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The Integral Cup — Complete Preparation Guide

India’s Premier Mathematics Competition by STEMvibe — 8 tracks, 2 seasons, 25 centers, ₹1 Crore prize pool.

🔗 Official Website · Discord · Registration


Table of Contents

  1. Overview & Competition Structure
  2. All 8 Tracks at a Glance
  3. Which Track Should I Start With? (Beginner Classification)
  4. Recommended Track Order for Beginners
  5. Track 1: Integration & Analysis
  6. Track 2: Linear Algebra & Optimization
  7. Track 3: Probability & Statistics
  8. Track 4: Game Theory
  9. Track 5: Discrete Mathematics
  10. Track 6: Number Theory
  11. Track 7: AI & Computational Mathematics
  12. Track 8: Topology
  13. General Preparation Strategy
  14. Official Resources

Overview & Competition Structure

Detail Info
Seasons 2 per year (Season 1: Feb–Apr, Season 2: Jun–Oct)
Tracks per season 4 (choose 3, assign priority weights 3:2:1)
Round 1 Offline Preliminary — 6 hrs, 30 MCQ + numerical problems, Gaussian scoring
Round 2 Online Open-Book Olympiad — 4 hrs, 6 proof-based problems (AI tools allowed, no human collaboration)
Round 3 Grand Finale — 2-day in-person event at an IIT (speed rounds, proofs, presentations, discussions)
Eligibility Any undergraduate student enrolled at a recognized Indian institution
Fee FREE
Sponsors Optiver, QRT, Jane Street

All 8 Tracks at a Glance

Season 1 (Feb–Apr 2026)

Track Domain Difficulty for Beginners
1. Integration & Analysis Pure Mathematics ⭐⭐ Moderate
2. Linear Algebra & Optimization Applied Mathematics ⭐⭐ Moderate
3. Probability & Statistics Applied Mathematics ⭐⭐ Moderate
4. Game Theory Mathematical Sciences ⭐⭐⭐ Challenging

Season 2 (Jun–Oct 2026)

Track Domain Difficulty for Beginners
5. Discrete Mathematics Pure/Applied Mathematics ⭐ Beginner-Friendly
6. Number Theory Pure Mathematics ⭐⭐ Moderate
7. AI & Computational Mathematics Emerging Sciences ⭐⭐⭐ Challenging
8. Topology Pure Mathematics ⭐⭐⭐⭐ Advanced

Which Track Should I Start With?

If you’re a complete beginner, here’s how the tracks stack up from easiest to hardest:

Tier 1 — Start Here (Most Accessible)

Track Why it’s beginner-friendly
5. Discrete Mathematics Builds on counting, logic, and combinatorics you’ve seen in high school. No calculus needed.
1. Integration & Analysis Direct extension of JEE/12th-grade calculus. Most Indian undergrads already have a foundation.
6. Number Theory Relies on arithmetic, divisibility, modular math — intuitive and pattern-based.

Tier 2 — Moderate (Need Some Course Background)

Track Why it’s moderate
2. Linear Algebra & Optimization Core undergrad topic, but optimization adds complexity. 1–2 semesters of linear algebra helps.
3. Probability & Statistics Conceptually intuitive but requires comfort with distributions, random variables, and inference.

Tier 3 — Challenging (Needs Dedicated Study)

Track Why it’s challenging
4. Game Theory Unique domain — not typically taught in standard curriculum. Requires strategic thinking and new frameworks.
7. AI & Computational Math Blends numerical methods, algorithms, and ML mathematics. Needs programming + math maturity.

Tier 4 — Advanced (Graduate-Level Depth)

Track Why it’s advanced
8. Topology Abstract mathematical thinking at its peak. Needs comfort with proofs, set theory, and real analysis first.

Follow this learning sequence to build foundational skills that snowball into harder tracks:

Step 1: Discrete Mathematics (logic, sets, counting — foundation for everything)
   ↓
Step 2: Integration & Analysis (calculus mastery — bridges high school to undergrad)
   ↓
Step 3: Number Theory (builds on discrete math + arithmetic reasoning)
   ↓
Step 4: Linear Algebra & Optimization (vector spaces, matrices — opens applied math)
   ↓
Step 5: Probability & Statistics (requires comfort with integrals + linear algebra)
   ↓
Step 6: Game Theory (applies probability + optimization + strategic reasoning)
   ↓
Step 7: AI & Computational Mathematics (combines linear algebra, probability, and coding)
   ↓
Step 8: Topology (requires real analysis maturity — tackle last)

For Season 1 specifically (choosing 3 of 4): Beginners should pick Integration & Analysis (priority 3), Linear Algebra (priority 2), and Probability & Statistics (priority 1). Skip Game Theory only if you’ve never studied it.


