Tips for mathematics competitions

competitions mathematics

Here are some quick tips to help with the strategy of preparing for math competitions:

  1. The Art of Problem Solving is your best friend.
  2. Problems don’t repeat, but the strategies for solving them do. When you study past problems, make sure to break down what techniques you can take away for future problems.
  3. Don’t assume that just because a problem looks like it fits in one category that it definitely does. Sometimes geometry problems are mostly algebra problems in disguise.
  4. Practice on hard problems as soon as you can handle them. Solving the AIME problems will make you better at the AMC/Math League etc. (USAMO is even better, but quite difficult.)
  5. Similarly, when you practice a problem, see what happens if you change small details about the question. What breaks your solution? Why?
  6. If you want to pursue mathematics long-term, don’t focus on speed solving easy problems and using calculators, deeper mathematical thinking problems like the Olympiads are much better preparation.
  7. The AMC tests are multiple choice, and the answer choices are carefully designed. When practicing, see if you can understand where the wrong answers come from. In competition, you might also use the answers to understand the structure of the problem.
  8. Consider other competitions like the USAMTS, which has particularly difficult problems given in a take-home exam and better suits certain styles of thinking.
  9. Practice test-taking strategies as well as math. Time management is key. Other strategies are also useful, like guessing on the AMC only when you can eliminate two or more choices, or when you think you need to guess to reach the AIME cutoffs.
  10. Connect with other people locally! Studying is way more fun if you can discuss problems with someone afterwards. ARML is great for this!

Andrew completed his undergraduate degree in Applied Mathematics at Yale and his MSc in Social Data Science at Oxford. He is now pursuing a doctorate in Social Data Science at the University of Oxford.

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