feat: add Interval List Intersections explanation

- Two pointers algorithm approach
- Comprehensive problem analysis
- Multiple approaches and optimizations
- Edge cases and applications

Author: TechnoBlogger14o3

neetcode-150/intervals/interval-list-intersections/explanation.md

@@ -0,0 +1,83 @@
+# Interval List Intersections
+
+## Problem Statement
+
+You are given two lists of closed intervals, `firstList` and `secondList`, where `firstList[i] = [starti, endi]` and `secondList[j] = [startj, endj]`. Each list of intervals is pairwise disjoint and in sorted order.
+
+Return the intersection of these two interval lists.
+
+A closed interval `[a, b]` (with `a <= b`) denotes the set of real numbers `x` with `a <= x <= b`.
+
+The intersection of two closed intervals is a set of real numbers that are empty or represented as a closed interval. For example, the intersection of `[1, 3]` and `[2, 4]` is `[2, 3]`.
+
+## Examples
+
+**Example 1:**
+```
+Input: firstList = [[0,2],[5,10],[13,23],[24,25]], secondList = [[1,5],[8,12],[15,24],[25,26]]
+Output: [[1,2],[5,5],[8,10],[15,23],[24,24],[25,25]]
+```
+
+## Approach
+
+### Method 1: Two Pointers (Recommended)
+1. Use two pointers to traverse both lists
+2. Find intersection of current intervals
+3. Move pointer of interval that ends first
+4. Most efficient approach
+
+**Time Complexity:** O(m + n) - Two pointers
+**Space Complexity:** O(1) - In-place modification
+
+### Method 2: Brute Force
+1. Check all pairs of intervals
+2. Less efficient than two pointers approach
+
+**Time Complexity:** O(m * n) - Nested loops
+**Space Complexity:** O(1) - No extra space
+
+## Algorithm
+
+```
+1. Initialize i = 0, j = 0
+2. While i < m and j < n:
+   a. Find intersection of intervals[i] and intervals[j]
+   b. If intersection exists: add to result
+   c. Move pointer of interval that ends first
+3. Return result
+```
+
+## Key Insights
+
+- **Two Pointers**: Efficiently traverse both lists
+- **Local Optimum**: Find intersection of current intervals
+- **Global Optimum**: All intersections found
+- **Space Optimization**: Use only necessary space
+
+## Alternative Approaches
+
+1. **Brute Force**: Check all pairs
+2. **Sweep Line**: Use sweep line algorithm
+3. **Binary Search**: Use binary search for efficiency
+
+## Edge Cases
+
+- Empty lists: Return empty list
+- No intersections: Return empty list
+- All intersections: Return all intervals
+- Single intersection: Return that intersection
+
+## Applications
+
+- Interval algorithms
+- Scheduling problems
+- Algorithm design patterns
+- Interview preparation
+- System design
+
+## Optimization Opportunities
+
+- **Two Pointers**: Most efficient approach
+- **Space Optimization**: O(1) space complexity
+- **Linear Time**: O(m + n) time complexity
+- **No Extra Space**: Use only necessary space