Non-Linear Programming

a linear program assumes you know the data. stochastic programming admits that you do not — some coefficients are random — but insists you know their distribution, and asks for the decision that is best on average. 𐃏 the structural insight that makes this a paradigm rather than a hack: split the decision in two. commit to \(x\) now, before the coin is flipped; after uncertainty resolves, take a corrective recourse action \(y\) that adapts to whatever happened. the objective charges you for both, weighting the second stage by expectation.

single-objective optimisation is a polite fiction. real decisions trade cost against quality, return against risk, speed against accuracy — and the objectives disagree, otherwise you would not have listed them separately. 𐃏 multi-objective optimisation refuses to mash them into one number prematurely. the price of that honesty: “the optimum” stops being a point and becomes a set — the pareto front — and half the subject is about how to trace it, the other half about how to pick from it.

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

“programming” here means planning, not coding — the word predates the software sense. 𐃏 a linear program optimises a linear objective over a region carved out by linear inequalities. it is the base camp of mathematical programming: quadratic, integer and non-linear programming all generalise it in one direction or another, and all of them lean on LP machinery (relaxations, duals, warm starts) to get anything done. clrs devotes chapter 29 to it (Cormen, Thomas H. and Leiserson, Charles E. and Rivest, Ronald L. and Stein, Clifford, 2009).