This paper is devoted to the study of an embedding method for semidefinite programming problems using Extended Lagrange-Slater dual (ELSD) and its Lagrangian dual. A theorem proved by de Klerk et al. in 1996 is re...
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This paper is devoted to the study of an embedding method for semidefinite programming problems using Extended Lagrange-Slater dual (ELSD) and its Lagrangian dual. A theorem proved by de Klerk et al. in 1996 is revisited. A new proof is provided utilizing a result regarding the weak feasibility of a conic linear programming problem.
In this paper, we propose a novel general-rank singlegroup multicast beamforming technique using space-time trellis coding (STTC) where a multi-antenna base station broadcasts common information to a group of single-a...
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In this paper, we propose a novel general-rank singlegroup multicast beamforming technique using space-time trellis coding (STTC) where a multi-antenna base station broadcasts common information to a group of single-antenna receivers. Our max-min fair (MMF) based beamforming approach extends the recently introduced concept of orthogonal space-time block codes (OSTBCs) based ranktwo beamforming approach to fully flexible general-rank beamforming. The increase of the rank of the beamforming solution is accompanied with an increased degree of freedom resulting in an optimal beamforming approach with considerable performance gains in terms of bit error rate (BER) and achievable data rate as compared to the state-of-the-art rank-one and rank-two beamforming approaches. Simulations show a substantially improved performance in terms of BER of the proposed technique as compared to the best known techniques.
In this paper we address the beamforming design problem for a full-duplex point-to-point MIMO system under imperfect channel state information (CSI). More specifically, we consider the worst-case beamforming design wh...
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In this paper we address the beamforming design problem for a full-duplex point-to-point MIMO system under imperfect channel state information (CSI). More specifically, we consider the worst-case beamforming design which minimizes the required transmit power subject to total SINR requirements and self-interference constraints which guarantee that the fullduplex device will work in the limited dynamic range of the receiver. This optimization problem is non-convex since it involves an infinite number of constraints which are due to the channel error model. Nevertheless, by applying the S-procedure and the Schur complement, it is possible to transform the original problem into a convex semidefinite programming problem which can be solved using the standard interior-point algorithm. The simulation results have shown that a significant gain is obtained via the proposed robust design especially when the channel error intensity is high and the array size is large.
The optimal power flow (OPF) is a basic optimization problem in the field of power system. The current mainstream convex relaxation methods, including semidefinite programming (SDP), quadratic convex relaxation (QC re...
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ISBN:
(数字)9798350349030
ISBN:
(纸本)9798350349047
The optimal power flow (OPF) is a basic optimization problem in the field of power system. The current mainstream convex relaxation methods, including semidefinite programming (SDP), quadratic convex relaxation (QC relaxation) and second-order cone programming (SOCP), have been well applied in the distribution network. However, when the network has a circle, the SOCP method does not support the solution because of the additional relaxation of the phase angle cycle condition, while the SDP and QC methods are less efficient for solving the transmission network. Based on this situation, an AC OPF convex relaxation and solution method suitable for the transmission network were proposed in this paper. The second-order cone relaxation is performed on the voltage expression in the rectangular form of the OPF problem, and the arctangent envelope method is proposed to relax the phase angle cycle constraint. Finally, the feasibility and effectiveness of the proposed method are verified in the simulation results, and the computational speed of the proposed solution is proved to be high.
For the disjoint 2-catalog segmentation problem (may be inequivalent), we propose a improved polynomial-time randomized approximation algorithm, and obtain a performance ratios ρ which is not less than 0.5 for a wide...
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For the disjoint 2-catalog segmentation problem (may be inequivalent), we propose a improved polynomial-time randomized approximation algorithm, and obtain a performance ratios ρ which is not less than 0.5 for a wide range of this problem. As a result, the 0.699-approximation algorithm for the disjoint equivalent 2-catalog segmentation problem can be obtained.
Finite-time stability and stabilization problem is first investigated for continuous-time polynomial fuzzy systems. The concept of finite-time stability and stabilization is given for polynomial fuzzy systems based on...
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The increasing penetration of renewable energy sources in the electricity grid brings new operational challenges. This brings up the need for effective means to provide demand response in spite of its distributed natu...
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The increasing penetration of renewable energy sources in the electricity grid brings new operational challenges. This brings up the need for effective means to provide demand response in spite of its distributed nature throughout the grid. Aggregators can be created to manage a set of such demand response resources, but deciding how to allocate an aggregator's resources is an important problem. One of the aspects that needs more attention is the impact of the transmission system on these decisions. In this paper, we propose a short-term optimization model for allocating demand response(DR) resources as well as generation resources to supply external demand that is offered after the scheduling decision is made. The DR resources will only be available for use after the scheduling decision is made. Finally, our work also considers the impact of congestion in the transmission system when allocating DR. We propose the use of a semidefinite relaxation to provide a good initial point to solve our model with the aim of guaranteeing that we will find an optimal solution. Results from numerical tests with the IEEE 96-RTS and the ACTIVSG500 test grids are reported. We found that DR resources mitigates congestion management, allowing the generators to supply more of the external demand that is offered. Besides that, we observe that using our proposed solution methodology, we were able to obtain optimal solution for both cases studies, which is not the case when solving the original formulation for the ACTIVSG500 grid.
When almost all underlying assets suddenly lose a certain part of their nominal value in a market crash, the diversification effect of portfolios in a normal market condition no longer works. We integrate the crash ri...
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When almost all underlying assets suddenly lose a certain part of their nominal value in a market crash, the diversification effect of portfolios in a normal market condition no longer works. We integrate the crash risk into portfolio management and investigate performance measures, hedging and optimization of portfolio selection involving derivatives. A suitable convex conic programming framework based on parametric approximation method is proposed to make the problem a tractable one. Simulation analysis and empirical study are performed to test the proposed approach. (C) 2020 Elsevier B.V. All rights reserved.
This paper proposes downside risk measure models in portfolio selection that captures uncertainties both in distribution and in parameters. The worst-case distribution with given information on the mean value and the ...
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This paper proposes downside risk measure models in portfolio selection that captures uncertainties both in distribution and in parameters. The worst-case distribution with given information on the mean value and the covariance matrix is used, together with ellipsoidal and polytopic uncertainty sets, to build-up this type of downside risk model. As an application of the models, the tracking error portfolio selection problem is considered. By lifting the vector variables to positive semidefinite matrix variables, we obtain semidefinite programming formulations of the robust tracking portfolio models. Numerical results are presented in tracking SSE50 of the Shanghai Stock Exchange. Compared with the tracking error variance portfolio model and the equally weighted strategy, the proposed models are more stable, have better accumulated wealth and have much better Sharpe ratio in the investment period for the majority of observed instances. (C) 2013 Elsevier B.V. All rights reserved.
The design of multivariable PID controllers which guarantee the stability of closed loop systems, have a fixed attenuation degree, and robust performance specifications is described. The design of such a multivariable...
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The design of multivariable PID controllers which guarantee the stability of closed loop systems, have a fixed attenuation degree, and robust performance specifications is described. The design of such a multivariable PID controller is transformed to a static output feedback (SOF) problem. The problem of system control is converted into a semi-definite programming problem with LMI constraints, and then by using linear matrix inequalities (LMI) and bilinear matrix inequalities (BMI), the required algorithm is derived and the parameters of the PID controllers can be obtained by further transforms. The validity and robustness of the algorithm is demonstrated by simulation of a numerical example in chemical industry process control.
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