Cracking Dynamic Programming with a 6-Step Framework

People often say that dynamic programming is difficult. However, some people find it straightforward to understand and apply. This suggest that those who consider it is hard is missing something. I believe the reasons usually fall in one of two categories:  1. They don't understand what DP is. 2. They haven't mastered the prerequisites of DP. Let's consider the first point. I have always wondered why this technique is named as dynamic programming, since this technique has nothing to do with this name. DP is also known as memoization (meaning memorization) and this term define the essence of dynamic programming. The whole idea of DP is to avoid recomputing the results which already been calculated. We use a cache to store results for certain inputs then reuse them whenever needed and that's it. The second source of confusions for beginners is that DP problems can be solved by different approaches. This initially confused me too because same problem could have completely different solutions. The commonly followed DP approaches are known as top-down and bottom-up approaches. Their implementations look very different but both ends with the same result just starting to solve the problem in opposite directions. I won't go into the details of these approaches here, since my goal is to stay focused on the main topic. Now let's move on to the second point. I started by solving some backtracking problems to understand the technique, which felt very challenging at first. When I grasped it, moving on to DP problems became much easier. I realized that, for top-down approach DP is essentially: backtracking + memoization. This means that those who don't understand backtracking will struggle to grasp DP. In other words, if we get the idea of backtracking then DP feels much more approachable and enjoyable to solve. Backtracking has itself prerequisites like recursion, decision tree, base case checking, problem decomposition, basic data structures. Mastering these concepts takes time, and missing even one can make you struggle in DP. At first glance, backtracking solutions may seem very straightforward so finding how to solve problems feels very easy but real challenge is in understanding why the solution works. Once you understand the 'why', approaching DP becomes much more intuitive. I think DP is a kind of beautiful mathematical magic because it can transform exponential time taking solution to linear.

Functional Programming Using Modern C++ ============================= Download : https://lnkd.in/dpHJQJfx Welcome to ”Functional Programming Using Modern C++”! This book is the culmination of decades of experience working with C++, a language that has been my trusted companion for over 30 years. Throughout my journey, I have witnessed the evolution of C++ from its early days to the powerful, modern language it is today. With the introduction of functional programming features in Modern C++ (C++11 and beyond), the language has taken a significant leap forward, enabling developers to write cleaner, more expressive, and more maintainable code. Functional programming is not just a paradigm; it is a mindset that encourages us to think differently about how we solve problems. By embracing immutability, pure functions, and higher-order abstractions, we can create software that is not only efficient but also easier to reason about and test. This book is designed to guide you through the principles of functional programming and demonstrate how they can be seamlessly integrated into Modern C++. Whether you are a seasoned C++ developer or someone exploring functional programming for the first time, this book aims to provide you with the tools and knowledge to harness the full potential of Modern C++. We will explore key concepts such as lambda expressions, ranges, monads, and more, all while keeping a practical focus on real-world applications. My goal is to make functional programming accessible and relevant to C++ developers. I hope this book inspires you to embrace these techniques and incorporate them into your projects, unlocking new levels of productivity and creativity. Thank you for joining me on this journey. Let’s dive into the world of functional programming with Modern C++ and discover how it can transform the way we write code. Happy coding!

[PDF] Exceptional C++ style: 40 new engineering puzzles, programming problems, and solutions Herb Sutter https://lnkd.in/egzUZ7zi Software "style" is about finding the perfect balance between overhead and functionality... elegance and maintainability... flexibility and excess. In Exceptional C++ Style, legendary C++ guru Herb Sutter presents 40 new programming scenarios designed to analyze not only the what but the why and help you find just the right balance in your software.Organized around practical problems and solutions, this book offers new insight into crucial C++ details and interrelationships, and new strategies for today's key C++ programming techniques - including generic programming, STL, exception safety, and more. You'll find answers to questions like: * What can you learn about library design from the STL itself? * How do you avoid making templated code needlessly non-generic? * Why shouldn't you specialize function templates? What should you do instead? * How does exception safety go beyond try and catch statements? * Should you use exception specifications, or not? * When and how should you "leak" the private parts of a class? * How do you make classes safer for versioning? * What's the real memory cost of using standard containers? * How can using const really optimize your code? * How does writing inline affect performance? * When does code that looks wrong actually compile and run perfectly, and why should you care? * What's wrong with the design of std::string?Exceptional C++ Style will help you design, architect, and code with style - and achieve greater robustness and performance in all your C++ software. digzon Software "style" is about finding the perfect balance between overhead and functionality... elegance and maintainability... flexibility and excess. In Exceptional C++ Style, legendary C++ guru Herb Sutter presents 40 new programming scenarios designed to analyze not only the what but the why and help you find just the right balance in your software.Organized around practical problems and solutions, this book offers new insight into crucial C++ details and interrelationships, and new strategies for today's key C++ programming techniques - including generic programming, STL, exception safety, and more. You'll find answers to questions like: * What can you learn about library design from the STL itself? * How do you avoid making templated code needlessly non-generic? * Why shouldn't you specialize function templates? What should you do instead? * How does exception safety go beyond try and catch statements? * Should you use exception specifications, or not? * When and how should you "leak" the private parts of a class? * How do you make classes safer for versioning? * What's the real memory cost of using standard containers? * How can using const really optimize your co...

