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Algorithmic Problem-Solving Patterns: Choose Data Structures, Analyze Complexity, and Build Efficient Solutions
Paperback
Publisher: Independently Published
Published: Sep 10 2026
Pages: 162
Weight: 0.45
Height: 0.41 Width: 6.00 Depth: 9.00
Language: English
Stop memorizing algorithm tricks. Learn how to recognize the structure that makes the right solution possible.
A problem may ask you to find duplicates, process a moving range, schedule competing jobs, search for the smallest feasible capacity, traverse dependencies, rank a continuous stream, or choose between competing optimizations.
The wording changes. The underlying patterns often do not.
Algorithmic Problem-Solving Patterns teaches a practical, repeatable way to move from a problem statement to an efficient and defensible solution. Instead of beginning with code, the book begins with the questions professional developers should ask first: How large can the input become? What operation repeats? What must remain true? Does order matter? Can information be precomputed? Is the search space monotonic? Are subproblems reused? What are the real time and memory constraints?
From those questions, familiar algorithms become easier to recognize, compare, and apply.
Inside, you'll learn how to:
- Translate vague requirements into concrete operations and constraints
- Analyze time and space complexity without confusing Big O with real execution time
- Reason accurately about worst-case, average-case, expected, and amortized costs
- Choose arrays, sets, maps, queues, heaps, trees, graphs, and other structures according to the operations that matter
- Build one-pass solutions using compact state and clear invariants
- Use hashing for membership, counting, indexing, grouping, and memoization
- Recognize when two pointers, sliding windows, prefix sums, and monotonic deques eliminate repeated work
- Use sorting as strategic preprocessing rather than treating it as an automatic cost to avoid
- Understand binary search as boundary finding, including binary search over answer spaces
- Distinguish structural recursion, backtracking, and divide-and-conquer reasoning
- Prune combinatorial search safely instead of relying on hopeful heuristics
- Justify greedy choices with reasoning such as exchange arguments
- Build dynamic-programming solutions by defining the state before writing the recurrence
- Apply depth-first search, breadth-first search, topological ordering, shortest paths, connected components, and union-find
- Use heaps for Top-K problems, priority processing, streaming data, scheduling, and k-way merging
- Test optimized solutions against the specific assumptions and failure modes that can make them wrong
- Benchmark real implementations without ignoring preprocessing, memory, I/O, runtime, and hardware costs
- Review AI-generated algorithmic code as a candidate solution rather than unquestioned truth
The book culminates in a repeatable problem-solving workflow that moves from contract and constraints through baseline design, repeated-work analysis, pattern selection, invariants, complexity analysis, testing, measurement, and refinement.
For hands-on practice, the book includes 12 applied professional workshops, covering problems such as high-volume event deduplication, API rate limiting, telemetry windows, permission reconciliation, capacity planning, configuration search, maintenance scheduling, dynamic-programming release planning, document comparison, dependency-based deployment order, and live Top-K security alerts.
You'll also get 12 guided problem-solving drills, a compact complexity and pattern reference, an 18-question selection checklist, and a substantial professional-review appendix covering contracts, invariants, hidden preprocessing, adversarial inputs, numerical correctness, benchmarking, testing, maintainability, and post-deployment reevaluation.
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