Convexity

promote the objective of a linear program from a plane to a bowl and you get quadratic programming: minimise a quadratic function over a polyhedron. it is the smallest step beyond LP, yet it captures a startling share of applied mathematics — support vector machines, portfolio selection, ridge regression, model-predictive control — because “squared penalty subject to linear rules” is how half the world states its preferences. 𐃏