25 research outputs found
Intrinsic Volumes of Polyhedral Cones: A combinatorial perspective
The theory of intrinsic volumes of convex cones has recently found striking
applications in areas such as convex optimization and compressive sensing. This
article provides a self-contained account of the combinatorial theory of
intrinsic volumes for polyhedral cones. Direct derivations of the General
Steiner formula, the conic analogues of the Brianchon-Gram-Euler and the
Gauss-Bonnet relations, and the Principal Kinematic Formula are given. In
addition, a connection between the characteristic polynomial of a hyperplane
arrangement and the intrinsic volumes of the regions of the arrangement, due to
Klivans and Swartz, is generalized and some applications are presented.Comment: Survey, 23 page
Gordon's inequality and condition numbers in conic optimization
The probabilistic analysis of condition numbers has traditionally been
approached from different angles; one is based on Smale's program in complexity
theory and features integral geometry, while the other is motivated by
geometric functional analysis and makes use of the theory of Gaussian
processes. In this note we explore connections between the two approaches in
the context of the biconic homogeneous feasiblity problem and the condition
numbers motivated by conic optimization theory. Key tools in the analysis are
Slepian's and Gordon's comparision inequalities for Gaussian processes,
interpreted as monotonicity properties of moment functionals, and their
interplay with ideas from conic integral geometry
Effective Condition Number Bounds for Convex Regularization
We derive bounds relating Renegar's condition number to quantities that
govern the statistical performance of convex regularization in settings that
include the -analysis setting. Using results from conic integral
geometry, we show that the bounds can be made to depend only on a random
projection, or restriction, of the analysis operator to a lower dimensional
space, and can still be effective if these operators are ill-conditioned. As an
application, we get new bounds for the undersampling phase transition of
composite convex regularizers. Key tools in the analysis are Slepian's
inequality and the kinematic formula from integral geometry.Comment: 17 pages, 4 figures . arXiv admin note: text overlap with
arXiv:1408.301
Robust Smoothed Analysis of a Condition Number for Linear Programming
We perform a smoothed analysis of the GCC-condition number C(A) of the linear
programming feasibility problem \exists x\in\R^{m+1} Ax < 0. Suppose that
\bar{A} is any matrix with rows \bar{a_i} of euclidean norm 1 and,
independently for all i, let a_i be a random perturbation of \bar{a_i}
following the uniform distribution in the spherical disk in S^m of angular
radius \arcsin\sigma and centered at \bar{a_i}. We prove that E(\ln C(A)) =
O(mn / \sigma). A similar result was shown for Renegar's condition number and
Gaussian perturbations by Dunagan, Spielman, and Teng [arXiv:cs.DS/0302011].
Our result is robust in the sense that it easily extends to radially symmetric
probability distributions supported on a spherical disk of radius
\arcsin\sigma, whose density may even have a singularity at the center of the
perturbation. Our proofs combine ideas from a recent paper of B\"urgisser,
Cucker, and Lotz (Math. Comp. 77, No. 263, 2008) with techniques of Dunagan et
al.Comment: 34 pages. Version 3: only cosmetic change
Effective condition number bounds for convex regularization
We derive bounds relating Renegar's condition number to quantities that govern the statistical performance of convex regularization in settings that include the â„“ 1 -analysis setting. Using results from conic integral geometry, we show that the bounds can be made to depend only on a random projection, or restriction, of the analysis operator to a lower dimensional space, and can still be effective if these operators are ill-conditioned. As an application, we get new bounds for the undersampling phase transition of composite convex regularizers. Key tools in the analysis are Slepian's inequality and the kinematic formula from integral geometry