Simulation & Modeling¶
Simulation and modeling techniques involve creating computational models to represent real-world scenarios or abstract problems. These methods are particularly useful for problems that require systematic exploration of solution spaces or optimization with specific constraints.
Techniques in This Category¶
| Technique | Description | Link |
|---|---|---|
| Two Pointer / Sliding Window | Efficient techniques for maintaining a dynamic range or window while processing data, commonly used for subarray problems. | Two Pointer / Sliding Window |
| Binary Search on Answer | An optimization technique that searches for the optimal parameter value using binary search, particularly useful when the problem has monotonic properties. | Binary Search on Answer |
| Greedy Algorithms | Algorithms that make locally optimal choices at each step, often leading to globally optimal solutions for certain types of problems. | Greedy Algorithms |
When to Use Simulation & Modeling¶
These techniques are effective when: - You need to explore a range of possible solutions - The problem has monotonic or predictable properties - Local optimization can lead to global optimization - Working with constrained optimization problems
Common Problem Types¶
Sliding Window Problems¶
- Maximum/Minimum Subarray: Finding optimal subarrays of fixed or variable size
- String Matching: Finding patterns or anagrams in strings
- Resource Allocation: Optimizing resource usage within constraints
Binary Search Applications¶
- Parameter Optimization: Finding optimal values for problem parameters
- Feasibility Testing: Determining if a solution exists for given constraints
- Range Queries: Finding values within specific ranges
Greedy Applications¶
- Scheduling: Optimizing task or resource scheduling
- Coin Change: Finding minimum number of coins for change
- Huffman Coding: Optimal data compression
- Minimum Spanning Tree: Finding optimal tree structures