Solving Problems Guide
Problem Solving Workflow descriptions
Start with the problem statement:
- Understand the problem requirements and constraints.
- Identify the input and output expectations.
- Brainstorm edge cases.
- Determine input validation.
- Consider error handling.
- Note performance requirements.
- Note any specific algorithms or techniques required.
- Identify any external dependencies.
- Consider scalability and extensibility.
Assess the problem characteristics:
- Determine if the problem involves sorting, searching, graph traversal, optimization, etc.
- Consider if the problem involves any specific data structures.
- Mathematical calculations or formulas.
- Strings or text manipulation.
- Arrays or collections of data.
- Dates, times, or time-related operations.
- Input/output operations or interacting with external systems.
- Graphs, trees, or other specialized data structures.
- Searching or matching patterns within data.
- Optimizing performance or minimizing resource usage.
- Modeling real-world scenarios or simulations
Analyze the input size and efficiency requirements:
- Determine the expected input size (e.g., small, moderate, large).
- Evaluate the time and space complexity constraints.
- Acceptable time complexity of the solution
- Space complexity requirements in terms of memory usage.
- Trade-offs between time and space complexity.
- Upper limits of the expected input size.
- Scalability requirements in terms of input growth over time.
- response time or execution speed for the given input size
- available computational resources and memory constraints.
- requires an optimal or near-optimal solution
- constraints related to real-time processing or responsiveness.
Consider known algorithmic patterns:
- Reflect on algorithms commonly used for similar problem domains.
- Review familiar algorithmic techniques and patterns.
- Sorting, such as Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, Quick Sort, or Heap Sort.
- Searching, such as Linear Search, Binary Search, or Hashing.
- Graph algorithms like Breadth-First Search (BFS) or Depth-First Search (DFS).
- Graph problems, such as Dijkstra's algorithm or the Floyd-Warshall algorithm.
- Tree algorithms, including Binary Search Trees (BSTs), AVL Trees, Red-Black Trees, or Trie Trees.
- Dynamic programming problems, such as the Knapsack problem or Longest Common Subsequence.
- Pattern matching algorithms like the Rabin-Karp algorithm or the Knuth-Morris-Pratt algorithm.
- Network flow problems, such as the Ford-Fulkerson algorithm or the Edmonds-Karp algorithm.
- Optimization problems, such as the Greedy algorithm or the Simulated Annealing algorithm.
- Greedy algorithms: Consider algorithms like Kruskal's algorithm, Prim's algorithm, or the Coin Change problem.
- Backtracking algorithms: Look into algorithms like N-Queens, Sudoku Solver, or Subset Sum.
Evaluate algorithm options:
- Research and study different algorithms applicable to the problem.
- Examine their time complexity, space complexity, and suitability.
Consider trade-offs:
- Assess trade-offs between time and space complexity.
- Evaluate whether the problem prioritizes efficiency or simplicity.
Experiment and benchmark:
- Implement and test different algorithms on representative input data.
- Measure their performance (execution time, memory usage).
- Create sample input data
- Implement different algorithms
- Develop a benchmarking framework
- Varying input sizes
- Execution time
- Measure memory usage
- Repeat experiments multiple times
- Analyze and compare results
- Consider trade-offs
- Validate results with different inputs
- Document and report findings
Analyze and refine:
- Evaluate the experimental results and compare algorithm performance.
- Refine your understanding of the problem and adjust the algorithm selection if necessary.
Iterate and seek feedback:
- Iterate on your algorithm selection if initial results are unsatisfactory.
- Seek feedback from peers, mentors, or programming communities.
Functiona name examples
- Be descriptive
- Use verbs for actions
- Follow a consistent style
- Avoid unnecessary abbreviations
- Be concise but not overly terse
- Use domain-specific terminology
- Consider function scope and context
- Avoid misleading names
- Review and iterate
- calculateTotal
- validateInput
- fetchUserData
- generateReport
- sortArray
- filterItems
- convertToCSV
- sendEmail
- loadImage
- loginUser
- createAccount
- updateProfile
- deleteItem
- searchDatabase
- formatString
- encryptData
- validateForm
- initializeApp
- handleEvent
- renderChart