Dynamic Programming Solution for Stable Binary Arrays

Continuing my C++ learning journey, I’ve now moved into some of the most powerful and practical concepts used in real-world development. In this phase, I focused on: • File Handling – reading from and writing to files using file streams • Streams – understanding input/output operations with cin, cout, and file streams • Generic Programming – writing flexible and reusable code using templates • Function Templates – creating type-independent functions • Class Templates – designing generic classes • STL (Standard Template Library):     – Containers (vector, list, map, etc.)     – Algorithms (sorting, searching, etc.)     – Iterators (traversing containers efficiently) These concepts provided a clearer understanding of how to write scalable, reusable, and efficient C++ programs using built-in libraries and generic techniques. Gradually progressing towards mastering C++ with consistent learning and hands-on practice. #CPP #STL #Programming #SoftwareDevelopment #LearningJourney

Today, I tackled a classic dynamic programming problem — Minimum Difficulty of a Job Schedule. This challenge really tested my understanding of: - Breaking problems into subproblems (partitioning jobs across days) - Optimizing brute force recursion using memoization - Thinking carefully about state definition (index, days left) Initially, I made a common mistake by trying to accumulate results greedily instead of exploring all valid partitions. Once I corrected that and properly defined the recurrence: 👉 current_day_max + solve(remaining_jobs, remaining_days) things started to click. Key takeaways include: - Always validate your recurrence before optimizing - DP is all about choosing the right state and transitions - Small indexing mistakes (i+1 vs ind+1) can completely break logic This was a valuable exercise in debugging and refining my dynamic programming approach. #LeetCode #DataStructures #Algorithms #DynamicProgramming #ProblemSolving

Solved a great DP problem today: Count Binary Strings Without Consecutive 1’s. Given a length n, the task is to count all distinct binary strings such that no two 1s are adjacent. Example: n = 3 → 000, 001, 010, 100, 101 → 5 n = 2 → 00, 01, 10 → 3 The key insight: 1. Strings ending with 0 can be formed by appending 0 to all valid strings of previous length. 2. Strings ending with 1 can only be formed by appending 1 to strings that previously ended with 0. This leads to a simple DP relation: 1. zero[i] = zero[i-1] + one[i-1] 2. one[i] = zero[i-1] So the pattern follows a Fibonacci-style sequence, and the problem can be solved in: O(n) time and O(1) space I enjoy how problems like this look simple at first, but the right state definition makes them very elegant. #DataStructures #Algorithms #DynamicProgramming #Coding #ProblemSolving #Cpp #Programming #SoftwareDevelopment

Built a custom string handling class in C++ from scratch — without using std::string. This project focuses on: • Dynamic memory management using new[] and delete[] • Implementation of the Rule of Three (Destructor, Copy Constructor, Copy Assignment) • Manual string manipulation algorithms (reverse, case conversion, word counting) • Operator overloading for intuitive usage (+, +=, [], (), comparison operators) The goal was to deeply understand how strings work internally rather than relying on built-in abstractions. A great exercise in mastering memory management, object-oriented programming, and low-level string operations in C++. #cpp #programming #softwareengineering #oop #learning #developers

💻In this new Ansys Innovation Space blog, we’ll take a closer look at one of the key improvements in Scade One 2026 R1: the Swan code generator. This tool converts Swan models into efficient, portable C code, and with the latest release, it’s now even better at optimizing performance of the generated code. Read more at: https://bit.ly/4c5MUeC #programmer #programming #modelbaseddedign

Is Dynamic Programming Scary?? 😱 Let's understand it.... But the core idea is simple: if the same smaller problem appears again and again, solve it once, store it, and reuse it. That is why DP is powerful. It is not about writing complex code first. It is about learning to spot repetition, define the right state, and build bigger answers from smaller ones. In this visual, I explained: what Dynamic Programming really means, why repeated subproblems matter, and how memoization and tabulation differ. Memoization = top-down Tabulation = bottom-up Once that difference becomes clear, DP starts feeling much more practical and much less scary. The goal is not to memorize solutions. The goal is to understand: what repeats, what to store, and how the final answer is built. Which DP problem helped you understand the concept for the first time? #DynamicProgramming #Algorithms #DataStructures #Programming #SoftwareEngineering #CodingInterview #ProblemSolving #DeveloperLearning #ComputerScience #TechEducation

🔥 DSA Practice Update Yesterday's LeetCode progress: ✅ LRU Cache ✅ LFU Cache ✅ Median of Two Sorted Arrays ✅ Kth Largest Element in a Stream ✅ Complement of Base 10 Integer Currently focusing on advanced data structures and system design patterns to strengthen my problem-solving skills. #LeetCode #DSA #Programming #SoftwareEngineer #CodingPractice

Panel Nonlinear Autoregressive Distributed Lag (ARDL) (Panel NARDL) Use pnardl (ardlverse) With (In) R Software https://ln.run/pnardl Panel Nonlinear Autoregressive Distributed Lag (ARDL) (Panel NARDL) Estimation Use pnardl With STATA 19 https://ristek.link/pnardl #rstudio #rsoftware #softwarer #stata #stata19 #olahdatasemarang #programming

Day 73/100 – DSA Challenge 🚀 Topic: Fibonacci using Dynamic Programming Key Learning: Dynamic Programming is an algorithm design technique used to solve complex problems by breaking them into smaller subproblems, solving each only once, and storing the results to avoid repeated calculations. It is mainly used for optimization problems such as finding minimum cost, maximum profit, shortest path, etc. In many problems, the same subproblem is solved again and again. Dynamic Programming stores the result of solved subproblems and reuses them instead of recomputing. This improves efficiency. Example: While calculating Fibonacci numbers recursively, many values repeat. DP stores them and avoids repeated calculations. GitHub: <https://lnkd.in/dtek96E3> #100DaysOfDSA #ProblemSolving #LinkedInLearning #clanguage #coding #programming #developer #softwareengnieer #datastructure

Built a C program to print number patterns 💻 Used nested loops to generate the output. Outer loop controls rows and inner loop prints numbers. Printed increasing number triangle pattern. Improved understanding of loops in C. Practiced logic building using simple patterns. Focused on clean and structured code. Pattern programs boost problem-solving skills. Learning step by step with consistency 🚀 Strong basics lead to better programming 🔥 #CLanguage #CProgramming #CodingJourney #LearnToCode #PatternProgramming #StudentDeveloper #LogicBuilding #ComputerScience #TechSkills #100DaysOfCode