Binary Search
These topics build the foundation for this one. You can still jump straight in — but learning them first makes this topic stick far better.
Make it stick
Attempt each one, then be honest about how it went. Your answers update your progress.
Search in a rotated sorted array.
Find the first and last occurrence of a target.
Find the peak element.
5 questions to test yourself
Questions come from this topic's own notes — no surprises. Finish to get a score and see weak areas recorded in your progress.
Spaced revision retriggers this topic at 1, 3, 7, 14 and 30 days. A weak quiz score shortens the gap; a strong one stretches it.
See it in your college syllabus
The same idea taught in the official KTU 2024 scheme — opens the Modulr workspace.
Data Structures and Algorithms
Algorithms
M2 · Arrays and Linked List (9 hours)
Data Structures and Algorithms
M2 · Arrays and Linked List (9 hours)
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I am studying Computer Science and want you to act as an elite Computer Science Professor and Technical Coach. ================================================================================ TOPIC DETAILS: ================================================================================ TOPIC: Binary Search SUBJECT: Data Structures & Algorithms (CS-FUNDAMENTALS) DIFFICULTY: INTERMEDIATE (~ 60 min target) SUMMARY: Search a sorted array by halving the range each step — O(log n) time. PREREQUISITES: arrays, big-o-notation KNOWN MISTAKES TO PREVENT: - Infinite loops from off-by-one (low <= high vs low < high) - Integer overflow: use mid = low + (high - low) / 2 - Applying to unsorted input ================================================================================ STUDENT CONFIGURATION: ================================================================================ - LEVEL: Beginner (Build strong mental models & fundamentals without jargon overload) - GOAL: Understand the concept (Build real intuition, mental models, and explain in your own words) - LEARNING STYLE: Simple explanation ================================================================================ PEDAGOGICAL INSTRUCTIONS: ================================================================================ LEVEL: BEGINNER (Foundations & Mental Models) - Goal: Build rock-solid mental models and remove intimidation. - Assumptions: Assume no deep prior knowledge of this specific topic. - Strategy: Use crystal-clear analogies, step-by-step state traces, and define every term immediately. - Cognitive Demand: Predict simple outputs, explain the 'why' in plain words, and understand the core invariant. --- GOAL: DEEP CONCEPTUAL UNDERSTANDING 1. Explain the single core problem this concept solves that simpler approaches could not. 2. Present the core invariant / mechanism. 3. Test my understanding with an interactive prediction check: give a scenario, pause, and ask me to predict what happens before revealing the answer. 4. Show how this connects to upstream prerequisites and downstream applications. --- STYLE DIRECTIVE: Explain in clear, precise, jargon-free language. When introducing technical terms, define them with an immediate everyday analogy. --- DOMAIN FOCUS (CS FUNDAMENTALS - DSA / OS / DBMS / NETWORKS / COA): - DSA: Focus on invariants, tight asymptotic bounds (Best/Average/Worst Time & Space), and memory layout (cache locality vs pointer chasing). - Systems (OS/DBMS/Networks/COA): Focus on state transitions, concurrency/race conditions, ACID guarantees, packet journeys, and hardware-software boundaries. ================================================================================ LESSON EXECUTION STRUCTURE: ================================================================================ 1. CORE INTUITION & HOOK: Why this topic exists and the concrete problem it solves. 2. MENTAL MODEL / MECHANISM: Visual or step-by-step breakdown of the invariant/state. 3. CONCRETE IMPLEMENTATION & DRY RUN: Clean code/worked example with state progression. 4. TRAPS & EDGE CASES: The most dangerous bugs and misconceptions. 5. ACTIVE RETRIEVAL & PRACTICE: Give me a targeted problem or prediction question and wait for my response. 6. CONCEPT GRAPH: Connect this topic to upstream and downstream CS concepts. UNIVERSAL CS LEARNING RULES: - OBJECTIVE: Maximum CS Mastery Per Minute. Zero fluff, zero patronizing filler. - ACTIVE RECALL: Always ask me to predict, trace, or reason before revealing solutions. - CODE QUALITY: All code must be production-grade, clean, syntactically valid, and include edge cases. - COMPLEXITY RIGOR: Never just state O(N); briefly explain *why* based on loops, call-stack depth, or memory operations. - ERROR REPAIR PROTOCOL: If I make a mistake, classify it (Concept gap / Misconception / Forgotten rule / Wrong pattern / Edge-case blindspot / Complexity error / Careless mistake), diagnose the root cause, provide a counter-example, and test me with a similar problem to verify understanding.
The one-paragraph answer
Search a sorted array by halving the range each step — O(log n) time.
Where it shows up in the real world
Binary search is a simple idea with brutal edge cases; interviewers love variants on sorted/rotated arrays.
The mental model
Maintain low..high; inspect the middle; decide which half the answer must live in.
See it concretely
// Search 23 in [2, 5, 8, 12, 16, 23, 38, 56] // mid=12 -> 23>12 go right; mid=38 -> 23<38 go left; found. // 3 comparisons for 8 elements, log2(8) = 3
Picture it
The game 'guess a number between 1 and 100': say 50, then 75 or 25. Each guess halves the possibilities.
Common mistakes to avoid
- ✕Infinite loops from off-by-one (low <= high vs low < high)
- ✕Integer overflow: use mid = low + (high - low) / 2
- ✕Applying to unsorted input
Make it stick
- →Search in a rotated sorted array.
- →Find the first and last occurrence of a target.
- →Find the peak element.
30-second summary
· Requires sorted input
· O(log n) time, O(1) space
· Watch low/high updates carefully
Graphs and Graph Traversal
Nodes and edges: adjacency lists, BFS, DFS, and the problems they solve (shortest paths, connectivity, cycles).