¶ Regular Expressions
¶ Competitive programming
¶ Introduction to Regular Expressions for Competitive Programming
If there’s one tool in programming that feels almost magical to a beginner, it is the regular expression. A small string of symbols, seemingly cryptic at first glance, can suddenly match patterns, extract information, validate structures, and transform text with astonishing speed. In competitive programming, regular expressions often sit quietly in the background of most languages’ standard libraries—waiting for you to discover just how powerful they really are.
Regex is one of those topics that programmers tend to either love or fear. Many developers pick up a few basic patterns, use them occasionally, and never go beyond simple matching. Contest programmers often ignore them entirely, unaware that mastering regex can significantly boost their efficiency, both in solving problems and in writing quick, precise code. This course aims to take you far beyond that shallow familiarity and introduce you to the full expressive force of regular expressions.
Over the span of 100 articles, we will explore regex not just as a tool for searching text, but as a way of thinking. You’ll learn to see patterns where others see noise, to express constraints compactly, and to handle string-heavy problems with elegance instead of brute force. Whether you’re preparing for national contests, coding interviews, online judge marathons, or personal projects, regex offers a unique advantage that very few competitive programmers exploit fully.
¶ Why Regular Expressions Matter in Competitive Programming
Many contestants underestimate the role of regex in contests because they associate it with tasks like log filtering or web scraping. But competitive programming has an enormous volume of problems involving strings: parsing, validation, tokenization, extraction, comparison, splitting, checking conditions, cleaning inputs, and recognizing complex patterns in sequences.
In many problems, you can solve the core logic manually with loops and conditions, but this tends to be slow, verbose, and sometimes error-prone—especially under time constraints. Regex turns these multi-line logic checks into clean one-liners that are easier to write, easier to debug, and far more expressive.
The moment you understand regex deeply, problems involving patterns stop being chores and become opportunities for efficiency. Instead of manually checking that a string contains at least one digit, one uppercase letter, one lowercase letter, and one symbol, you can express that entire logic compactly. Instead of writing loops for substring detection with complex boundaries, you can transform the problem into a concise matching pattern.
More importantly, regex teaches you discipline in thinking about patterns: repetition, grouping, alternation, boundaries, and constraints become second nature. This mindset carries over into dynamic programming on strings, automata theory, hashing, and even grammar problems.
¶ Regex Is Not About Memorizing Symbols
A common mistake beginners make is trying to memorize every regex symbol without understanding the logic behind them. That is a fast track to confusion. You end up mixing up character classes, misusing anchors, overriding special characters, or applying greedy patterns where non-greedy ones are required. The key to mastering regex is understanding structure, not memorizing syntax.
A well-built regex pattern is like a sentence. Different parts play different roles:
– anchors define where the pattern starts or ends
– quantifiers express how long something can be
– character classes specify what is allowed
– grouping creates small logical units
– alternation allows multiple possible paths
– lookarounds let you check context without consuming it
When you learn regex conceptually, the syntax becomes easy. You stop writing patterns blindly and start thinking in terms of intent and structure.
This course will teach regex the way it should be taught: as a language for expressing constraints, not a bag of symbols to memorize.
¶ The Hidden Power of Regex in CP Tasks
In competitive programming, regex is useful in far more ways than most people realize. Consider the kinds of operations contestants frequently need to perform:
– parse tokens from a messy input format
– detect malformed strings
– find repeated sequences
– validate path-like patterns
– identify numeric formats with optional signs or decimals
– check if input follows a specific grammar
– split strings on complex boundaries
– extract substrings that follow certain rules
– determine if a string contains illegal characters
– collapse multiple spaces, commas, or delimiters
– pre-process input for further algorithms
Most contestants write custom logic for these tasks every time, re-solving the same problems repeatedly. Regex allows you to express these operations concisely and reuse logic elegantly.
For example:
Instead of writing a loop to confirm that a string is a valid integer including sign, you can use a simple pattern. Instead of manually trimming whitespace or removing repeating characters, you can write a single substitution rule. Instead of writing 10 lines of code to validate a password or check balanced structure, you can combine lookarounds and quantifiers to express the logic far more cleanly.
Regex isn’t meant to replace algorithms. It is meant to eliminate unnecessary boilerplate.
¶ Regex Teaches You a New Way of Seeing Patterns
Strings are everywhere in programming, but few people learn to see them structurally. Regex forces you to think in terms of:
– what can appear
– how many times
– where it can appear
– what must follow
– what must not precede
– what sequences are allowed
– what relationships characters must have
This is a powerful shift in thinking. For example, many dynamic programming problems that involve sequence matching or checking constraints have a structural nature similar to regex. When you get comfortable thinking structurally, many problems become easier.
