An Inexact Proximal Path-Following Algorithm for Constrained Convex Minimization
نویسندگان
چکیده
منابع مشابه
An Inexact Proximal Path-Following Algorithm for Constrained Convex Minimization
Many scientific and engineering applications feature large-scale non-smooth convex minimization problems over convex sets. In this paper, we address an important instance of this broad class where we assume that the non-smooth objective is equipped with a tractable proximity operator and that the convex constraints afford a self-concordant barrier. We provide a new joint treatment of proximal a...
متن کاملAn Accelerated Inexact Proximal Point Algorithm for Convex Minimization
The proximal point algorithm (PPA) is classical and popular in the community of Optimization. In practice, inexact PPAs which solves the involved proximal subproblems approximately subject to certain inexact criteria are truly implementable. In this paper, we first propose an inexact PPA with a new inexact criterion for solving convex minimization, and show that the iteration-complexity of this...
متن کاملAn inexact proximal method for quasiconvex minimization
In this paper we propose an inexact proximal point method to solve constrained minimization problems with locally Lipschitz quasiconvex objective functions. Assuming that the function is also bounded from below, lower semicontinuous and using proximal distances, we show that the sequence generated for the method converges to a stationary point of the problem.
متن کاملA Proximal Minimization Algorithm for Constrained Optimization
The proximal minimization algorithm (PMA) is an iterative method for minimizing a convex function f(x) over D, the closure of the essential domain of a second convex function h. The PMA is an interior-point algorithm, in the sense that each iterate lies within the interior of D. For each k, the next iterate, x, minimizes the function f(x) +Dh(x, x ), where Dh(x, z) = h(x)− h(z)− 〈∇h(z), x− z〉 i...
متن کاملAn inexact primal-dual path following algorithm for convex quadratic SDP
We propose primal-dual path-following Mehrotra-type predictor-corrector methods for solving convex quadratic semidefinite programming (QSDP) problems of the form: minX{2X • Q(X) + C • X : A(X) = b,X 0}, where Q is a self-adjoint positive semidefinite linear operator on Sn, b ∈ Rm, and A is a linear map from Sn to Rm. At each interior-point iteration, the search direction is computed from a dens...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SIAM Journal on Optimization
سال: 2014
ISSN: 1052-6234,1095-7189
DOI: 10.1137/130944539