With a deeper understanding of algorithms and its basic concepts, our programming assignment experts will help you to define the basic groundwork and algorithms based on your theory. We can help you to create the efficient and correct algorithms to achieve the goal of your application or operation.
Algorithms are the base of your application and creating the powerful algorithms is always helping you to achieve your goal of the system.
An algorithm is a methodical approach for solving a problem.
When writing a good algorithm, it should be written in such a way that it can be applied in all programming language. Algorithm can be common for many programming languages to achieve similar output from it. Every step in algorithm must be clear and very important. Algorithm in Programming should have a clear starting and a stopping point. Some students usually mistake algorithm with coding but algorithm is just the instructions to the computer to get an expected result. There are various ways to classify algorithms. Some of the popular methodologies are:
The logic component expresses the axioms that may be used in the computation and the control component determines the way in which deduction is applied to the axioms. This is the basis for the logic programming paradigm. In pure logic programming languages the control component is fixed and algorithms are specified by supplying only the logic component.
Serial, Parallel or Distributed:
Algorithms are usually discussed with the assumption that computers execute one instruction of an algorithm at a time. Those computers are sometimes called serial computers. An algorithm designed for such an environment is called a serial algorithm, as opposed to parallel algorithms or distributed algorithms. Parallel algorithms take advantage of computer architectures where several processors can work on a problem at the same time, whereas distributed algorithms utilize multiple machines connected with a network.
Parallel or distributed algorithms divide the problem into more symmetrical or asymmetrical sub problems and collect the results back together.
Deterministic or Non-deterministic:
Deterministic algorithms solve the problem with exact decision at every step of the algorithm whereas non-deterministic algorithms solve problems via guessing although typical guesses are made more accurate through the use of heuristics.
Exact or Approximate:
While many algorithms reach an exact solution, approximation algorithms seek an approximation that is closer to the true solution. Approximation can be reached by either using a deterministic or a random strategy. Such algorithms have practical value for many hard problems.
Divide and Conquer:
A divide and conquer algorithm repeatedly reduces an instance of a problem to one or more smaller instances of the same problem until the instances are small enough to solve easily. One such example of divide and conquer is merge sorting. Sorting can be done on each segment of data after dividing data into segments and sorting of entire data can be obtained in the conquer phase by merging the segments.
Search and Enumeration:
Many problems (such as playing chess) can be modeled as problems on graphs. A graph exploration algorithm specifies rules for moving around a graph and is useful for such problems. This category also includes search algorithms, branch and bound enumeration and backtracking.
Such algorithms make some choices randomly. They can be very useful in finding approximate solutions for problems where finding exact solutions can be
Reduction of Complexity:
This technique involves solving a difficult problem by transforming it into a better known problem for which we have asymptotically optimal algorithms. The goal is to find a reducing algorithm whose complexity is not dominated by the resulting reduced algorithm's.
When searching for optimal solutions to a linear function bound to linear equality and inequality constraints, the constraints of the problem can be used directly in producing the optimal solutions.
When a problem shows optimal substructures – meaning the optimal solution to a problem can be constructed from optimal solutions to sub-problems – and overlapping sub-problems, meaning the same sub-problems are used to solve many different problem instances, a quicker approach called dynamic programming avoids recomputing solutions that have already been computed. Sub-problems overlap in dynamic programming.
Dynamic programming reduces the exponential nature of many problems to polynomial complexity.
The Greedy Method:
A greedy algorithm is similar to a dynamic programming algorithm in that it works by examining substructures, in this case not of the problem but of a given solution. Such algorithms start with some solution, which may be given or have been constructed in some way, and improve it by making small modifications.
Properties of Algorithms
1. Initialize sum =0 and count= 0
2. Enter n (I/O)
3. Find sum+n and assign it to sum then increment count by 1
4. If count<5
if YES go to step 2
Print sum (I/O)
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|Alpha Beta Pruning Algorithm|
Analysis of Algorithms
Bellman Ford Algorithm
Binary Search Algorithms
Bucket Sort Algorithm
Bubble Sort Algorithm
Burrows Wheeler Algorithm
Comb Sort Algorithm
Counting Sort Algorithm
Depth First Search Algorithm
Dikin Ellipsoid Algorithm
Huffman Code Algorithm
Huffman Tree Algorithm
Insertion Sort Algorithm
Linear Search Algorithm
Merge Sort Algorithm
NP Completeness Algorithm
Selection Sort Algorithm
Sequential Search Algorithm
Sort Merge Joins Algorithm
Topological Sort Algorithm
Tree Sort Algorithm