Algorithm Assignment Help

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We have hired professionals who have the expertise and knowledge of designing algorithm assignments. Let us first understand what an algorithm is before we discuss why you should opt for our service.

What is an Algorithm?

An algorithm is a set of instructions to be executed in a particular order to yield the desired output.

It is a procedure that is generally created independent of underlying languages. In other words, an algorithm is cross-platform. It can be implemented in more than one programming language. With our help with algorithms programming assignments, you can get better grades in your term. 

Categories of Algorithms Based on Data Structures

  • Search – This type of algorithm searches an item in a data structure

  • Sort - This is an algorithm that sorts items in a certain order.
  • Insert – The insert algorithm inserts items in a data structure
  • Update - This type of algorithm is used to update an existing item in a data structure
  • Delete - A delete algorithm removes an existing item from a data structure

Characteristics of an Algorithm

You cannot classify all procedures as an algorithm. Here are the features that differentiate an algorithm from other procedures.
The step by step directions of an algorithm should be independent of any programming code.

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There are no outlined steps and well-defined standards for writing an algorithm.
Instead, it is the problem at hand and resources that determine how the algorithm is to be written. You should remember that algorithms are never written to support a particular programming code.

From your basic programming language, your professor probably mentioned that all programming languages share code constructs. These same constructs like loops (do, for, and while) and flow control (if, else) can be used to write an algorithm. The programmers associated with us can help you write exceptional algorithms.

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Algorithms are usually written in a step-by-step manner. However, this is not always the case. The process of writing an algorithm can only be executed after the problem domain has been well-defined. This means that we should perfectly understand the problem for which we are designing a solution.

Example
Suppose we want to design an algorithm that adds two numbers and displays the result:
1. START
2. Declare three integers, which are a, b & C
3. Provide the values of a & b
4. Add the values of a & b
5. Store the sum obtained from the output (step 4) to c
6. Print c
7. STOP

Algorithms guide coders on how to write a program. We can alternatively write the algorithm above in the following way. If you are not familiar with the process of writing an algorithm, consider hiring our experts.

1. START ADD
2. Get values of a & b
3. C = a + b
4. Display c
5. STOP

This second method is usually used in design and analysis to describe an algorithm. A programmer can ignore all the unwanted definitions while analyzing the algorithms. Also, the coder can observe the operations that are in use and the flow of the entire process. It is optional to write step numbers.

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For this reason, we can derive several algorithms for a particular problem. The difficult and most essential part is to analyze the proposed solutions and implementing the more suitable one.

Algorithm Analysis
In algorithm analysis, we focus on the execution or running time of the operations involved. The number of computer instructions executed per operation is known as the running time. There are two different stages where the efficiency of an algorithm can be analyzed, before implementation and after implementation.

Here are the types of algorithm analysis.

1. A Posterior Analysis- A posterior analysis is an empirical analysis of an algorithm. A programming language is used to implement the selected algorithm, which is then executed on a target computer machine. A posterior analysis requires actual statistics like running time and space to be collected. If you do not understand these two types of analysis, you can avail of our help with algorithm assignment.

2. A Prior Analysis - It is a theoretical analysis of an algorithm. When measuring the efficiency of an algorithm, we assume that all other factors like processor speed have no effect on the implementation and are constant.

Algorithm Complexity
Let us say that X is an algorithm and n is the size of input data. The two main factors which decide the efficiency of X is the time and space used by the algorithm.

● The time factor
 To measure time, we have to count the number of key operations such as the comparisons in the sorting algorithm.
● The space factor
We can measure space by counting the maximum memory space that the algorithm requires.
The running time and/or the storage space required by the algorithm is given by the complexity of an algorithm f(n), where n is the size of input data.

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Space Complexity

 In Algorithms, space complexity refers to the amount of memory space required by the algorithm in its life cycle. If you need help with algorithms programming assignments, our programming assignment experts are there to guide you. The sum of the two components mentioned below is equal to the space required by an algorithm: 

● A fixed part – a space that is required to store data and variables that are independent of the size of the problem. Examples include constants used, simple variables, program size, etc.

● A variable part – it is the space required by variables, whose sizes depend on the size of the problem. Examples include dynamic memory allocation and recursion stack space.

The space complexity of an algorithm P can be given by:
S(P) = C+ SP(I) where:
● S(P) – The space complexity of an algorithm
● C – The fixed part
● S(I) – The variable part of the algorithm. It depended on instance characteristic I.
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Time Complexity
This refers to the amount of time required by an algorithm to run to completion. We can define time requirements as a numerical function T(n). This function is measured as the number of steps provided each step consumes constant time.

Classifications of Algorithms

The axioms that may be used in the computation are expressed by the logic component. It is the control component that determines the way deduction is applied to the axioms. The basis for the logic programming paradigm is based on this.

The control component is fixed and the algorithms are specified by supplying only the logic component in pure logic programming languages.
In algorithms, we usually assume that computers execute one instruction of an algorithm at a time. Such computers are sometimes called serial computers. A serial algorithm is designed for such an environment.

It is quite different from parallel and distributed algorithms. Distributed algorithms work with multiple machines that are connected via a network. On the other hand, parallel algorithms utilize computer architectures where several processors can work on a problem at the same time.

With deterministic algorithms, the problem is solved with a precise decision at every step of the algorithm. Non-deterministic algorithms perform guesses while solving problems. It uses heuristics to make typical guesses more accurate.

Some algorithms reach an exact solution. Approximation algorithms on the other hand seek an approximation that is closer to the true solution. A deterministic or a random strategy can be used to reach an approximation.

Approximate algorithms provide practical value for several hard problems.

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Other classifications include:

● Logical

● Serial. Distributed or parallel

● Deterministic or Non-deterministic

● Exact or approximate

● Divide and conquer

● Search and enumeration

● Randomized algorithm

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1. Unambiguous

Algorithms are clear and unambiguous. The steps or phases and their inputs/outputs should be clear and only lead to one meaning.

2. Input

The inputs of an algorithm should be well-defined and be 0 or more.

3. Output

Algorithms can have one or more well-defined outputs

4. Finiteness

 After a finite number of steps, an algorithm must terminate

5. Feasibility

An algorithm should be feasible with the resources available

6. Independent

Note: Distributed and parallel algorithms divide the problem into symmetrical or asymmetrical subproblems and collect the results back together.

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