In statistical learning theory, the principle of empirical risk minimization defines a family of learning algorithms based on evaluating performance over a known and fixed dataset.

In automata theory (a branch of theoretical computer science), DFA minimization is the task of transforming a given deterministic finite automaton into an equivalent DFA that has a minimum number of states.

In the field of computational chemistry, energy minimization (also called energy optimization, geometry minimization, or geometry optimization) is the process of finding an arrangement in space of a collection of atoms where, according to some computational model of chemical bonding, the net inter-atomic force on each atom is acceptably close to zero and the position on the potential energy surface is a stationary point (described later).