You begin to see when constraints are describing a pattern. You recognize when transitions in a DP behave like regex repetition. You notice that some problems can be simplified by preprocessing with regex before applying more complex algorithms.
Pattern awareness is one of the most underrated skills in competitive programming, and regex trains it beautifully.
¶ Regex Helps You Write Cleaner, Shorter, Faster Code Under Pressure
Contests involve time pressure. Your solutions need to be correct, efficient, and quick to implement. Regex is one of the few tools that can significantly reduce implementation time without sacrificing performance.
Imagine the following scenarios:
– a problem involves cleaning input formatting before parsing
– you need to split a string using multiple delimiters
– a problem includes tricky pattern-validation constraints
– you must perform fast filtering on large text inputs
– parsing instructions involves recognizing encoded commands
– detecting malformed input is essential for correctness
Regex lets you do all of this with elegance. The difference between writing five lines of character checks and writing a single expressive pattern is immense when you’re racing against a clock.
Regex also encourages modular thinking. You break patterns into components, test them quickly, and modify them without rewriting entire functions.
¶ Regex Is a Gateway Into Automata Theory
Behind every regular expression is a finite automaton — a machine that recognizes languages. While competitive programmers may never need to formally construct these automata, understanding the connection enriches your intuition.
When you write a regex pattern, you are essentially designing:
– states
– transitions
– branching paths
– accepting conditions
You’re creating a miniature machine that tests your input for correctness — all inside a single line of code.
Understanding this connection becomes extremely valuable when solving advanced string problems that involve DP on states, automaton-like transitions, or constraints that resemble grammar rules.
This course will gently introduce these connections so you can appreciate the deeper theory behind regex without diving too far into formal mathematical definitions.
¶ Why a 100-Article Course on Regex Makes Sense
At first glance, regex seems like a small topic: a bunch of symbols for matching strings. But the moment you dig deeper, you realize it’s an entire universe.
There are topics like:
– basic literals
– grouping and alternation
– greedy vs non-greedy behavior
– backtracking
– lookaheads and lookbehinds
– advanced quantifiers
– recursive patterns
– atomic groups
– pattern rewriting
– complex substitution rules
– multiline modes
– Unicode categories
– capturing and non-capturing groups
– multi-line parsers
– advanced boundary conditions
– regex performance traps
– optimizations and limitations
Each of these topics has practical value in competitive programming, especially in problems where input format is non-trivial.
This course will cover the full depth of regex: from the simplest building blocks to the most advanced constructs you’ll ever need in a contest environment.
¶ The Practical Side of Regex in CP
Regex is not only about learning patterns. It’s about getting comfortable using them.
You’ll write dozens of patterns.
You’ll debug tricky expressions.
You’ll learn how to craft readable regex.
You’ll understand how to keep performance stable.
You’ll learn when regex solves a problem beautifully and when it becomes overkill.
One of the key goals of this course is helping you develop an instinct: the ability to look at a problem and decide whether regex is the right tool or whether you need something else.
Good competitive programmers make these decisions naturally. This course aims to help you reach that level.
¶ A New Way of Thinking About Strings
Strings can be messy. They can include symbols, numbers, whitespace, markup, instructions, data—all blended together. Regex helps you break that chaos into understandable structure.
Suddenly, the string is no longer a long sequence of characters. It's a composition of fragments, patterns, repetitions, and rules. You learn to read these rules the way someone reads grammar.
And that shift makes everything easier:
– tokenization
– parsing
– verification
– preprocessing
– transformation
This mindset is incredibly useful in solving CP problems that involve reading unusual or multi-layered input formats.
¶ What This Course Aims to Give You
By the end of 100 articles, regex will feel natural, not intimidating. You’ll be able to:
– write expressive, clean patterns
– validate complex string structures effortlessly
– preprocess tricky input formats with confidence
– build patterns that handle recursive or contextual rules
– debug regex with clarity
– decide when regex is appropriate
– optimize regex for speed
– combine regex with other algorithms
– approach complex string problems with structural awareness
Regex will become part of your toolkit, not an obscure topic on the sidelines.
¶ A Final Thought Before You Begin
Every programmer who falls in love with regex starts in the same place: curiosity mixed with a hint of confusion. The symbols look strange. The rules feel unusual. But once your mind adapts to the way regex thinks, everything starts to make sense. Patterns become clear. Chaos becomes structure.
This course is here to guide you through that transformation. With patience and curiosity, you will soon wield regular expressions with a level of confidence that makes string-heavy problems smoother, faster, and far more enjoyable.
Welcome to your journey into the world of Regular Expressions for Competitive Programming.
