On the generic properties of convex optimization problems in conic form
نویسندگان
چکیده
We prove that strict complementarity, primal and dual nondegeneracy of optimal solutions of convex optimization problems in conic form are generic properties. In this paper, we say generic to mean that the set of data possessing the desired property (or properties) has the same Hausdorr measure as the set of data that does not. Our proof is elementary and it employs an important result due to Larman 7] on the boundary structure of convex bodies.
منابع مشابه
Gauge Optimization, Duality, and Applications
Gauge functions significantly generalize the notion of a norm, and gauge optimization, as defined by Freund (1987), seeks the element of a convex set that is minimal with respect to a gauge function. This conceptually simple problem can be used to model a remarkable array of useful problems, including a special case of conic optimization, and related problems that arise in machine learning and ...
متن کاملConic optimization: an elegant framework for convex optimization
The purpose of this survey article is to introduce the reader to a very elegant formulation of convex optimization problems called conic optimization and outline its many advantages. After a brief introduction to convex optimization, the notion of convex cone is introduced, which leads to the conic formulation of convex optimization problems. This formulation features a very symmetric dual prob...
متن کاملGeneralization of Primal-Dual Interior-Point Methods to Convex Optimization Problems in Conic Form
We generalize primal-dual interior-point methods for linear programming problems to the convex optimization problems in conic form. Previously, the most comprehensive theory of symmetric primal-dual interior-point algorithms was given by Nesterov and Todd 8, 9] for the feasible regions expressed as the intersection of a symmetric cone with an aane subspace. In our setting, we allow an arbitrary...
متن کاملA Semismooth Newton Method for Fast, Generic Convex Programming
We introduce Newton-ADMM, a method for fast conic optimization. The basic idea is to view the residuals of consecutive iterates generated by the alternating direction method of multipliers (ADMM) as a set of fixed point equations, and then use a nonsmooth Newton method to find a solution; we apply the basic idea to the Splitting Cone Solver (SCS), a state-of-the-art method for solving generic c...
متن کاملیک الگوریتم کارا برای زیر مسالهی ناحیه اطمینان توسیع یافته با دو قید خطی
Trust region subproblem (TRS), which is the problem of minimizing a quadratic function over a ball, plays a key role in solving unconstrained nonlinear optimization problems. Though TRS is not necessarily convex, there are efficient algorithms to solve it, particularly in large scale. Recently, extensions of TRS with extra linear constraints have received attention of several researchers. It ha...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Math. Program.
دوره 89 شماره
صفحات -
تاریخ انتشار 2001