29,942 research outputs found
Semidefinite relaxations for semi-infinite polynomial programming
This paper studies how to solve semi-infinite polynomial programming (SIPP)
problems by semidefinite relaxation method. We first introduce two SDP
relaxation methods for solving polynomial optimization problems with finitely
many constraints. Then we propose an exchange algorithm with SDP relaxations to
solve SIPP problems with compact index set. At last, we extend the proposed
method to SIPP problems with noncompact index set via homogenization. Numerical
results show that the algorithm is efficient in practice.Comment: 23 pages, 4 figure
Computing Optimal Designs of multiresponse Experiments reduces to Second-Order Cone Programming
Elfving's Theorem is a major result in the theory of optimal experimental
design, which gives a geometrical characterization of optimality. In this
paper, we extend this theorem to the case of multiresponse experiments, and we
show that when the number of experiments is finite, and optimal
design of multiresponse experiments can be computed by Second-Order Cone
Programming (SOCP). Moreover, our SOCP approach can deal with design problems
in which the variable is subject to several linear constraints.
We give two proofs of this generalization of Elfving's theorem. One is based
on Lagrangian dualization techniques and relies on the fact that the
semidefinite programming (SDP) formulation of the multiresponse optimal
design always has a solution which is a matrix of rank . Therefore, the
complexity of this problem fades.
We also investigate a \emph{model robust} generalization of optimality,
for which an Elfving-type theorem was established by Dette (1993). We show with
the same Lagrangian approach that these model robust designs can be computed
efficiently by minimizing a geometric mean under some norm constraints.
Moreover, we show that the optimality conditions of this geometric programming
problem yield an extension of Dette's theorem to the case of multiresponse
experiments.
When the number of unknown parameters is small, or when the number of linear
functions of the parameters to be estimated is small, we show by numerical
examples that our approach can be between 10 and 1000 times faster than the
classic, state-of-the-art algorithms
Linear convergence of accelerated conditional gradient algorithms in spaces of measures
A class of generalized conditional gradient algorithms for the solution of
optimization problem in spaces of Radon measures is presented. The method
iteratively inserts additional Dirac-delta functions and optimizes the
corresponding coefficients. Under general assumptions, a sub-linear
rate in the objective functional is obtained, which is sharp
in most cases. To improve efficiency, one can fully resolve the
finite-dimensional subproblems occurring in each iteration of the method. We
provide an analysis for the resulting procedure: under a structural assumption
on the optimal solution, a linear convergence rate is
obtained locally.Comment: 30 pages, 7 figure
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