Exemplos de uso de Convex optimization em Inglês e suas traduções para o Português
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Convex Optimization PDF.
Topics in optimal methods for convex optimization.
This paper proposes a convex optimization scheme based on linear programming and genetic algorithms for the blind equalizers applied to digital communications systems.
The development methodology is based on linear matrix inequalities,that are convex optimization procedures with wide numerical support.
Aiming at improving the robustness of such systems against the snapping shrimp noise,this dissertation proposes a noise reduction technique based on convex optimization.
The design of the repetitive controller gains is done through a convex optimization problem with linear matrix inequalities(lmi) constraints. the formulation of the o.
The proximity operator, introduced by moreau in 1962, is an important tool in the analysis andnumerical solution of convex optimization problems.
The project is treated as a convex optimization problem from which detectors are developed under the minimax criterion, adapted to the presence and absence of impulsive noise.
Such procedures are popularly used to find integer solutions to mixed integer linear programming(MILP) problems,as well as to solve general, not necessarily differentiable convex optimization problems.
Convex optimization usually deals with minimization schemes for which the functions are not differentiable in their entire domain, i.e., these functions have discontinuous gradients.
Philip Starr"Phil" Wolfe(August 11, 1927- December 29, 2016) was an American mathematician andone of the founders of convex optimization theory and mathematical programming.
The problem is formulated as a convex optimization problem with nonlinear constraints and seeks to minimize the distance between the surfaces of bodies and also the maximum interference between them.
Rst model, presented by liu e shen(2009),refers to a promotion time model with its estimation based on an em algorithm variation and using convex optimization techniques for the maximization process.
This work considers convex optimization problems with a separable structure, i.e., to minimize problems the sum of convex functions subject restrictions for each independent variable.
If the objective function is concave(maximization problem), or convex(minimization problem) and the constraint set is convex, then the program is called convex andgeneral methods from convex optimization can be used in most cases.
We present two new methods for solving bilevel convex optimization problems, where both functions are not necessarily differentiable, i.e., we show that the sequences generated by those methods converge to the optimal set of a nonsmooth function subject to a set that also involves a function minimization.
If the objective function is a ratio of a concave and a convex function(in the maximization case) and the constraints are convex, then the problem can be transformed to a convex optimization problem using fractional programming techniques.
Since the conditions are given in the form of linear matrix inequalities(lmis), convex optimization problems can be used for the determination of the trigger function parameters, as well as the co-design of the feedback gain and the trigger function, which are given next. from this basic result, the methodology is extended to the case where occurs the saturation of the actuator.
The underwater acoustic communication system was simulated using the matlab software tool, and the proposed noise reduction technique was treated by the cvx,which is a system for modeling and solving convex optimization problems, developed to work under the matlab platform.
The main objective of this dissertation is the study andimplementation of algorithms for solving convex optimization problems using the language r. specifically, we propose the creation and implementation of the r language packages that solve problems semidefinite programming with applications in the control theory: stability and stabilization of linear time-invariant systems in continuous-time and discrete-time, which are expressed in terms of linear matrix inequalities lmis.
Attitude controllers were designed using apd/h2 discrete control structure, which the gain of h2 part was performed by solving a convex optimization problem, described in linear matrix inequalities form.
In this work an approach theory of geometrical problems and programming optimization theory convex difference function(dc) is shown which is made of a class of geometric programming problems, known as signomiais problems can be written as the difference convex functions and further, a dc problem can be written as cdc which is the canonical form of the problem dc.