The purpose of this paper is to develop a quadratic programming method for solving interval-valued cooperative games with fuzzy coalitions. In this method, the interval-valued cooperative games with fuzzy coalitions a...
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ISBN:
(纸本)9789811067532;9789811067525
The purpose of this paper is to develop a quadratic programming method for solving interval-valued cooperative games with fuzzy coalitions. In this method, the interval-valued cooperative games with fuzzy coalitions are converted into the interval-valued cooperative games (with crisp coalitions) by using the Choquet integral. Two auxiliary quadratic programming models for solving the interval-valued cooperative games are constructed by using the least square method and distance between intervals. The proposed models and method are validated and compared with other similar methods. A numerical example is examined to demonstrate the validity, superiority and applicability of the method proposed in this paper.
The dual quadratic programming algorithm of Goldfarb and Idnani is implemented as a solver for a sequential quadratic programming algorithm. Initially the algorithm is briefly described. As the algorithm requires the ...
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The dual quadratic programming algorithm of Goldfarb and Idnani is implemented as a solver for a sequential quadratic programming algorithm. Initially the algorithm is briefly described. As the algorithm requires the inverse of the Cholesky factor of the Hessian matrix at each iteration a procedure is presented to directly obtain a matrix that multiplied by its transpose gives the BFGS update of the Hessian. A procedure is then presented to triangularise the updated factor using two series of Givens rotations. In order to increase efficiency a 'warm start' strategy is proposed whereby the choice of constraints to enter the active set is based on information of previous SQP iterations. Finally two examples are given to demonstrate the efficiency and robustness of the implementation. (C) 2002 Civil-Comp Ltd and Elsevier Science Ltd. All rights reserved.
quadratic programming is a versatile tool for calculating estimates in penalized regression. It can be used to produce estimates based on L-1 roughness penalties, as in total variation denoising. In particular, it can...
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quadratic programming is a versatile tool for calculating estimates in penalized regression. It can be used to produce estimates based on L-1 roughness penalties, as in total variation denoising. In particular, it can calculate estimates when the roughness penalty is the total variation of a derivative of the estimate. Combining two roughness penalties, the total variation and total variation of the third derivative, results in an estimate with continuous second derivative but controls the number of spurious local extreme values. A multiresolution criterion may be included in a quadratic program to achieve local smoothing without having to specify smoothing parameters.
We present a method for assimilating Lagrangian sensor measurement data into a ShallowWater Equation model. Using our method, the variational data assimilation problem is formulated as a quadratic programming problem ...
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ISBN:
(纸本)9781424438723
We present a method for assimilating Lagrangian sensor measurement data into a ShallowWater Equation model. Using our method, the variational data assimilation problem is formulated as a quadratic programming problem with linear constraints. Drifting sensors that gather position and velocity information in the modeled system can then be used to refine the estimate of the initial conditions of the system. A new sensor network hardware platform for gathering flow information is presented. We summarize the results of a field experiment designed to demonstrate the capabilities of our assimilation method with data gathered from the sensors. Validation of the results is performed by comparing them to an estimate derived from an independent set of static sensors.
Demand-Side Management (DSM) is an effective means to optimize resource utilization in the electricity grid. It makes the electricity consumption pattern of users more even, reducing the Peak-to-Average demand Ratio (...
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ISBN:
(数字)9783319188027
ISBN:
(纸本)9783319188027;9783319188010
Demand-Side Management (DSM) is an effective means to optimize resource utilization in the electricity grid. It makes the electricity consumption pattern of users more even, reducing the Peak-to-Average demand Ratio (PAR) in the power system. The utility company can monitor and shape the hourly electricity consumption of the users by adopting an appropriate pricing strategy and advertising it online exploiting the underlying Smart Grid infrastructure. On the other hand, the users can monitor the hourly price of electricity in the market and based on the price variation, they can schedule their appliances to minimize their electricity payment, without compromising their daily need. In this paper, we consider a DSM problem where the company adopts a quadratic pricing strategy to encourage the users to have a flat consumption pattern. We formulate the problem incorporating quadratic programming (QP). The simulation results show that the QP approach reduces the PAR drastically.
We show that the problem of minimizing a concave quadratic function with one concave direction is NP-hard. This result can be interpreted as an attempt to understand exactly what makes nonconvex quadratic programming ...
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This paper proposes an approach for optimal power flow considering several contingency states. Initially, contingency selection is conducted to measure how much a specific contingency may affect the operation cost. Th...
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ISBN:
(纸本)9781467378635
This paper proposes an approach for optimal power flow considering several contingency states. Initially, contingency selection is conducted to measure how much a specific contingency may affect the operation cost. