Top Interview Patterns

[!NOTE] This module explores the core principles of Top Interview Patterns, deriving solutions from first principles and hardware constraints to build world-class, production-ready expertise.

1. The Meta-Game

Coding interviews are not just about code. They are a role-playing game where you act as a “Senior Engineer” solving a problem with a peer. Your goal is to generate Signal.

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2. The 4 Pillars of Interviewing

Pillar	Focus	The Signal
1. Clarification	Constraint Hunting	“Does this candidate dive in blind, or do they de-risk the problem first?”
2. Algorithm	Verbal Design	“Can they communicate complex logic simply before solving it?”
3. Coding	Translation Speed	“Can they turn thoughts into clean, bug-free code efficiently?”
4. Verification	Self-Testing	“Do I need to find their bugs, or do they find them for me?”

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3. The Pattern Toolkit

Fixed Size: Find max sum of subarray size K.

1. Add element right.
2. If window size > K, remove element left.

Variable Size: Find smallest subarray with sum \ge S.

1. Expand right until condition met.
2. Shrink left to minimize while condition holds.

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4. Two Pointers

Use Case: Searching pairs in Sorted Arrays or reversing. Optimization: O(n) time, O(1) space.

Example: Two Sum (Sorted).

• left at 0, right at end.
• Sum too small? left++.
• Sum too big? right--.

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5. Fast & Slow Pointers (Tortoise & Hare)

Use Case: Cyclic Detection (LinkedList, Array).

• Slow moves 1 step. Fast moves 2 steps.
• If they meet, there is a Cycle.
• If Fast reaches null, no cycle.

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6. Monotonic Stack

Use Case: “Next Greater Element”, Histogram Area. Constraint: Keep stack sorted (increasing or decreasing).

• Before pushing X, pop all elements < X (for Next Greater).

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7. Code Example: Sliding Window (Variable)

Java

// Find min length subarray with sum >= target
public int minSubArrayLen(int target, int[] nums) {
  int left = 0, sum = 0, minLen = Integer.MAX_VALUE;

  for (int right = 0; right < nums.length; right++) {
    sum += nums[right];

    // Shrink window
    while (sum >= target) {
      minLen = Math.min(minLen, right - left + 1);
      sum -= nums[left];
      left++;
    }
  }
  return minLen == Integer.MAX_VALUE ? 0 : minLen;
}

Go

func minSubArrayLen(target int, nums []int) int {
  left, sum := 0, 0
  minLen := len(nums) + 1

  for right, val := range nums {
    sum += val

    for sum >= target {
      if right - left + 1 < minLen {
        minLen = right - left + 1
      }
      sum -= nums[left]
      left++
    }
  }

  if minLen > len(nums) { return 0 }
  return minLen
}

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8. Deep Dive Strategy Lab: Patterns

Intuition Through Analogy

Think of this chapter like solving under whiteboard time pressure. The goal is not to memorize a fixed trick, but to repeatedly answer:

1. What is the bottleneck?
2. Which constraint dominates (time, memory, latency, correctness)?
3. Which representation makes the bottleneck easier to eliminate?