Aggregation of Marginal Gains


If you want to get better at something, you need a plan. Improvement doesn’t happen on its own. But once you have that plan, a bigger challenge is executing on it along with your other responsibilities.

One way to increase your chances of following through on changes is not to try to make big changes all at once. Instead, make small changes, but make them regularly. Let’s see how that works.

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Getting Past a Competitive Programming Plateau

In the Peak book, the authors describe the following learning challenge in a section called “Getting Past Plateaus”: When you first start learning something new, it is normal to see rapid — or at least steady — improvement, and when that improvement stops, it is natural to believe you’ve hit some sort of implacable [immovable] […]


Three Ways to Solve UVa 108

UVa 108 is rated as a Level 1 (easy) problem by uHunt, but its solution nevertheless contains some interesting techniques. Here’s a summary of the problem statement: Given an $N \times N$ array $A$ of positive and negative integers, print the sum of the nonempty subarray of $A$ that has the maximum sum. The sum […]


Competitive Programming Training Tips

Over the past couple of weeks, I have been writing about deliberate practice as described in Peak by Anders Ericsson and Robert Pool. The book describes three types of practice: Na├»ve practice, purposeful practice, and deliberate practice. The latter two types of practice are both effective, but there’s a key difference that makes deliberate practice […]


Achieving Peak Performance in Competitive Programming

Last week, I wrote about the concept of mental representations, an important topic in Peak by Anders Ericsson and Robert Pool. According to the authors, learners seeking expertise should have as their goal a virtuous cycle between mental representations and deliberate practice: Deliberate practice should produce more effective mental representations, and more effective mental representations […]


Mental Representations for Competitive Programming Practice

Psychologist and deliberate practice pioneer K. Anders Ericsson has been studying and writing about deliberate practice for decades, and his landmark 1993 paper provides an accessible introduction to the topic. This year, he published his first book-length exploration of deliberate practice for a general audience. Peak: Secrets from the New Science of Expertise explains the […]


Three-Dimensional Dynamic Programming for UVa 10755

Two weeks ago, I introduced the concept of memoization for dynamic programming, using as an example UVa 787. That problem involves operations on a sequence of integers, a one-dimensional structure. UVa 10755: Garbage Heap increases the problem complexity by organizing its integer data into a three-dimensional shape, a rectangular parallelepiped. Nevertheless, we can use memoization […]


Dynamic Programming Basics for UVa 787

In programming contests, some algorithms and techniques get more emphasis than they do in school or in professional programming work. One such technique is dynamic programming. CP3 has this to say about dynamic programming: This technique was not known before 1940s, nor frequently used in ICPCs or IOIs before mid 1990s, but it is considered […]