Convex Optimization
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Home > Art, Film & Photography > Convex Optimization: Barrier Function, Conic Optimization, Danskin's Theorem, Duality (Optimization), Duality Gap, Ellipsoid Method, Entrop
Convex Optimization: Barrier Function, Conic Optimization, Danskin's Theorem, Duality (Optimization), Duality Gap, Ellipsoid Method, Entrop

Convex Optimization: Barrier Function, Conic Optimization, Danskin's Theorem, Duality (Optimization), Duality Gap, Ellipsoid Method, Entrop


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About the Book

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 22. Chapters: Barrier function, Conic optimization, Danskin's theorem, Duality (optimization), Duality gap, Ellipsoid method, Entropy maximization, Fenchel's duality theorem, Geodesic convexity, Lagrangian relaxation, Linear programming, Perturbation function, Second-order cone programming, Strong duality, Subgradient method, Weak duality, Wolfe duality. Excerpt: Linear programming (LP, or linear optimization) is a mathematical method for determining a way to achieve the best outcome (such as maximum profit or lowest cost) in a given mathematical model for some list of requirements represented as linear relationships. Linear programming is a specific case of mathematical programming (mathematical optimization). More formally, linear programming is a technique for the optimization of a linear objective function, subject to linear equality and linear inequality constraints. Its feasible region is a convex polyhedron, which is a set defined as the intersection of finitely many half spaces, each of which is defined by a linear inequality. Its objective function is a real-valued affine function defined on this polyhedron. A linear programming algorithm finds a point in the polyhedron where this function has the smallest (or largest) value if such a point exists. Linear programs are problems that can be expressed in canonical form: where x represents the vector of variables (to be determined), c and b are vectors of (known) coefficients, A is a (known) matrix of coefficients, and is the matrix transpose. The expression to be maximized or minimized is called the objective function (cx in this case). The inequalities Ax b are the constraints which specify a convex polytope over which the objective function is to be optimized. In this context, two vectors are comparable when they have the same dimensions. If every entry in the first...


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Product Details
  • ISBN-13: 9781230772219
  • Publisher: Booksllc.Net
  • Publisher Imprint: Booksllc.Net
  • Height: 246 mm
  • No of Pages: 24
  • Spine Width: 1 mm
  • Weight: 64 gr
  • ISBN-10: 1230772219
  • Publisher Date: 13 Sep 2013
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Sub Title: Barrier Function, Conic Optimization, Danskin's Theorem, Duality (Optimization), Duality Gap, Ellipsoid Method, Entrop
  • Width: 189 mm


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