Titlebook: Recent Advances in Optimization and its Applications in Engineering; Moritz Diehl,Francois Glineur,Wim Michiels Conference proceedings 201

柳树；枯黄

C. Lageman,R. Sepulchreen necessary to involve foreign multinational extractive companies that have the required technical and managerial expertise. While the terms on which these companies undertook such projects were determined for long by brute force, the instrument of choice today is the web of treaties, contracts, an

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Copositive Programming – a Survey it means optimizing over the cone of so called copositive matrices. Like semidefinite programming, it has proved particularly useful in combinatorial and quadratic optimization. The purpose of this survey is to introduce the field to interested readers in the optimization community who wish to get

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fetter

Solving Infinite-dimensional Optimization Problems by Polynomial Approximationtead, we restrict the decision variable to a sequence of finite-dimensional linear subspaces of the original infinite-dimensional space and solve the corresponding finite-dimensional problems in a efficient way using structured convex optimization techniques.We prove that, under some reasonable assu

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Abstract Cones of Positive Polynomials and Their Sums of Squares Relaxationss of polynomials which can be represented as ratios, with an SOS as numerator and a fixed positive polynomial as denominator. We employ nonlinear transformations of the arguments instead. A fixed cone of positive polynomials, considered as a subset in an abstract coefficient space, corresponds to an

SEED

Asperity

Approximate Geometric Ellipsoid Fitting: A CG-Approach distance of a point to an ellipsoid is used to calculate the fitting parameters. The approach we follow here ensures optimization over the set of all ellipsoids with codimension one rather than allowing for different conics as well. The distance function is analyzed in some detail and a numerical e

Allege

Continuous Reformulation of MINLP Problemsombined computational challenges of the discrete nature of the decision variables and the nonlinearity or even nonconvexity of the equations. By means of a continuous reformulation, the discrete decision variables can be replaced by continuous decision variables and the MINLP can then be solved by r

Enthralling

Local Convergence of Sequential Convex Programming for Nonconvex Optimizationorithm is presented. Under mild conditions the local convergence of the algorithm is proved as a main result of this paper. An application to optimal control illustrates the performance of the proposed algorithm.