About This Chapter
Algorithmic Analysis, Sorting & Searching - Chapter Summary
In this chapter, you'll find easy-to-understand lessons about algorithmic sorting, searching and analysis. The lessons cover the functions of algorithm in programming, methods used in algorithm analysis and the seven experimental functions in algorithm analysis. Work at your own pace and go back as often as needed to review any of the lessons over again. If any of the topics are challenging for you, reach out to one of our experts through the Dashboard with your questions. This resource is accessible 24/7 on any computer, tablet or smartphone for easy and flexible study. After finishing this chapter, you should be ready to:
- Outline the principles and usage for Big-O notation in algorithm analysis
- Discuss performance and memory use in Java arrays
- Explain how to sort an array in Java
- Detail the performance and functionality of bubble sort, selection sort and merge sort
- Differentiate between quick sort and insertion sort in Java
- Identify the algorithm related to binary search in Java
- Use recursion in Java for binary search
1. What is an Algorithm in Programming? - Definition, Examples & Analysis
In this lesson, we look at what a programming algorithm is - and what it isn't. We also look at an example of a common algorithm shown as both a numbered list and a flowchart, after which we briefly analyze what it does.
2. What is Algorithm Analysis? - Methods & Types
This lesson explains algorithm analysis and the various ways of doing it. It also describes the difference between experimental and asymptotic analysis and also discusses the limitations of experimental analysis.
3. Seven Experimental Functions in Algorithm Analysis
In this lesson on algorithm analysis, we explore the seven experimental functions in algorithm analysis - constant function, logarithmic functions, linear function, N-log-N function, quadratic function, cubic function, and the exponential function.
4. Big-O Notation in Algorithm Analysis: Principles & Use
This lesson explains the underlying principles behind the Big-O notation. It also shows how to derive a Big-O notation of an algorithm. It then discusses the use of the Big-O notation while deriving space and time complexities.
5. How to Sort an Array in Java
In this lesson, we will learn how sorting works in Java, the importance of the sorting algorithms and the variables to consider when using sorting to manipulate arrays.
6. Bubble Sort in Java: Functionality, Implementation & Performance
This lesson addresses the basics of a Bubble Sort algorithm in Java. It shows how to implement and gives a code walk-through and explains its performance.
7. Selection Sort in Java: Functionality, Implementation & Performance
In this lesson, we will learn the working of selection sort through a Java example. We will also discuss the algorithm and performance of the selection sort algorithm.
8. Merge Sort in Java: Functionality, Implementation & Performance
In this lesson, we will learn the workings of merge sort through a Java example. We will also learn the algorithm and performance of a merge sort algorithm.
9. Quick Sort in Java: Functionality, Implementation & Performance
This lesson will explain how Quick Sort works including its algorithm and implementation in Java. A simple implementation in Java will be presented. The performance and efficiency measured in Big-O notation will be discussed.
10. Insertion Sort in Java: Functionality, Implementation & Performance
There are times when arrays and lists are not in the correct order. We can sort arrays in Java using an insertion sort. This lesson uses working code to explain this type of sort and describe the challenges with processing an insertion sort.
11. Binary Search in Java: Algorithm, Implementation & Analysis
In this lesson, you will understand how binary search works in computer systems. The binary search algorithm is explained and implemented using Java programming.
12. Using Recursion in Java for Binary Search
Today, you will learn how to use Java to perform an efficient binary search of both sorted and unsorted arrays by recursively cutting them in half. Working code examples are provided.
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