Convex optimization linear programming. Many optimization problems can be reformulated as convex minimization problems. For example, the problem of "maximizing" a "concave function" formula_1 can be re-formulated equivalently as a problem of minimizing the convex function formula_2. An optimization problem, which aims to find a point formula_3 such that formula_4 for all formula_5 in some feasible set formula_6, is convex if and only if formula_6 and the objective function formula_1 satisfy the following two properties: Perhaps more conveniently, the convex problem can be phrased in the more shorthand general definition of optimization: finding some formula_19 such that for some feasible set formula_6
Luneburg lens one side, and formula_14 only on the other: The parameter formula_19 is a constant for any given ray, but differs between rays passing at different distances from the centre of the lens. For rays passing through the centre, it is zero. In some special cases, such as for Maxwell's fish-eye, this first order equation can be further integrated to give a formula for formula_14 as a function or formula_13. In general it provides the relative rates of change of formula_14 and formula_13, which may be integrated numerically to follow the path of the ray through the lens. Luneburg lens A