve the puzzle
- Construct a tree from a string
- Validates the generator
- Simple test
- Static test program
- Is the root of the tree iterative
- Test to see if the tree is modified
- Main entry point
- Example of how to use this method
- Test program
- Sorts the original array
- Prints a list
Algorithms-in-Java Key Features
Algorithms-in-Java Examples and Code Snippets
Lines of Code : 62
License : No License
Copypublic interface Trampoline {
T get();
default Trampoline jump() {
return this;
}
default T result() {
return get();
}
default boolean complete() {
return true;
}
static Trampoline done(final T result) {
return ( public static int fibMonaccianSearch(int arr[], int x, int n)
{
/* Initialize fibonacci numbers */
int fibMMm2 = 0; // (m-2)'th Fibonacci No.
int fibMMm1 = 1; // (m-1)'th Fibonacci No.
int fibM = fibMMm2 + fibMMm1; // m'th Fibonacci
public static int MatrixChainMultiplication(int[] dims, int i, int j)
{
// base case: one matrix
if (j <= i + 1) {
return 0;
}
// stores minimum number of scalar multiplications (i.e., cost)
// needed to compute the matrix M[i+1]... static int interpolationSearch(int x) {
// Find indexes of two corners
int lo = 0, hi = (arr.length - 1);
// Since array is sorted, an element present
// in array must be in range defined by corner
while (lo <= hi && x >= arr Community Discussions
Trending Discussions on Algorithms-in-Java
QUESTION
To get the book list from https://www.java67.com/2015/09/top-10-algorithm-books-every-programmer-read-learn.html , I am using the following code in the console of the firefox DevTools:
...ANSWER
Answered 2020-Sep-22 at 17:21QUESTION
I am having a hard time understanding the process of drawing the repeated square diagram of recursion.
I have the book of data structure and algorithms which shows this code on page 210:
...ANSWER
Answered 2020-Jun-28 at 14:05The difference in numbers that you see in the recursion trace comes from n/2 in the code. It is an integer division by two. So 13/2 is 6 and 6/2 is 3 and 3/2 is 1, and finally 1/2 is 0. This is what you see when you read the diagram from top to bottom. The diagram shows these entries shifted more and more to the right to represent the depth of the recursion. The function calls itself, and then it calls itself again, ...etc. All of these calls are pending... Each time these calls pass a value for n that is smaller (halved).
Somewhere this stops. It needs to, of course. This stops when the value of n receives the value 0. You see this state at the bottom of the diagram. At that moment, the recursion does not go deeper and starts to unwind, and you need to read the diagram now from bottom back to the top. For n equal to 0, the return value is 1 (return 1).
This value is read by the function execution where n is 1 and where the call power(2, 0) was made and waiting for a return value. It receives 1 as result. This value is then squared (partial * partial) which in this case still is 1. And because n is odd (it is 1 here), there is an additional multiplication *= x. So we actually calculate partial * partial * x, which is 1 * 1 * 2. You'll see this in the diagram.
And this value (2) is returned to the function execution where n is 3 and where the call power(2, 1) was made. It receives 2 as result. This value is then squared (partial * partial) which in this case is 4. And because n is odd (it is 3 here), there is an additional multiplication *= x. So we actually calculate partial * partial * x, which is 2 * 2 * 2. You'll see this in the diagram.
I hope you see the pattern. The function executions that are pending for a result, all get their value back from the recursive function call they made, do some multiplication with it and return that, in turn, to their caller.
And so the recursion backtracks to the top, providing the end result to the top-level call of power(2, 13).
QUESTION
I have implemented a merge sort algorithm using divide and conquer approach where an array is split into two sub arrays.
In my code i re-used insertion sort algorithm to sort the sub arrays in merge sort. Is this right approach or i have to use different sorting approach to sort the sub arrays in merge sort ?
As far as concerned with the understanding of merge sort algorithm, everything is clear but when coming to the implementation of merge sort, how does it happen to divide an array into n-sub arrays without using recursive strategy.
is recursive or non-recursive efficient way to implement merge sort ?
Below is my code snippet in github: https://github.com/vamsikankipati/algorithms-in-java/blob/master/src/com/algorithms/sort/MergeSort.java
I have understood from implementation perspective that my code is wrong as i divided the array into only two sub arrays instead of n-sub arrays.
Any help needed to clearly understand merge sort in terms of algorithm implementation perspective.
Here is the code:
...ANSWER
Answered 2020-May-02 at 15:03Both time complexity is O( n log n).
About space complexity, the implementation may vary on your data structure choice. in recursive if you choose array: space complexity: N log N if you choose linked-list: space complexity is O(1) in iterative: if you choose array: space complexity: N ( based on your implementation it is O ( N log N) because you create a new sub-array in every dividing state, to reduce it to O(n) you should use one extra array as size of the original one, and indexes) if you choose linked-list: space complexity is O(1)
As you can see linked-list is best for sorting. Beyond that recursive may consume more memory than expected based on programming language because of function frame creation.
QUESTION
I was looking at this simple implementation of the QuickSort algorithm.
I'm having trouble with the partition method. I've re-named the variables to help me understand. I understand most of the method, but there's one variable whose purpose I don't get. That's the store variable.
Here's my version of this method:
...ANSWER
Answered 2019-Apr-25 at 18:02To answer your simpler question, you should be able to see that store does not remain the same as currentIndex as the iteration progresses. It is only incremented if array[currentIndex] <= array[pivotIndex] is true for a particular iteration. - so store is generally going to be smaller than currentIndex.
To answer more specifically, what is going on is that you pick a pivotIndex at the beginning that is pointing at some value. Then you are moving elements around in the table so that all of the values smaller than the pivotIndex value are before that value in the array, and all of the values greater than the pivotIndex value are after that value in the array. store is keeping track of where to move the next value that is found to be smaller than the pivotIndex value. That value is moved to the store position, and then store is incremented to move past that value and represent where the next smaller value should be placed.
At the end of the iteration, store is by design pointing to where the pivot value should be, since all values before store are less than that value, and all values after store are greater. So at the end, the pivot value is moved to the location pointed to by store, and store becomes the final index of the partitioning operation, the index pointing to the pivot value
QUESTION
I'm studying some common algorithms implementations in JavaScript, and found this one while looking for quicksort: https://rawgit.com/escherba/algorithms-in-javascript/master/src/quickmiddle-sort.js
It implements an array partition function as well:
...ANSWER
Answered 2017-Mar-23 at 10:55Bitwise operation is nothing but dividing the sum of left and right values by 2. if left=2 and right=7 output is 9/2 and truncated to 4.
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You can use Algorithms-in-Java like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the Algorithms-in-Java component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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