algorithms in java live problem solving & design techniques

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Algorithms in Java :Live problem solving & Design Techniques

Description

Algorithm Design Techniques: Live problem-solving in Java

Algorithms are everywhere. One great algorithm applied sensibly can result in a System like GOOGLE!

Completer scientists have worked from 100s of years and derived some of the techniques that can be applied to write and design algorithms.

So Why to reinvent the wheel ??

Let’s go through some of the most famous algorithm design techniques in this course.

Once you will come to know these design techniques It will become very easy for you to approach a problem by identifying which technique to apply to solve that correctly and efficiently.

0. Complexity analysis

1. Recursion is the base of any algorithm design

2. Backtracking

3. Divide and Conquer

4. Greedy algorithms

5. Dynamic programming

And WE WILL WRITE THE CODE LINE BY LINE IN JAVA !!

By the end of this course –

    1. You will understand how to design algorithms

    2. A lot of coding practice and design live problems in Java

    3. Algorithm Complexity analysis

If you are preparing for your coding Interview or doing competitive programming This course will be a big help for you.

THRILLED? I welcome you to the course and I am sure this will be fun!!

If it does not – It comes with a 30 Days money-back guarantee so don’t think twice to give it a shot.

Happy Learning

Basics>Strong;

Who this course is for:

• Looking to learn to design algorithms
• Want to develop how to think solve algo problems
• Code Famous Algorithms in Java

Requirements

• Basic Java – Loops and Conditions

Last Updated 1/2021

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Algorithms in Java - Tutorial

1.1. motivation, 1.2. what is an algorithm, 1.3. complexity, 2.1. quicksort, 2.2. mergesort, 2.3. standard java array sorting, 2.4. testing the sort algorithm, 3.2. typical graph problems.

Algorithms in Java. This article describes some very common algorithm in Java.

1. Algorithm

It could be argued that for most problems someone else has already written an implementation of an algorithm which solves this problem. So rather by copying the implementation and using it you can also solve the problem. While this argumentation is true and while code reuse is important, I believe that having a sound understanding of the basis algorithms for standard problems helps to solve other analytical problems. So please see this article as a kind of brain teaser. Understanding algorithms can be very rewarding.

In addition everybody has to sort Arrays or Lists, so I believe it is important to at least understand the complexity for this problem and the available implementations of these.

An algorithm describes a way of solving a specific problem.

The complexity of an algorithm is dependent on the problem and on the algorithm itself. It can be described using the Big-O notation. An algorithm is said to have a complexity of O(n) if the runtime of solving a problem with this algorithm, depends on the number of elements (n) of this problem. For example a sort algorithm for a list with n elements is said to have the complexity of O(n^2) if the runtime of this algorithm increases exponentially with the number of elements.

A problem is said to be NP-hard (nondeterministic polynomial-time hard) if no algorithm exists which solves the problem in polynomial time.

2. Sort in Java

Sort algorithms are ordering the elements of a list according to a certain order. For the Java examples I will assume that we are sorting an array of integers. The examples for this chapter will be created in a Java project "de.vogella.algorithms.sort". The sorting algorithm will implement the following interface.

Quicksort is a fast, recursive, non-stable sort algorithm which works by the divide and conquer principle. It has in average O(n log (n)) and in the worst case O(n2) complexity. Quicksort selects first a pivot elements. It then divides the elements of the list into two lists based on this pivot element. Every element which is smaller than the pivot element is placed in the left list and every element which is larger is placed in the right list. If an element is equal to the pivot element then it can go in any list. After this sorting the algorithm is then called recursively for the left and right list. If a list contains only one (or zero) elements then it sorted. Quicksort does not copy any data it just exchanges the values in the array therefore it does not consume a lot of memory (only on the call stack for the recursive call of the function. The disadvantages of Quicksort is that it does not work well on already sorted lists or an lists with lots of similar values.

Mergesort is a fast, recursive, stable sort algorithm which works by the divide and conquer principle. The algorithm has a complexity of O(n log (n)). Similar to Quicksort the list of elements which should be sorted is divided into two lists. These lists are sorted independently and then combined. During the combination of the lists the elements are inserted (or merged) on the correct place in the list. Merge can be implemented in several ways.

The simplest way of merge is the following :

Assume the size of the left array is k, the size of the right array is m and the size of the total array is n (=k+m).

