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
- Overview & Competition Structure
- All 8 Tracks at a Glance
- Which Track Should I Start With? (Beginner Classification)
- Recommended Track Order for Beginners
- Track 1: Integration & Analysis
- Track 2: Linear Algebra & Optimization
- Track 3: Probability & Statistics
- Track 4: Game Theory
- Track 5: Discrete Mathematics
- Track 6: Number Theory
- Track 7: AI & Computational Mathematics
- Track 8: Topology
- General Preparation Strategy
- 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. |
Recommended Track Order for Beginners
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
- Master all standard integration techniques from JEE Advanced level
- Build a personal formula sheet for transforms and series tests
- Practice 10–15 integrals daily under timed conditions
- Work through improper integrals and convergence problems
- 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
- Prove all major theorems yourself (don’t just read them)
- Do computational exercises: find eigenvalues, decompose matrices by hand
- Solve linear programming problems using graphical & simplex methods
- Practice abstract proof problems from Axler’s exercises
- 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
- Solve combinatorial probability problems daily (Putnam, IMO-style)
- Master all standard distributions and their relationships
- Practice deriving results from first principles (MGFs, transformations)
- Work through Bayesian inference problems step by step
- 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
- Start with simple 2×2 games — find all equilibria by hand
- Draw extensive-form game trees for sequential games
- Solve mixed-strategy equilibrium problems
- Study auction formats (first-price, second-price, Vickrey)
- 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
- Master proof techniques: induction, contradiction, pigeonhole
- Solve counting problems daily — permutations, combinations, stars & bars
- Practice recurrence relations → derive closed forms
- Study graph theory through algorithmic problems (e.g., CP graph problems)
- 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
- Build strong modular arithmetic intuition — solve 5 problems daily
- Prove major theorems (Euler’s, Fermat’s, CRT) yourself
- Study Diophantine equations systematically (linear → quadratic → higher)
- Work through Olympiad NT shortlists (ISI, CMI, IMO, Putnam)
- 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
- Implement numerical methods from scratch in Python (Newton’s method, Gaussian elimination, etc.)
- Understand error analysis — truncation vs rounding errors
- Study the math behind ML: gradient descent convergence proofs, regularization theory
- Solve computational problems with pen + paper first, then verify with code
- 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
- Do NOT skip prerequisites — you need real analysis (metric spaces, continuity, compactness in ℝⁿ) first
- Read Munkres chapters 1–4 methodically, doing every exercise
- Build a counterexample notebook — for every theorem, know where it fails if conditions are relaxed
- Practice proof-writing: “Show that X is compact,” “Prove f is continuous,” etc.
- 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
- Join the Discord — The Integral Cup Discord has past problems in
#archive, syllabus in #announcements, and sample papers in #resources
- 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
- Round 2 is open-book — Focus on deep understanding over memorization; know where to find things and how to apply them
- Round 3 includes presentations — Practice explaining proofs verbally; communication matters at the finale
- Form study groups — Connect with participants from your center via Discord
- Track the sponsors — Optiver, QRT, and Jane Street sponsor this; their quant interview prep resources overlap heavily with Tracks 1–4
Recommended Books Across All Tracks
| 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