The convex subsets of R (the set of real numbers) are simply the intervals of R. Some examples of convex subsets of Euclidean 3-space are the Archimedean solids and the Platonic solids. The Kepler solids are examples of non-convex sets.
The intersection of any collection of convex sets is itself convex, so the convex subsets of a (real or complex) vector space form a complete lattice. This also means that any subset A of the vector space is contained within a smallest convex set (called the convex hull of A), namely the intersection of all convex sets containing A.
One application of convex hulls is found in efficiency frontier analysis[?]. Efficiency is assumed to be a monotonic function of each of finitely many[?] of real variables[?]. Each one of finitely many data points[?] is in exactly one hull, and is considered more efficient than all data points in hulls contained within its own hull. A particle whose velocity vector has a value of a for all coordinates representing maximized[?] variables, and a value less than a for all minimized[?] variables, will pass through the hulls in increasing order of efficiency.
A function is also said to be strictly convex if
A convex function defined on some interval is continuous on the whole interval and differentiable at all but at most countably many points. A twice differentiable function is convex on an interval if and only if its second derivative is non-negative there.
One may compare this definition of convexity and that for sets, and note that a function is convex if, and only if, the region of the plane lying above the graph of said function is a convex set.
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