Create a helper array with the size n

Copy the elements of the left array into the left part of the helper array. This is position 0 until k-1.

Copy the elements of the right array into the right part of the helper array. This is position k until m-1.

Create an index variable i=0; and j=k+1

Loop over the left and the right part of the array and copy always the smallest value back into the original array. Once i=k all values have been copied back the original array. The values of the right array are already in place.

Compared to quicksort the mergesort algorithm puts less effort in dividing the list but more into the merging of the solution.

Standard mergesort does require a copy of the array although there are (complex) implementations of mergesort which allow to avoid this copying.

Java offers a standard way of sorting Arrays with Arrays.sort(). This sort algorithm is a modified quicksort which shows more frequently a complexity of O(n log(n)). See the Javadoc for details.

Here is a small unit test which you can use to test the sort algorithms.

A graph is made out of nodes and directed edges which define a connection from one node to another node. A node (or vertex) is a discrete position in a graph. Edges can be directed or undirected. Edges have an associated distance (also called costs or weight). The distance between two nodes a and b is labeled as [a,b]. The mathematical description for graphs is G= {V,E}, meaning that a graph is defined by a set of vertexes (V) and a collection of edges. The "order" of a graph is the number of nodes. The "size" of a graph is the number of edges.

Typical graph problems are:

finding the shortest path from a specific node to another node

finding the maximum possible flow through a network where each edge has a pre-defined maximum capacity (maximum-flow minimum-cut problem)

4. Links and Literature

Excellent Java Book with a very good part on Algorithms and Data Structures

German Page over Algorithms with an implementation in Java

Sort Algorithms described

If you need more assistance we offer Online Training and Onsite training as well as consulting

See License for license information .

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Introduction to Algorithms

Complexity Analysis

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Data Structures and Algorithms with Java

This course, “Data Structures and Algorithms with Java” is designed to provide an in-depth understanding of data structures and algorithms and how to implement them using Java. Whether you’re a beginner or an experienced programmer, this course will provide you with the knowledge and skills you need to become proficient in data structures and algorithms.

Throughout the course, you’ll learn about a wide range of data structures including arrays, stacks, queues, linked lists, skip lists, hash tables, binary search trees, Cartesian trees, B-trees, red-black trees, splay trees, AVL trees, and KD trees. Additionally, you’ll learn about a wide range of algorithms such as Quicksort, Mergesort, Timsort, Heapsort, bubble sort, insertion sort, selection sort, tree sort, shell sort, bucket sort, radix sort, counting sort, and cubesort.

As you progress through the course, you’ll also learn about algorithm design techniques such as greedy algorithms, dynamic programming, divide and conquer, backtracking, and randomized algorithms. To help you apply and practice the concepts you learn, the course includes hands-on exercises and examples. The course will also cover the Time and Space Complexity of the algorithm and Data Structures, so that you can understand the trade-offs of choosing one over the other.

By the end of the course, you’ll have a solid understanding of data structures and algorithms and how to use them effectively in Java. This course is perfect for anyone who wants to improve their skills as a developer or prepare for a career in computer science or data science.

If you’re ready to begin your journey towards mastering data structures and algorithms with Java, this course is perfect for you, Sign up now and start your journey towards mastering data structures and algorithms with Java.

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Yasin Cakal

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Introduction to Algorithm Design Techniques

Understand different algorithm design techniques with the help of relevant examples.

Exhaustive search algorithms

• Branch-and-bound algorithms
• Greedy algorithms
• Dynamic programming algorithms
• Recursive algorithms
• Divide-and-conquer algorithms
• Randomized algorithms

Over the last half a century, computer scientists have discovered that many algorithms share similar ideas, even though they solve very different problems. For now, we’ll mention the most common algorithm design techniques so that future examples can be categorized in terms of the algorithm’s design methodology.

To illustrate the design techniques, we’ll consider a very simple problem that plagued nearly everyone before the era of mobile phones, when people used cordless phones. Suppose your cordless phone rings, but you’ve misplaced the handset somewhere in your home. How do you find it? To complicate matters, you’ve just walked into your home with an armful of groceries, and it’s dark out, so you cannot rely solely on eyesight.

In an exhaustive search, or brute force, the algorithm examines every possible alternative to find one particular solution.

