Sentence examples for minimizing a nonconvex from inspiring English sources
As the calculation of censored quantile regressions involves minimizing a nonconvex and nondifferentiable function, global optimization techniques can be the only breakthroughs.
minimizing a nonconvex function on a convex set; minimizing a convex/nonconvex function on a nonconvex set; minimizing a convex/nonconvex function on a convex/nonconvex set with integer variables.
In terms of optimization, nonconvex problems appeared in CS can be divided into three classes: minimizing a nonconvex function on a convex set; minimizing a convex/nonconvex function on a nonconvex set; minimizing a convex/nonconvex function on a convex/nonconvex set with integer variables.
On the other hand, we generalize the method of [13] from minimizing a single nonconvex nonsmooth function to the sum of two functions.
In this paper, we study a new class of nonconvex multitime multiobjective variational problems of minimizing a vector-valued functional of curvilinear integral type.
In this paper, we propose an alternating linearization bundle method for minimizing the sum of a nonconvex function and a convex function, both of which are not necessarily differentiable.
In this paper, we consider to minimize the sum of a nonconvex function and a convex function with the form of (1).
The joint optimization problem, which is to minimize an objective function based on the mean square error (MSE), is formulated as a nonconvex optimization problem in the AF MIMO relay system.
On one hand, we generalize the method of [9] from minimizing the sum of two convex functions to the sum of a nonconvex function and a convex function.
Attouch et al. [3] and Bolte et al. [4] considered a broad class of nonconvex and nonsmooth minimization problems that include as a special case minimizing the sum of two nonconvex functions, in which the proximal alternating minimization technique is used and the Kurdyka Łojasiewicz property is assumed.
This paper proves that a certain class of nonconvex matrix inequalities is equivalent to linear matrix inequalities (LMIs) plus a nonconvex rank constraint.