Delegates in C#: The Hidden Gateway to Functional Programming What if I told you that C# has supported functional programming for years — and most developers use it without realizing it? When people think about functional programming, they usually think of languages like F# or JavaScript. But the truth is: C# quietly introduced functional concepts through delegates long ago — and they completely changed how we write flexible, modular code. What Is a Delegate? A delegate in C# is a type that represents a reference to a method. In simple terms: A delegate allows you to treat a method like data. That means you can: -Pass methods as parameters -Return methods from other methods -Store methods inside variables Delegates Enable Behavior as a Parameter Traditional programming focuses on data. Functional programming focuses on behavior. With delegates, you can write one reusable method and change its behavior dynamically. Example concept: Instead of writing: -GetEvenNumbers -GetOddNumbers -GetPositiveNumbers Delegates + Lambda Expressions = Functional Power Delegates became even more powerful when lambda expressions were introduced. Instead of writing long method definitions, we can define behavior inline. This enables: -LINQ queries -Fluent APIs -Clean event handling -Dynamic logic injection Without delegates, LINQ would not exist. Why This Matters in Real Projects Delegates help us: -Reduce code duplication -Follow the Open/Closed Principle -Improve modularity -Increase testability -Separate behavior from structure They allow systems to grow without rewriting core logic. Delegates are not just a feature. They are the foundation that allowed C# to evolve into a hybrid object-oriented and functional language. If you understand delegates deeply, you understand: -Events -LINQ -Asynchronous programming -Dependency injection patterns -And ultimately, modern C# design

Thought started with Backtracking and ended up with Dynamic Programming LeetCode 139. Word Break is a classic problem where a brute-force idea quickly turns inefficient. My first instinct was Backtracking—try splitting the string at every possible position and check if the substring exists in the dictionary. While this works conceptually, it leads to exponential time complexity because the same substrings get recomputed multiple times. So the idea evolves into Dynamic Programming, where we store intermediate results to avoid redundant work. Approach (DP) 1. Convert the wordDict list into a HashSet for O(1) lookup. 2. Create a boolean array dp of size n + 1, where: •dp[i] means the substring from index 0 to i can be segmented into valid dictionary words. 3. Initialize dp[0] = true because an empty string is always valid. 4. Compute the maximum word length in the dictionary to limit unnecessary substring checks. 5. For each position i from 1 → n: •Check previous positions j from i-1 down to max(0, i - maxLength). •If: •dp[j] is true (prefix is valid), and •s.substring(j, i) exists in the dictionary then mark dp[i] = true and break the loop. 6. The final answer is stored in dp[n]. 🧠 Key Insight Instead of recomputing substrings repeatedly (as in backtracking), DP builds the solution incrementally, remembering which prefixes can form valid words. ⏱ Time Complexity •Outer loop runs n times. •Inner loop runs at most maxLength times. Time Complexity: O(n × maxLength) Space Complexity: O(n) By limiting the substring checks using the maximum word length, we significantly reduce unnecessary computations and make the solution efficient.

