Sentence examples for minimizing a convex from inspiring English sources

The performance function minimization problem with polynomial matrix inequalities is then transformed into a problem of minimizing a convex performance function involving standard LMIs.

Using Corollary 5.1, we next study the problem of minimizing a convex function.

It is well known that minimizing a convex function f can be reduced to finding zero points of the subdifferential mapping ∂f.

It is well known that minimizing a convex function f can be reduced to finding zero points of the subdifferential mapping A = ∂ f.

Such schemes are equivalent to linear algebraic techniques that minimize a convex quadratic objective function with structure induced by the dynamic model.

First, we prove strong convergence result for a problem of finding a point which minimizes a convex function f such that its image under a bounded linear operator A minimizes another convex function g.

Let us consider an operation of a wireless data network with the objective to minimize a convex cost function (or to maximize a concave utility function).

Our problem of interest consists of minimizing a separable, convex and differentiable function over a convex set, defined by bounds on the variables and an explicit constraint described by a separable convex function.

For the computation of the dual function (39) we use the projected subgradient method [6, 14], which is an algorithm for minimizing a nondifferentiable convex function with the main feature of enabling distributed implementation.

Recently, in 2014, Bačák [26] employed a split version of the PPA for minimizing a sum of convex functions in complete (operatorname{CAT}(0)) spaces.

Solving problem (11) is not straightforward because we need to minimize a non-convex function over a convex set.