Track 1: Integration & Analysis

What it covers: Limits, continuity, differentiability, definite/indefinite integrals, improper integrals, series convergence, differential equations, Laplace/Fourier transforms.

Learning Path

Phase Topics Duration
Foundation Limits, continuity, differentiation rules, basic integration 2 weeks
Intermediate Integration techniques (by parts, partial fractions, trig substitution), sequences & series 2 weeks
Advanced Improper integrals, uniform convergence, Fourier/Laplace transforms, ODEs 2 weeks
Competition Problem-solving drills, timed practice, proof-writing 2 weeks

Resources

Resource Type Link
MIT 18.01 Single Variable Calculus Video Course MIT OCW
MIT 18.02 Multivariable Calculus Video Course MIT OCW
3Blue1Brown — Essence of Calculus Video Series YouTube
Paul’s Online Math Notes Text + Problems Tutorial
Calculus by James Stewart Textbook Standard in most universities
Mathematical Analysis by Tom Apostol Textbook (Advanced) For rigorous foundations
Inside Interesting Integrals by Paul Nahin Problem Book Excellent for competition-style integrals
Integration Bee problems (MIT, Stanford) Practice Search online for past problems
Integral Cup Discord #archive Past Problems Discord

Practice Strategy

  1. Master all standard integration techniques from JEE Advanced level
  2. Build a personal formula sheet for transforms and series tests
  3. Practice 10–15 integrals daily under timed conditions
  4. Work through improper integrals and convergence problems
  5. Attempt proof-based analysis problems (Apostol, Rudin exercises)

Track 2: Linear Algebra & Optimization

What it covers: Vector spaces, linear transformations, eigenvalues/eigenvectors, matrix decompositions, linear programming, convex optimization, high-dimensional geometry.

Learning Path

Phase Topics Duration
Foundation Matrices, row reduction, determinants, vector spaces 2 weeks
Intermediate Eigenvalues, diagonalization, inner product spaces, SVD 2 weeks
Advanced Linear programming, simplex method, duality, convex optimization 2 weeks
Competition Abstract proofs, problem-solving, optimization modeling 2 weeks

Resources

Resource Type Link
3Blue1Brown — Essence of Linear Algebra Video Series YouTube
MIT 18.06 Linear Algebra (Gilbert Strang) Video Course MIT OCW
Linear Algebra Done Right by Sheldon Axler Textbook Best for proof-based understanding
Introduction to Linear Algebra by Gilbert Strang Textbook Computational + intuitive
Convex Optimization by Boyd & Vandenberghe Textbook (Free) Stanford
Khan Academy — Linear Algebra Video + Practice Khan Academy
MIT 6.046 — Linear Programming Lecture Notes Available on MIT OCW

Practice Strategy

  1. Prove all major theorems yourself (don’t just read them)
  2. Do computational exercises: find eigenvalues, decompose matrices by hand
  3. Solve linear programming problems using graphical & simplex methods
  4. Practice abstract proof problems from Axler’s exercises
  5. Study past Integral Cup problems on Discord

Track 3: Probability & Statistics

What it covers: Probability axioms, conditional probability, distributions (discrete & continuous), random variables, expectation, variance, hypothesis testing, Bayesian inference, stochastic processes.

Learning Path

Phase Topics Duration
Foundation Counting, Bayes’ theorem, discrete distributions 2 weeks
Intermediate Continuous distributions, joint distributions, CLT, MGFs 2 weeks
Advanced Hypothesis testing, Bayesian inference, Markov chains 2 weeks
Competition Tricky probability puzzles, proof-based problems 2 weeks

Resources

Resource Type Link
MIT 18.05 Introduction to Probability and Statistics Course MIT OCW
Harvard Stat 110 (Joe Blitzstein) Video Course YouTube
Introduction to Probability by Blitzstein & Hwang Textbook Free PDF
All of Statistics by Larry Wasserman Textbook Concise and competition-oriented
Probability and Statistics by DeGroot & Schervish Textbook Comprehensive reference
Brilliant.org — Probability Interactive Brilliant
50 Challenging Problems in Probability by Mosteller Problem Book Classic brain-teasers

Practice Strategy

  1. Solve combinatorial probability problems daily (Putnam, IMO-style)
  2. Master all standard distributions and their relationships
  3. Practice deriving results from first principles (MGFs, transformations)
  4. Work through Bayesian inference problems step by step
  5. Study quant interview probability puzzles (Jane Street, Optiver style)

Track 4: Game Theory

What it covers: Strategic form games, Nash equilibrium, dominant strategies, mixed strategies, extensive form games, cooperative games, mechanism design, auction theory.

