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Multiple object tracking has been formulated recently as a global optimization problem, and solved efficiently with optimal methods such as the Hungarian Algorithm. A se-vere limitation is the inability to model multiple objects that are merged into a single measurement, and track them as a group

Abstract—In this paper, a method to synthesize controllers that achieve globally optimal robust performance within any prespec-ified tolerance, against structured norm-bounded time-varying and/or nonlinear uncertainty is developed. The performance measure considered is the infinity to infinity

number of designs using global data fitting such as DACE. The optimization algorithm repetitive analysis needs are all performed using the cheap metamodels. In this study a two-stage approach is employed based on the Efficient Global Optimization algorithm, EGO, due to Jones. First an initial sample

This paper is concerned with the single-row facility layout problem (SRFLP). A globally optimal solution to the SRFLP is a linear placement of rectangular facilities with varying lengths that achieves the minimum total cost associated with the (known or projected) interactions between them. We

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matrix inequality (LMI) problems. A notion of Karush-Kuhn-Tucker polynomials is introduced in a global optimality condition. Some illustrative examples are provided.

point of view. A directed graph minimum edge cost network flow modeling of the problem for a truly multistage formulation using mathematical programming that guarantees global optimality and addresses the noted deficiencies is proposed. The proposed formulation is implemented on small case studies under

of the underlying cut polytope are automatically enforced. On the computational side, we use the SDP relaxation to obtain globally optimal layouts for large SRFLPs with up to thirty facilities, some of which have remained unsolved since 1988.

Abstract not found