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Keep shoppingBeyond Fixed Windows: Adaptive Sliding Algorithms
1,002 students
Beyond Fixed Windows: Adaptive Sliding Algorithms
Learn to build dynamic sliding window and two-pointer solutions for real-world coding problems and technical interviews.
Created byNorbert Grover
Last updated 11/2025
English
What you'll learn
- Analyze and apply adaptive sliding window techniques to solve dynamic subarray and substring problems efficiently.
- Identify when to use fixed-size versus adaptive sliding window patterns in coding challenges.
- Construct algorithms using two-pointer strategies that don’t rely on traditional window structures.
- Solve problems involving longest or shortest subarrays that meet complex conditions (e.g. sum ≥ target, exactly K distinct values).
- Use frequency maps and hash-based counters to manage dynamic constraints within a moving window.
- Implement in-place array and string manipulation using pointer movement and condition tracking.
- Debug and visualize adaptive window logic using print tracing and incremental analysis.
- Apply prefix sum and cumulative frequency techniques to count or optimize subarray conditions.
- Build reusable templates for window-shrinking problems involving constraints like duplicates or target counts.
- Prepare for technical interviews with confidence by mastering advanced pointer and loop control patterns.
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Course content
2 sections • 19 lectures • 2h 19m total length
Requirements
- Basic understanding of variables, loops, and conditionals in any programming language.
- Familiarity with Swift (or another modern language) and how functions, arrays, and dictionaries work.
- Comfort with writing and running simple algorithms in an IDE or code editor.
- Basic problem-solving experience on platforms like LeetCode, HackerRank, or CodeSignal.
- Understanding of arrays, strings, and hash maps as data structures.
- Ability to trace code using print statements or a debugger to follow variable states.
- Familiarity with the sliding window pattern at a basic, fixed-size level.
- Some exposure to two-pointer techniques, even if not fully mastered.
- Experience reading or implementing simple algorithms with time and space complexity in mind.
- Willingness to break down problems into smaller parts and iterate on a solution.
Description
If you've ever worked with the sliding window technique and felt like the "fixed-size" window explanations were just scratching the surface, you're not alone — and you're exactly who this course is for.
Beyond Fixed Windows: Adaptive Sliding Algorithms is the first comprehensive course to focus specifically on the inner mechanics of adaptive sliding windows and pointer manipulation in iterative structures. While most resources only teach the basic fixed-size window template, this course takes you deeper — exploring how dynamic, condition-driven windows work in real coding interviews and algorithm design.
What You'll Learn
The fundamentals of adaptive (dynamic-size) sliding window problems
How to manage window boundaries based on conditions (e.g. distinct elements, sum constraints, frequency maps)
Mastery of two-pointer approaches that don’t involve explicit windowing
Real-world patterns like:
Longest/Shortest subarray that meets a condition
Substring problems with at most or exactly K distinct elements
Minimum window problems (e.g. "smallest substring containing a pattern")
Counting and optimization strategies using prefix sums and hash maps
In-place operations and performance optimization techniques
Debugging adaptive window logic through visual walkthroughs and print tracing
How interviewers expect you to adapt base patterns to new constraints
Why This Course Is Unique
This is not a survey course that glosses over templates. Instead, you'll dive deeply into how adaptive windows actually work, why they break, how to fix them, and how to apply the logic to new problems. No other course provides such an intensive breakdown of pointer-driven loop behavior in the context of algorithmic problem solving.
By the end of the course, you won’t just recognize the patterns — you'll be able to build your own adaptive solutions from scratch with full control over correctness, efficiency, and clarity.
Who this course is for:
- Developers who already understand basic algorithms and want to go deeper into dynamic sliding windows.
- Students preparing for technical coding interviews involving subarray and substring problems.
- Programmers frustrated with vague explanations of sliding window patterns and looking for clear guidance.
- Intermediate Swift developers who want to sharpen their algorithmic thinking.
- Competitive programming participants seeking to master dynamic window-based problems.
- Self-taught developers aiming to fill gaps in their algorithm foundations.
- Bootcamp graduates looking for a structured approach to sliding window variations.
- Developers who know how to use two pointers but struggle with conditionally shrinking or expanding windows.
- Engineers who want to understand how adaptive logic plays out in loop design and pointer movement.
- Anyone who has studied fixed-size windows and wants to go beyond templates and into real-world problems.