Learning Path

Phase Topics Duration
Foundation Normal form games, dominant strategies, Nash equilibrium 2 weeks
Intermediate Mixed strategies, extensive form games, subgame perfection 2 weeks
Advanced Cooperative games, Shapley value, mechanism design, auctions 2 weeks
Competition Novel game analysis, proof-based equilibrium problems 2 weeks

Resources

Resource Type Link
Yale ECON 159 — Game Theory (Ben Polak) Video Course YouTube
MIT 14.12 Economic Applications of Game Theory Course MIT OCW
Game Theory: An Introduction by Steven Tadelis Textbook Best for beginners
Strategy: An Introduction to Game Theory by Joel Watson Textbook Practical with exercises
An Introduction to Game Theory by Martin Osborne Textbook (Free) Author’s site
Coursera — Game Theory (Stanford/Duke) Online Course Coursera
Nicky Case — The Evolution of Trust Interactive Play

Practice Strategy

  1. Start with simple 2×2 games — find all equilibria by hand
  2. Draw extensive-form game trees for sequential games
  3. Solve mixed-strategy equilibrium problems
  4. Study auction formats (first-price, second-price, Vickrey)
  5. Practice creating novel game scenarios and analyzing them formally

Track 5: Discrete Mathematics

What it covers: Logic, set theory, relations, functions, combinatorics, graph theory, recurrence relations, generating functions, Boolean algebra.

Learning Path

Phase Topics Duration
Foundation Propositional logic, sets, proof techniques, counting principles 2 weeks
Intermediate Recurrence relations, generating functions, pigeonhole, inclusion-exclusion 2 weeks
Advanced Graph theory (coloring, matchings, flows), Ramsey theory, combinatorial design 2 weeks
Competition Olympiad-style combinatorics, creative proofs 2 weeks

Resources

Resource Type Link
MIT 6.042J Mathematics for Computer Science Video Course MIT OCW
Discrete Mathematics and Its Applications by Kenneth Rosen Textbook Standard undergrad reference
Concrete Mathematics by Graham, Knuth, Patashnik Textbook (Advanced) Deep combinatorics + generating functions
Brilliant.org — Discrete Mathematics Interactive Brilliant
A Walk Through Combinatorics by Miklós Bóna Textbook Excellent for competitions
TréntMath — Combinatorics playlist Videos Search on YouTube
Art of Problem Solving (AoPS) — Combinatorics Forum + Problems AoPS

Practice Strategy

  1. Master proof techniques: induction, contradiction, pigeonhole
  2. Solve counting problems daily — permutations, combinations, stars & bars
  3. Practice recurrence relations → derive closed forms
  4. Study graph theory through algorithmic problems (e.g., CP graph problems)
  5. Attempt Putnam/Olympiad combinatorics problems

Track 6: Number Theory

What it covers: Divisibility, primes, modular arithmetic, Euler’s theorem, quadratic residues, continued fractions, Diophantine equations, algebraic number theory basics.

Learning Path

Phase Topics Duration
Foundation Divisibility, GCD/LCM, primes, fundamental theorem of arithmetic 1 week
Intermediate Modular arithmetic, Euler’s totient, Chinese Remainder Theorem, Fermat’s little theorem 2 weeks
Advanced Quadratic residues, Legendre/Jacobi symbols, continued fractions, Pell’s equation 2 weeks
Competition Diophantine equations, Olympiad-style NT problems, proofs 3 weeks

Resources

Resource Type Link
Elementary Number Theory by David Burton Textbook Best starting point
An Introduction to the Theory of Numbers by Niven, Zuckerman, Montgomery Textbook Comprehensive
MIT 18.781 Theory of Numbers Course MIT OCW
Number Theory: Structures, Examples, and Problems by Andreescu & Andrica Problem Book Olympiad-oriented
Michael Penn — Number Theory playlist Videos YouTube
Project Euler Programming + NT Project Euler
AoPS Number Theory Forum + Problems AoPS

Practice Strategy

  1. Build strong modular arithmetic intuition — solve 5 problems daily
  2. Prove major theorems (Euler’s, Fermat’s, CRT) yourself
  3. Study Diophantine equations systematically (linear → quadratic → higher)
  4. Work through Olympiad NT shortlists (ISI, CMI, IMO, Putnam)
  5. Use Project Euler to blend computation with theory

Track 7: AI & Computational Mathematics

What it covers: Numerical methods (root-finding, interpolation, numerical integration), optimization algorithms, machine learning mathematics, computational complexity, algorithmic problem-solving.

