Determining the least expensive path for a new subway line underneath a metropolis like New York City is a colossal planning challenge—involving thousands of potential routes through hundreds of city blocks, each with uncertain construction costs. Conventional wisdom suggests extensive field studies across many locations would be needed to determine the costs associated with digging below certain city blocks.
Algorithm finds smallest dataset that guarantees optimal solutions to complex problems
