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  2. Convex optimization - Wikipedia

    en.wikipedia.org/wiki/Convex_optimization

    Such methods are called interior point methods. [7]: chpt.11 They have to be initialized by finding a feasible interior point using by so-called phase I methods, which either find a feasible point or show that none exist. Phase I methods generally consist of reducing the search in question to a simpler convex optimization problem.

  3. Convex position - Wikipedia

    en.wikipedia.org/wiki/Convex_position

    For instance, the traveling salesman problem, NP-hard for arbitrary sets of points in the plane, is trivial for points in convex position: the optimal tour is the convex hull. [3] Similarly, the minimum-weight triangulation of planar point sets is NP-hard for arbitrary point sets, [ 4 ] but solvable in polynomial time by dynamic programming for ...

  4. Convex hull algorithms - Wikipedia

    en.wikipedia.org/wiki/Convex_hull_algorithms

    One of the simplest (although not the most time efficient in the worst case) planar algorithms. Created independently by Chand & Kapur in 1970 and R. A. Jarvis in 1973. It has O(nh) time complexity, where n is the number of points in the set, and h is the number of points in the hull. In the worst case the complexity is O(n 2). Graham scan ...

  5. Convex hull - Wikipedia

    en.wikipedia.org/wiki/Convex_hull

    The point set itself forms one endpoint of this family of shapes, and its convex hull forms the other endpoint. [41] The convex layers of a point set are a nested family of convex polygons, the outermost of which is the convex hull, with the inner layers constructed recursively from the points that are not vertices of the convex hull. [47]

  6. List of convexity topics - Wikipedia

    en.wikipedia.org/wiki/List_of_convexity_topics

    Carathéodory's theorem (convex hull) - If a point x of R d lies in the convex hull of a set P, there is a subset of P with d+1 or fewer points such that x lies in its convex hull. Choquet theory - an area of functional analysis and convex analysis concerned with measures with support on the extreme points of a convex set C.

  7. Subgradient method - Wikipedia

    en.wikipedia.org/wiki/Subgradient_method

    In recent years, some interior-point methods have been suggested for convex minimization problems, but subgradient projection methods and related bundle methods of descent remain competitive. For convex minimization problems with very large number of dimensions, subgradient-projection methods are suitable, because they require little storage.

  8. Convex function - Wikipedia

    en.wikipedia.org/wiki/Convex_function

    A strictly convex function is a function that the straight line between any pair of points on the curve is above the curve except for the intersection points between the straight line and the curve. An example of a function which is convex but not strictly convex is f ( x , y ) = x 2 + y {\displaystyle f(x,y)=x^{2}+y} .

  9. Convex analysis - Wikipedia

    en.wikipedia.org/wiki/Convex_analysis

    then is called strictly convex. [1]Convex functions are related to convex sets. Specifically, the function is convex if and only if its epigraph. A function (in black) is convex if and only if its epigraph, which is the region above its graph (in green), is a convex set.