An abstract indefinite least squares problem with a quadratic constraint is considered. This is a quadraticprogramming problem with one quadratic equality constraint, where neither the objective nor the constraint is...
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An abstract indefinite least squares problem with a quadratic constraint is considered. This is a quadraticprogramming problem with one quadratic equality constraint, where neither the objective nor the constraint is a convex function. Necessary and sufficient conditions are found for the existence of solutions. (C) 2022 Elsevier Inc. All rights reserved.
This paper examines the nonconvex quadratically constrained quadratic programming (QCQP) problems using a decomposition method. It is well known that a QCQP can be transformed into a rank-one constrained optimization ...
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ISBN:
(纸本)9781509045839
This paper examines the nonconvex quadratically constrained quadratic programming (QCQP) problems using a decomposition method. It is well known that a QCQP can be transformed into a rank-one constrained optimization problem. Finding a rank-one matrix is computationally complicated, especially for large scale QCQPs. A decomposition method is applied to decompose the single rank-one constraint on original unknown matrix into multiple rank-one constraints on small scale submatrices. An iterative rank minimization (IRM) algorithm is then proposed to gradually approach all of the rank-one constraints. To satisfy each rank-one constraint in the decomposed formulation, linear matrix inequalities (LMIs) are introduced in IRM with local convergence analysis. The decomposition method reduces the overall computational cost by decreasing size of LMIs, especially when the problem is sparse. Simulation examples with comparative results obtained from an alternative method are presented to demonstrate advantages of the proposed method.
This article studies the problem of real-time relative pose estimation of multi-UAV systems based on inter-UAV distance measurement and onboard odometry. In large-scale UAV systems, the centralized localization proble...
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ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
This article studies the problem of real-time relative pose estimation of multi-UAV systems based on inter-UAV distance measurement and onboard odometry. In large-scale UAV systems, the centralized localization problem using only distance measurements is challenging from the perspective of computational burden. The concerned relative pose estimation problem is formulated as a squared distance weighted least squares problem and is then decomposed to be executed on each UAV. Constraints on the relative poses of neighboring UAVs with mutual distance measurements are added to the problem under the condition that some UAVs lack direct distance measurements, subsequently transforming it into a quadratically constrained quadratic programming(QCQP) form for solving. Simulation experiments show that the proposed optimization problem is effective in real-time relative pose estimation of large-scale UAVs with distance measurements and odometry, and can yield more accurate pose estimates than the relevant literature.
In this paper, we introduce a procedure for the null broadening algorithm design with respect to the perturbation of the interference location. This method is based on maximizing the array output signal-to-interferenc...
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In this paper, we introduce a procedure for the null broadening algorithm design with respect to the perturbation of the interference location. This method is based on maximizing the array output signal-to-interference-plus-noise-ratio(SINR)subject to quadratic constraints. The design problem can be cast as a fractional quadratically constrained quadratic programming(QCQP) problem that can be solved efficiently using the semidefinite programming(SDP) techniques, the semidefinite relaxation can be used to obtain a lower bound on the optimal objective function. This proposed approach imposes broadened nulls towards the interference region while possesses well-maintained pattern. Theoretical analysis and numerical results demonstrate that the performance of proposed adaptive beamformer is almost always close to optimal.
Vision-based control of mobile robots in formation has received a lot of attention. Real-time and efficient control is crucial for practical applications. An efficient model predictive control method is proposed using...
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ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
Vision-based control of mobile robots in formation has received a lot of attention. Real-time and efficient control is crucial for practical applications. An efficient model predictive control method is proposed using a recurrent neural network(RNN)-based optimizer to obtain optimal solutions in real-time. First, an efficient and robust model predictive control method is introduced for vision-based mobile robot formation control with stability constraints. Second, an RNN solver that decomposes the QCQP problem into a series of subproblems for solving is proposed. Finally, the applicability and performance of the proposed control scheme are demonstrated by typical hardware experiments.
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