¶ Regular Expressions
¶ 🟢 Beginner Level (Chapters 1–30): Fundamentals and Syntax Basics
1. What Are Regular Expressions?
2. Why Use Regex in Competitive Programming?
3. Basic Characters and Literal Matching
4. The Dot . – Matching Any Character
5. Character Classes [abc]
6. Negated Character Classes [^abc]
7. Ranges and Shorthand Classes: [a-z], \d, \w
8. Anchors: Start ^ and End $
9. Quantifiers: *, +, ?
10. Exact and Range Quantifiers: {n}, {n,}, {n,m}
11. Escape Sequences and Special Characters
12. Grouping and Capturing: (abc)
13. Alternation with | (OR operator)
14. Using Regex in C++, Python, and Java
15. Testing Regex with Online Tools
16. Simple Pattern Matching Tasks
17. Matching Fixed-Length Tokens
18. Repetition and Greedy Matching
19. Introduction to Regex Engines in CP Languages
20. Regex in String Parsing Problems
21. String Validation Using Regex
22. Building Regex from Problem Constraints
23. Input Filtering with Regex
24. Case Sensitivity in Regex
25. Regex Time Complexity Basics
26. Replacing Substrings Using Regex
27. Finding All Matches vs First Match
28. Using Regex to Split Strings
29. Precompiling Regex Patterns
30. Avoiding Common Beginner Mistakes
¶ 🟡 Intermediate Level (Chapters 31–70): Pattern Crafting and Competitive Use Cases
31. Matching Dates in DD/MM/YYYY Format
32. Matching Email Addresses
33. Parsing and Validating URLs
34. Detecting HTML/XML Tags
35. Matching Hexadecimal and Binary Numbers
36. Validating IPv4 and IPv6 Addresses
37. Extracting Integers from Mixed Text
38. Matching Palindromes with Regex
39. Identifying Duplicates in Input
40. Matching Balanced Parentheses (Limitations)
41. Using Lookaheads and Lookbehinds
42. Greedy vs Lazy Matching Explained
43. Finding the Longest Valid Match
44. Real-World Regex in Parsing Logs
45. Combining Multiple Patterns Efficiently
46. Negative Lookaheads for Exclusion
47. Regex for Token Extraction in CP
48. Pattern Matching in Bioinformatics Problems
49. Regex for Number Ranges
50. Backreferences in Pattern Matching
51. Cleaning Up Input Using Regex
52. Using Regex in Regex-Based Search Problems
53. Multi-Line and Dotall Modes in Regex
54. Identifying Comments in Code Snippets
55. Regex for CSV and TSV Parsing
56. Dealing with Escape Characters
57. Creating Regex Libraries for CP
58. Regex and Input Sanitization
59. Using Regex in Brute-Force Pattern Search
60. Constraints-Based Matching in Regex
61. Regex Tricks for Hidden Test Cases
62. Regex in Online Judges: Codeforces, LeetCode
63. Writing Efficient Regex for Fast Matching
64. Dynamic Regex Pattern Construction
65. Competitive Regex Golf: Smallest Pattern Wins
66. Pattern Detection in DNA Sequences
67. Regex for Mathematical Expression Validation
68. Finding Repeating Sequences
69. Extracting Nested Patterns (Limits of Regex)
70. Regex for Sentence Tokenization
¶ 🔴 Advanced Level (Chapters 71–100): Optimization, Pitfalls, and Expert Strategies
71. Performance Tuning in Regex
72. Avoiding Catastrophic Backtracking
73. Regex in Trie-Based Problems
74. Regex vs Manual Parsers in CP
75. Benchmarking Regex Solutions
76. Regex Use in Natural Language Processing Tasks
77. Recursive Patterns (Where Supported)
78. Advanced Lookarounds and Assertions
79. Custom Tokenizers with Regex
80. Regex Engine Internals (DFA vs NFA)
81. Writing Regex in Memory-Constrained Environments
82. Regex to Solve Game-Based Input Problems
83. Extracting Named Groups
84. Unicode and Regex in CP
85. Regex and Non-ASCII Matching
86. Using Regex in Stream Processing
87. Combining Regex with Other String Algorithms
88. Integrating Regex into Search-and-Replace Algorithms
89. Writing Regex Without Libraries (Regex Engines from Scratch)
90. Avoiding Overuse of Regex in CP
91. Regex with Complicated Whitespace Handling
92. Solving Cryptic Text Decoding Challenges
93. Custom Regex Matchers for CP
94. Regex for Competitive File Parsing Challenges
95. Handling Multilingual Input Using Regex
96. Regex-Based Input Compression
97. Combining Regex with Greedy Algorithms
98. Regex for Runtime Input Filters
99. Regex Obfuscation and Compression Tricks
100. Masterclass: Writing a One-Liner Regex to Solve a Full CP Problem