A proximal point algorithm with a ϕ-divergence for quasiconvex programming
نویسندگان
چکیده
منابع مشابه
A Proximal Point Algorithm with φ-Divergence to Quasiconvex Programming
We use the proximal point method with the φ-divergence given by φ(t) = t− log t−1 for the minimization of quasiconvex functions subject to nonnegativity constraints. We establish that the sequence generated by our algorithm is well-defined in the sense that it exists and it is not cyclical. Without any assumption of boundedness level to the objective function, we obtain that the sequence conver...
متن کاملGeneralized Proximal Point Algorithms for Quasiconvex Programming
In this paper, we proposed algorithms interior proximal methods based on entropylike distance for the minimization of the quasiconvex function subjected to nonnegativity constraints. Under the assumptions that the objective function is bounded below and continuously differentiable, we established the well definedness of the sequence generated by the algorithms and obtained two important converg...
متن کاملA path-following infeasible interior-point algorithm for semidefinite programming
We present a new algorithm obtained by changing the search directions in the algorithm given in [8]. This algorithm is based on a new technique for finding the search direction and the strategy of the central path. At each iteration, we use only the full Nesterov-Todd (NT)step. Moreover, we obtain the currently best known iteration bound for the infeasible interior-point algorithms with full NT...
متن کاملA Proximal Point Algorithm with Bregman Distances for Quasiconvex Optimization over the Positive Orthant
We present an interior proximal point method with Bregman distance, whose Bregman function is separable and the zone is the interior of the positive orthant, for solving the quasiconvex optimization problem under nonnegative constraints. We establish that the sequence generated by our algorithm is well defined and we prove convergence to a solution point when the sequence of parameters goes to ...
متن کاملA scalarization proximal point method for quasiconvex multiobjective minimization
In this paper we propose a scalarization proximal point method to solve multiobjective unconstrained minimization problems with locally Lipschitz and quasiconvex vector functions. We prove, under natural assumptions, that the sequence generated by the method is well defined and converges globally to a Pareto-Clarke critical point. Our method may be seen as an extension, for the non convex case,...
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: Optimization
سال: 2010
ISSN: 0233-1934,1029-4945
DOI: 10.1080/02331930902884273