Level up your interview prep. Join Educative to access 70+ hands-on prep courses.

Algorithms in Java :Live problem solving & Design Techniques

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• Algorithm Design in Computer Systems
• Algorithm Design Techniques
• How to Code a Algo in Java
• Calculate time and space complexity
• Dynamic Programming, Greedy, D&C and Much more

Algorithm Design Techniques: Live problem-solving in Java

Algorithms are everywhere. One great algorithm applied sensibly can result in a System like GOOGLE!

Completer scientists have worked from 100s of years and derived some of the …

Completer scientists have worked from 100s of years and derived some of the techniques that can be applied to write and design algorithms.

So Why to reinvent the wheel ??

Let’s go through some of the most famous algorithm design techniques in this course.

Once you will come to know these design techniques It will become very easy for you to approach a problem by identifying which technique to apply to solve that correctly and efficiently.

0. Complexity analysis

1. Recursion is the base of any algorithm design

2. Backtracking

3. Divide and Conquer

4. Greedy algorithms

5. Dynamic programming

And WE WILL WRITE THE CODE LINE BY LINE IN JAVA !!

By the end of this course -

1. You will understand how to design algorithms

2. A lot of coding practice and design live problems in Java

3. Algorithm Complexity analysis

If you are preparing for your coding Interview or doing competitive programming This course will be a big help for you.

THRILLED? I welcome you to the course and I am sure this will be fun!!

If it does not - It comes with a 30 Days money-back guarantee so don’t think twice to give it a shot.

Happy Learning

Basics>Strong;

Learn Java practically and Get Certified .

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The Java collections framework provides various algorithms that can be used to manipulate elements stored in data structures.

Algorithms in Java are static methods that can be used to perform various operations on collections.

Since algorithms can be used on various collections, these are also known as generic algorithms .

Let's see the implementation of different methods available in the collections framework.

1. Sorting Using sort()

The sort() method provided by the collections framework is used to sort elements. For example,

Here the sorting occurs in natural order (ascending order). However, we can customize the sorting order of the sort() method using the Comparator interface .

To learn more, visit Java Sorting .

2. Shuffling Using shuffle()

The shuffle() method of the Java collections framework is used to destroy any kind of order present in the data structure. It does just the opposite of the sorting. For example,

When we run the program, the shuffle() method will return a random output.

The shuffling algorithm is mainly used in games where we want random output.

3. Routine Data Manipulation

In Java, the collections framework provides different methods that can be used to manipulate data.

• reverse() - reverses the order of elements
• fill() - replace every element in a collection with the specified value
• copy() - creates a copy of elements from the specified source to destination
• swap() - swaps the position of two elements in a collection
• addAll() - adds all the elements of a collection to other collection

For example,

Note : While performing the copy() method both the lists should be of the same size.

4. Searching Using binarySearch()

The binarySearch() method of the Java collections framework searches for the specified element. It returns the position of the element in the specified collections. For example,

Note : The collection should be sorted before performing the binarySearch() method.

To know more, visit Java Binary Search .

5. Composition

• frequency() - returns the count of the number of times an element is present in the collection
• disjoint() - checks if two collections contain some common element

6. Finding Extreme Values

The min() and max() methods of the Java collections framework are used to find the minimum and the maximum elements, respectively. For example,

Table of Contents

• Sorting Using sort()
• Shuffling Using shuffle()
• Routine Data Manipulation
• Searching Using binarySearch()
• Composition
• Finding Extreme Values

Solving Real-World Problems Using Appropriate Algorithm Design Techniques

Algorithms are at the heart of problem-solving in computer science. They act as step-by-step instructions for solving a specific problem efficiently. From searching and sorting algorithms to graph algorithms and machine learning algorithms, they are used to solve a wide range of real-world problems. In this article, we will explore how appropriate algorithm design techniques can be employed to tackle real-world problems effectively.

Understanding Real-World Problems

Real-world problems are complex and often involve large data sets. It is crucial to deeply understand the problem at hand before attempting to solve it with an algorithm. This involves analyzing the problem's requirements, identifying the input and output constraints, and considering any specific requirements or limitations.

Selecting Appropriate Algorithm Design Techniques

Once we have a clear understanding of the problem, we can start selecting appropriate algorithm design techniques. There are several commonly used algorithm design techniques, including:

Brute Force

The brute force technique involves trying out all possible solutions and selecting the one that meets the problem's requirements. Although this approach can be time-consuming, it is often a good starting point for solving real-world problems.

