Sampling optimization is a methodological approach dedicated to identifying and implementing the most effective strategies for selecting a subset of data points or observations from a larger population or domain. It investigates principles, algorithms, and criteria to maximize the statistical efficiency, representativeness, or information yield of a sample while minimizing associated costs or resource expenditure, thereby enabling robust inference from limited data across various scientific and engineering disciplines.