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Java 9: Data Structures are introductory tutorials for a broad set of data formats that can be learned in Java or in any Java programming language. This product’s contents are offered without any warranty.

Preface
Chapter 1: Why Bother? – Basic
The performance of an algorithm
Best case, worst case and the average case complexity
Analysis of asymptotic complexity
Asymptotic upper bound of a function
Asymptotic upper bound of an algorithm
Asymptotic lower bound of a function
Asymptotic tight bound of a function
Optimization of our algorithm
Fixing the problem with large powers
Improving time complexity
Summary
Chapter 2: Cogs and Pulleys – Building Blocks
Arrays
Insertion of elements in an array
Insertion of a new element and the process of appending it
Linked list
Appending at the end
Insertion at the beginning
Insertion at an arbitrary position
Looking up an arbitrary element
Removing an arbitrary element
Iteration
Doubly linked list
Insertion at the beginning or at the end
Insertion at an arbitrary location
Removing the first element
Removing an arbitrary element
Removal of the last element
Circular linked list
Insertion
Removal
Rotation
Summary
Chapter 3: Protocols – Abstract Data Types
Stack
Fixed-sized stack using an array
Variable-sized stack using a linked list
Queue
Fixed-sized queue using an array
Variable-sized queue using a linked list
Double ended queue
Fixed-length double ended queue using an array
Variable-sized double ended queue using a linked list
Summary
Chapter 4: Detour – Functional Programming
Recursive algorithms
Lambda Expressions in Java
Functional interface
Implementing a functional interface with lambda
Functional data structures and monads
Functional linked lists
The forEach method for a linked list
Map for a linked list
Fold operation on a list
Filter operation for a linked list
Append on a linked list
The flatMap method on a linked list
The concept of a monad
Option monad
Try monad
Analysis of the complexity of a recursive algorithm
Performance of functional programming
Summary
Chapter 5: Efficient Searching – Binary Search and Sorting
Search algorithms
Binary search
Complexity of the binary search algorithm
Sorting
Selection sort
Complexity of the selection sort algorithm
Insertion sort
Complexity of insertion sort
Bubble sort
Inversions
Complexity of the bubble sort algorithm
A problem with recursive calls
Tail recursive functions
Non-tail single recursive functions
Summary
Chapter 6: Efficient Sorting – Quicksort and Merge Sort
Quicksort
Complexity of quicksort
Random pivot selection in quicksort
Merge sort
The complexity of merge sort
Avoiding the copying of tempArray
Complexity of any comparison-based sorting
The stability of a sorting algorithm
Summary
Chapter 7: Concepts of Tree
A tree data structure
The traversal of a tree
The depth-first traversal
The breadth-first traversal
The tree abstract data type
Binary tree
Types of depth-first traversals
Non-recursive depth-first search
Summary
Chapter 8: More About Search – Search Trees and Hash Tables
Binary search tree
Insertion in a binary search tree
Invariant of a binary search tree
Deletion of an element from a binary search tree
Complexity of the binary search tree operations
Self-balancing binary search tree
AVL tree
Complexity of search, insert, and delete in an AVL tree
Red-black tree
Insertion
Deletion
The worst case of a red-black tree
Hash tables
Insertion
The complexity of insertion
Search
Complexity of the search
Choice of load factor
Summary
Chapter 9: Advanced General Purpose Data Structures
Priority queue ADT
Heap
Insertion
Removal of minimum elements
Analysis of complexity
Serialized representation
Array-backed heap
Linked heap
Insertion
Removal of the minimal elements
Complexity of operations in ArrayHeap and LinkedHeap
Binomial forest
Why call it a binomial tree?
Number of nodes
The heap property
Binomial forest
Complexity of operations in a binomial forest
Sorting using a priority queue
In-place heap sort
Summary
Chapter 10: Concepts of Graph
What is a graph?
The graph ADT
Representation of a graph in memory
Adjacency matrix
Complexity of operations in a sparse adjacency matrix graph
More space-efficient adjacency-matrix-based graph
Complexity of operations in a dense adjacency-matrix-based graph
Adjacency list
Complexity of operations in an adjacency-list-based graph
Adjacency-list-based graph with dense storage for vertices
Complexity of the operations of an adjacency-list-based graph with dense storage for vertices
Traversal of a graph
Complexity of traversals
Cycle detection
Complexity of the cycle detection algorithm
Spanning tree and minimum spanning tree
For any tree with vertices V and edges E, |V| = |E| + 1
Any connected undirected graph has a spanning tree
Any undirected connected graph with the property |V| = |E| + 1 is a tree
Cut property
Minimum spanning tree is unique for a graph that has all the edges whose costs are different from one another
Finding the minimum spanning tree
Union find
Complexity of operations in UnionFind
Implementation of the minimum spanning tree algorithm
Complexity of the minimum spanning tree algorithm
Summary
Chapter 11: Reactive Programming
What is reactive programming?
Producer-consumer model
Semaphore
Compare and set
Volatile field
Thread-safe blocking queue
Producer-consumer implementation
Spinlock and busy wait
Functional way of reactive programming
Summary
Index

Java 9 Data Structure and Algorithms PDF

Improving Time complexity

Analysis of Asymptotic complexity

About the Author:

Debasish Ray Chawdhuri

Debasish Ray Chawdhuri is an established Java developer and has been in the industry for the last 8 years. He has developed several systems, right from CRUD applications to programming languages and big data processing systems.

Best case, worst case and the average case complexity

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