## Given: sequence of tasks

## E.g., n-th task for NP-hard optimization: find better approximation than best found so far

## Or n-th teacher-given task: find program that computes k! for k=1,2,…,n (so n-th task is a task set)

## Bias in form of probability distribution on programs

## OOPS is a time-optimal way of solving one task after another, exploiting solutions to previous tasks when possible, never suffering from previous solutions

## Asymptotically as fast as any other search algorithm

## With near-optimal constants (normally hidden in O()-notation): essentially, we lose a factor of 8 relative to unknown theoretically optimal method

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