Computing Optimal Experimental Designs via Interior Point Method
نویسندگان
چکیده
منابع مشابه
Computing Optimal Experimental Designs via Interior Point Method
In this paper, we study optimal experimental design problems with a broad class of smooth convex optimality criteria, including the classical A-, Dand pth mean criterion. In particular, we propose an interior point (IP) method for them and establish its global convergence. Further, by exploiting the structure of the Hessian matrix of the optimality criteria, we derive an explicit formula for co...
متن کاملInterior Point Methods for Optimal Experimental Designs
In this paper, we propose a primal IP method for solving the optimal experimental design problem with a large class of smooth convex optimality criteria, including A-, Dand pth mean criterion, and establish its global convergence. We also show that the Newton direction can be computed efficiently when the size of the moment matrix is small relative to the sample size. We compare our IP method w...
متن کاملOptimal Power Flow in Rectangular Form via an Interior Point Method
In this paper we describe an Interior Point Method (IPM) to solve large scale NonLinear Programming (NLP) problems, tailored to the solution of a specialized Optimal Power Plow (OPF) formulation that uses bus voltages in rectangular coordinates. The distinctive feature of this OPF formulation is that the objective function and constraints are quadratic functions, and such quadratic properties a...
متن کاملInterior Point Method
All forms of the simplex method reach the optimum by traversing a series of basic solutions. Since each basic solution represents an extreme point of the feasible region, the track followed by the algorithm moves around the boundary of the feasible region. In the worst case, it may be necessary to examine most if not all of the extreme points. This can be cripplingly inefficient given that the ...
متن کاملComputing c-optimal experimental designs using the simplex method of linear programming
An experimental design is said to be c-optimal if it minimizes the variance of the best linear unbiased estimator of cTβ , where c is a given vector of coefficients, and β is an unknown vector parameter of the model in consideration. For a linear regression model with uncorrelated observations and a finite experimental domain, the problem of approximate c-optimality is equivalent to a specific ...
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 Matrix Analysis and Applications
سال: 2013
ISSN: 0895-4798,1095-7162
DOI: 10.1137/120895093