C++ runs competitive programming. I chose Rust anyway? Honestly, it’s an unorthodox choice, and there are definitely times when it feels like a heavy trade-off. If your only metric for success is how fast you can type out a solution, Rust can be an uphill battle. There are real friction points: Having to constantly cast i32 to usize just to index an array can slow down your momentum. Fighting the borrow checker when you just want to write a quick, messy graph traversal takes practice. The standard I/O is notoriously wordy,writing .unwrap().parse() over and over is just straight up annoying. But despite all that, the learning experience has been incredible. Writing in Rust forces me to understand data and logic. Instead of losing half a contest to obscure double-pointer errors or use-after-free segfaults, the compiler catches the errors before the code even runs. It builds a rigorous discipline that pays off massively when you transition to building complex, large scale engineering projects outside of CP. To make Rust more viable for contests and to fix the verbosity issue, I’ve put together an open source I/O and algorithm template. It’s far from perfect, but it removes a lot of the friction. A custom Scanner that lets you just write let mut n: u64 = scan.next(); and focus on the logic. A lightweight BufRead setup (cp.rs) for standard inputs (10^4), and a heavy duty BufWriter setup (tmp2.rs) for massive constraints (10^6). Drop in functions for modular exponentiation, O(log N) integer square roots (to avoid float precision nightmares), and precomputed O(1) factorials for combinatorics. I’m sharing this to hopefully encourage more people to give Rust a try in CP. It’s a fantastic sandbox for mastering the language. On a related note, I’ll also be releasing a separate repository soon that documents my journey of implementing Data Structures and Algorithms in an idiomatic Rust style. It will be structured as a blog-based guide focusing heavily on memory safety and optimizations touching on everything from basic time complexities and sorting arrays to graphs and potentially even Entity Component Systems (ECS). I'll share more details on that soon! I would love to hear thoughts from the community,especially from C++ veterans or fellow Rustaceans. repo -> https://lnkd.in/gjPyccUH #Rust #CompetitiveProgramming

It has only been three or four months since I began using LLM-driven programming as my primary way of building software. After I became comfortable with it, I found myself reflecting on how I started. Between 2004 and 2008, during my bachelor's degree, we began with assembly language on 8085 and 8086 kits. The environments were constrained. The instruction sets were small. We entered instructions directly and watched the processor respond. It forced you to think in terms of registers, memory locations, and execution flow. Alongside that, we learned C. Memory allocation and deallocation were explicit. In the operating systems lab, we implemented semaphores and scheduling. C hid some hardware detail compared to assembly, but it still required discipline and precision. Then came Java. With garbage collection and the virtual machine, memory management faded into the background. The focus shifted to modeling systems through classes and objects, and the runtime handled more of the machinery. After graduation, I worked with C#, then moved to Ruby and Rails. Rails removed much of the repetitive setup through conventions and generators, which meant more time on business logic and user needs. Later, with JavaScript frameworks like React, the pattern continued: more abstraction, more focus on behavior, less on plumbing. Now I work with LLM-driven programming. I describe features and constraints in natural language. The model generates scaffolding, tests, and patterns that I have written many times before. My time shifts toward clarifying intent and reviewing output. This is another step upward in abstraction, but every step upward narrows something. When we moved from assembly to C, we gave up direct instruction-level control. With Java, we gave up manual memory management. With Rails, we accepted convention over explicit configuration. With LLMs, we give up writing each line ourselves. And with each layer, we lose a little visibility. Higher abstraction makes systems easier to build but harder to fully see. The distance between intent and execution grows. Prompts are easier to write than code, but harder to reason about. Code is slower to write, but easier to trace and verify. Determinism gives way to probability. Programming has always moved between control and convenience. Each cycle asks us to trade some clarity for speed. This time, the trade feels larger. We are no longer just abstracting syntax or memory. We are abstracting authorship itself. And that takes time to accept.

