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
Lens (optics) real object at the location of that virtual image. Likewise, it appears to a subsequent lens as if it were an object at that location, so that second lens could again focus that light into a real image, "S" then being measured from the virtual image location behind the first lens to the second lens. This is exactly what the eye does when looking through a magnifying glass. The magnifying glass creates a (magnified) virtual image behind the magnifying glass, but those rays are then re-imaged by the lens of the eye to create a "real image" on the retina.