Greedy Algorithms

Greedy algorithms make locally optimal choices at each step with the hope of reaching a global optimum. They are useful for solving optimization problems where making the best decision at each step leads to the overall best solution. For example, the Dijkstra's algorithm for finding the shortest path in a graph is based on the greedy approach.

Divide and Conquer

The divide and conquer technique breaks down a complex problem into smaller, more manageable subproblems and solves them individually. The solutions to the smaller subproblems are then combined to obtain the solution to the original problem. The merge sort and quicksort algorithms are classic examples of divide and conquer techniques.

Dynamic Programming

Dynamic programming is used to solve problems by breaking them down into overlapping subproblems and solving each subproblem only once. The solutions to the subproblems are stored in a table and can be reused to solve larger problems. The famous Fibonacci sequence calculation using dynamic programming is one such example.

Optimizing Algorithms

Efficiency is a crucial aspect of solving real-world problems using algorithms. Optimizing algorithms can significantly improve their performance. There are various ways to optimize an algorithm, including:

• Analyzing the time complexity and space complexity to identify bottlenecks and areas for improvement.
• Employing data structures that are best suited for the problem at hand, such as hash tables, trees, or graphs.
• Utilizing algorithmic techniques like memoization, pruning, or parallelization to enhance execution speed.

Implementing Algorithms Using Java

Java is a widely used programming language for implementing algorithms due to its robustness and extensive libraries. When implementing algorithms in Java, it is essential to leverage the language's built-in features and libraries effectively.

Building real-world problem-solving algorithms using Java involves writing clear, modular, and well-documented code. It is recommended to follow best practices, such as employing object-oriented programming principles, using design patterns, and conducting thorough testing.

Algorithms are powerful tools for solving real-world problems efficiently. By employing suitable algorithm design techniques, understanding the problem domain deeply, and optimizing the algorithms, we can tackle even the most complex problems effectively. With Java as a programming language, we can implement these algorithms accurately and efficiently. So, embrace the algorithm design techniques and start solving real-world problems with confidence using Java.

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Enhancing Problem-Solving Skills in Java Programming: A Comprehensive Guide

Avinash bidkar, introduction.

Problem-solving skills lie at the heart of successful Java programming. In the realm of software development, the ability to dissect complex challenges, design efficient solutions, and implement them through code is paramount. This article delves into the art of honing problem-solving skills within the context of Java programming. By combining logical thinking, algorithmic strategies, and effective coding practice, developers can elevate their problem-solving skills in Java programming and create elegant, robust solutions.

Understanding Problem-Solving in Java Programming

Problem-solving in the realm of Java programming refers to the systematic approach of identifying, analyzing, and solving coding challenges. In a software developer course, you’ll learn that it involves breaking down intricate problems into manageable components and devising logical strategies to tackle them. Logical thinking and algorithmic understanding play a pivotal role in this process, allowing developers to construct well-structured and optimized solutions. Such skills are indispensable for crafting software that is not only functional but also efficient.

Guidelines for Enhancing Problem-Solving Skills

Here’s a rundown of the guidelines to enhance your problem-Solving skills.

• Breaking down complex problems: Start by breaking down a complex problem into smaller sub-problems. This approach simplifies the overall challenge and makes it easier to develop individual solutions for each component.
• Developing a systematic approach: Establish a systematic approach to problem-solving. Define the problem, identify the required inputs and desired outputs, and outline the steps needed to bridge the gap.
• Understanding before coding: Resist the urge to immediately dive into coding. Spend ample time understanding the problem statement, its nuances, and potential edge cases. A solid understanding is the foundation of an effective solution.
• Utilizing pseudocode and flowcharts: Before writing actual code, create pseudocode or flowcharts to outline the logical flow of your solution. This helps in visualizing the process and identifying potential flaws early on.
• Incremental development and testing: Build your solution incrementally, testing each component as you progress. This approach allows you to catch errors early and refine your solution iteratively.