🚀 Understanding the Structure of a Method in Programming A method is a block of code designed to perform a specific task. It helps developers write clean, reusable, and organized programs. 📌 Basic Structure of a Method: 1️⃣ Access Modifier – Defines the visibility of the method (public, private, protected). 2️⃣ Return Type – Specifies the type of value the method returns (int, string, void, etc.). 3️⃣ Method Name – The name used to call the method. 4️⃣ Parameters – Inputs passed to the method (optional). 5️⃣ Method Body – The set of statements that perform the required task. 💡 Example: public int addNumbers(int a, int b) { return a + b; } Methods improve code reusability, readability, and maintainability, which are essential skills for every developer. #Programming #Coding #SoftwareDevelopment #Java #CSharp #Learning #Developers #CodingJourney

🚀 Task.WhenAll vs Task.WhenAny in C# Know When to Use Which One of the most impactful decisions in async programming isn't whether to use tasks, it's knowing how to coordinate them. Let's break down these two powerful methods that can improve your application's performance. ⚡Task.WhenAll — Run Everything in Parallel Use Task.WhenAll when you have multiple independent tasks and you need all of their results before moving forward. Real-World Example: Loading a user profile page that requires: → UserBasicInfo → UserOrders → UserSettings ❌ Wrong Approach (Sequential): var info = await GetUserBasicInfo(); // 1s var orders = await GetUserOrders(); // 1s var settings = await GetUserSettings(); // 1s // Total: ~3 seconds 😬 ✅ Right Approach (Parallel): var infoTask = GetUserBasicInfo(); var ordersTask = GetUserOrders(); var settingsTask = GetUserSettings(); await Task.WhenAll(infoTask, ordersTask, settingsTask); // Total: ~1 second 🚀 Key Takeaways: ✔ All tasks run simultaneously ✔ Waits until every task is complete ✔ If any task throws an exception, it propagates ✔ Best for: Parallel API calls, independent DB queries, concurrent file reads. 🎯 Task.WhenAny -> First One Wins Use Task.WhenAny when you have multiple tasks running and you only care about whichever completes first. Real-World Example: Fetching the same data from two different servers,take whichever responds faster: var server1Task = GetDataFromServer1(); var server2Task = GetDataFromServer2(); var firstCompleted = await Task.WhenAny(server1Task, server2Task); var result = await firstCompleted; // Fastest server wins! 🏆 Key Takeaways: ✔ Returns as soon as the first task completes ✔ Remaining tasks continue running in the background ✔ Perfect for redundancy and fallback patterns ✔ Best for: Competing API calls, timeout handling, fastest-response scenarios 📊 Side-by-Side Comparison: Task.WhenAll -> Waits for All tasks Use case -> Need all results On failure -> One fail = exception Pattern -> Parallel execution Task.WhenAny -> First task Use case -> Need fastest result On failure -> First success is enough Pattern -> Race condition pattern The Bottom Line: If you are still writing sequential await calls for independent operations, you are leaving serious performance on the table. → Need all results? → Task.WhenAll → Need the fastest result? → Task.WhenAny Master these two, and your async code will be faster, cleaner, and production-ready. 💪 #DotNet #CSharp #DotNetCore #SoftwareEngineering #BackendDevelopment #Programming #CodeQuality #CleanCode #DotNetDeveloper #100DaysOfCode #LINQ #AspNetCore #SoftwareArchitecture #TechTips #Developer

Day 65 of #100daysofcode Now that my mini-shopping list application is done, I want to implement object-oriented programming in my next project. I am going to be learning about Object-oriented programming at my @Codetrain class tomorrow. I decided to read up on it again, and so I found the YouTube video linked below. The teacher does a SPECTACULAR job of breaking down the need for object-oriented programming and how it works. I highly recommend this video for anyone who wants to learn! In a nutshell, Object-oriented programming is a programming paradigm (i.e a style of programming) where data is bundled up into these containers called “objects”. The characteristics describing the features of these objects are called “properties”, and the actions the objects can perform are stored in “methods”. Methods are basically functions FOUND IN objects. This paradigm is normally used in large applications that have many entities that have a lot of functionality and manipulate large amounts of data. An example of an object with properties and a method is found below. const human = { //properties name: "Ethan", gender: "Male", age: 19, //a method speak() { return I am ${this.name}. I am ${this.age} years old. } } console.log([human.name]; //OUTPUT: “Ethan” console.log(human.gender); //OUTPUT: “Male” console.log(human.gender); //OUTPUT: 18 human.speak(); //OUTPUT: 'I am Ethan. I am 19 years old.' Object-oriented programming is actually a topic I have read about before, back when I went through the book “Eloquent JavaScript”, but because I haven’t used it in a while, I have forgotten a lot of it 🙁. But my new project, which I will reveal in my next post, will apply the basic principles of object-oriented programming. Stay tuned! https://lnkd.in/dPGcsY4G