Learning Path

Phase Topics Duration
Foundation Floating-point arithmetic, root-finding (bisection, Newton’s), interpolation 2 weeks
Intermediate Numerical linear algebra, numerical integration/differentiation, ODEs 2 weeks
Advanced Gradient descent, ML math (loss functions, backprop), convex optimization 2 weeks
Competition Algorithm design, error analysis, computational proofs 2 weeks

Resources

Resource Type Link
Numerical Analysis by Burden & Faires Textbook Standard undergrad text
MIT 18.330 Introduction to Numerical Analysis Course MIT OCW
Mathematics for Machine Learning by Deisenroth, Faisal, Ong Textbook (Free) MML Book
3Blue1Brown — Neural Networks Video Series YouTube
Stanford CS229 — Machine Learning Course Notes Stanford
Deep Learning by Goodfellow, Bengio, Courville Textbook (Free) deeplearningbook.org
Fast.ai — Computational Linear Algebra Video Course fast.ai
Python (NumPy, SciPy) Tools Practice implementations

Practice Strategy

  1. Implement numerical methods from scratch in Python (Newton’s method, Gaussian elimination, etc.)
  2. Understand error analysis — truncation vs rounding errors
  3. Study the math behind ML: gradient descent convergence proofs, regularization theory
  4. Solve computational problems with pen + paper first, then verify with code
  5. Read research papers on numerical methods for competition-level depth

Track 8: Topology

What it covers: Topological spaces, open/closed sets, continuity, connectedness, compactness, quotient spaces, fundamental group, homology basics.

Learning Path

Phase Topics Duration
Prerequisite Set theory, real analysis (metric spaces, open/closed sets) 2 weeks
Foundation Topological spaces, basis, subspace/product/quotient topologies 3 weeks
Intermediate Continuity, homeomorphisms, connectedness, compactness 3 weeks
Advanced Separation axioms, fundamental group, covering spaces 3 weeks
Competition Proof-based topology problems, counterexamples 2 weeks

Resources

Resource Type Link
Topology by James Munkres Textbook The standard text — start here
Introduction to Topology: Pure and Applied by Adams & Franzosa Textbook More gentle introduction
MIT 18.901 Introduction to Topology Course MIT OCW
Topology Without Tears by Sidney Morris Textbook (Free) Free PDF
Counterexamples in Topology by Steen & Seebach Reference Essential for building intuition
3Blue1Brown — Topology concepts Videos Scattered across his channel
ThoughtSpaceZero — Topology playlist Videos YouTube

Practice Strategy

  1. Do NOT skip prerequisites — you need real analysis (metric spaces, continuity, compactness in ℝⁿ) first
  2. Read Munkres chapters 1–4 methodically, doing every exercise
  3. Build a counterexample notebook — for every theorem, know where it fails if conditions are relaxed
  4. Practice proof-writing: “Show that X is compact,” “Prove f is continuous,” etc.
  5. Discuss problems on math forums (StackExchange, AoPS) for feedback on your proofs

General Preparation Strategy

8-Week Preparation Plan (Per Season)

Week Focus
1–2 Choose tracks, gather resources, review fundamentals
3–4 Deep study of core topics for each chosen track
5–6 Problem-solving practice — timed sessions, past papers
7 Mock exams, identify weak areas, targeted revision
8 Review formula sheets, light practice, rest before exam

Tips for All Tracks

  1. Join the Discord — The Integral Cup Discord has past problems in #archive, syllabus in #announcements, and sample papers in #resources
  2. Priority strategy matters — Assign your strongest track as priority 3 (weight = 3×), second-strongest as priority 2, and weakest as priority 1 for maximum score
  3. Round 2 is open-book — Focus on deep understanding over memorization; know where to find things and how to apply them
  4. Round 3 includes presentations — Practice explaining proofs verbally; communication matters at the finale
  5. Form study groups — Connect with participants from your center via Discord
  6. Track the sponsors — Optiver, QRT, and Jane Street sponsor this; their quant interview prep resources overlap heavily with Tracks 1–4
Book Covers
Putnam and Beyond by Gelca & Andreescu Multi-topic competition problems
Problem-Solving Through Recreational Mathematics by Averbach & Chein Beginner-friendly mathematical puzzles
The Art and Craft of Problem Solving by Paul Zeitz Competition mindset + techniques
How to Prove It by Daniel Velleman Proof-writing fundamentals (essential for Round 2 & 3)

Official Resources


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