Effective Coding Practice for Problem-Solving

Regular coding practice is essential for honing problem-solving skills. Engage in a variety of coding challenges that span different difficulty levels and problem domains. Online coding platforms, such as LeetCode, HackerRank, and CodeSignal, offer a wealth of practice problems and allow you to benchmark your skills against a global community of programmers. Mastering problem-solving skills in Java programming involves a combination of understanding the language's features, algorithms, and data structures, as well as practicing systematic approaches to tackling challenges.

Applying Algorithms for Efficient Solutions

Algorithms are step-by-step instructions for solving a specific problem. Learning and implementing various algorithms can greatly improve your problem-solving capabilities. Start by understanding the fundamental algorithms such as sorting, searching, and graph traversal. As you become more comfortable, move on to more advanced algorithms like dynamic programming, greedy algorithms, and divide-and-conquer techniques. Regularly practicing these algorithms through coding exercises will help you develop a toolkit of strategies to approach different types of problems effectively.

Strengthening Logical Thinking Abilities

Logical thinking is the foundation of problem-solving. It involves breaking down complex problems into smaller, manageable parts and understanding the relationships between different elements. To enhance your logical thinking skills, practice solving puzzles, brain teasers, and mathematical problems. Additionally, try to analyze the structure of various programming challenges and identify patterns and dependencies. Cultivating this skill will enable you to devise creative and efficient solutions to intricate programming problems.

Learning from Real-world Examples

Real-world examples provide practical insights into applying problem-solving skills in Java programming. Let's explore a few examples:

Example 1: Finding the factorial of a number: Break down the problem into smaller steps, creating a loop to multiply consecutive integers.

Example 2: Implementing binary search: Utilize the divide and conquer strategy to narrow down the search space and efficiently find the target element.

Example 3: Solving the Fibonacci sequence: Employ dynamic programming to optimize recursive calculations and generate Fibonacci numbers efficiently.

Overcoming Common Challenges

When solving programming problems, you're likely to encounter common challenges such as bugs, runtime errors, and inefficiencies. Embrace these challenges as opportunities to learn. Debugging is a crucial aspect of problem-solving. Cultivate a systematic approach to identifying and fixing errors by using tools like debugging environments and print statements. Additionally, optimize your code by analyzing time and space complexity, identifying bottlenecks, and making informed decisions to improve efficiency.

Putting Problem-Solving Skills to the Test

Practicing problem-solving skills is essential for improvement. Utilize coding platforms like LeetCode, HackerRank, and Codeforces to access a wide range of programming challenges. These platforms often categorize problems by difficulty, allowing you to gradually progress from easy to more complex tasks. Set aside dedicated time for consistent practice, and challenge yourself by attempting problems that initially seem daunting.

Collaborate with fellow programmers, engage in coding competitions, and participate in online forums to gain exposure to different problem-solving approaches.

An essential talent for Java programmers is the ability to solve problems. Developers can become skilled problem solvers by following logical rules, practicing code often, learning algorithms, and cultivating logical thought. The secret to maximizing the potential of problem-solving abilities is constant learning combined with practical experience. As you set out on this path, keep in mind that every obstacle you overcome will help you get closer to mastering Java programming and being able to create unique, effective solutions.

How can I improve my problem-solving skills in Java programming?

Enhance your problem-solving skills in Java by practicing algorithmic challenges, breaking down complex problems into smaller tasks, and leveraging data structures effectively. Regular coding practice and participating in coding competitions can also contribute to your skill development.

What strategies can I employ to tackle challenging Java programming problems?

Approach difficult Java programming problems step by step, analyze the problem requirements thoroughly, devise a clear plan, and implement it incrementally. Utilize debugging tools and collaborate with peers to gain different perspectives on problem-solving techniques.

Are there any specific resources for practicing Java problem-solving?

Numerous online platforms offer Java programming challenges and exercises, such as LeetCode, HackerRank, and Codeforces. These resources provide a range of problems to solve, helping you refine your problem-solving skills and Java proficiency.

How do I optimize my Java code for better problem-solving outcomes?

Optimize Java code by choosing efficient data structures, minimizing unnecessary iterations, and employing dynamic programming or memoization techniques when appropriate. Regularly review and refactor your code to improve its clarity and performance.

Q. Can problem-solving in Java programming benefit my overall programming proficiency?

Enhancing your problem-solving skills in Java translates to improved programming proficiency in general. The analytical mindset and structured problem-solving techniques you develop will prove valuable across various programming languages and domains.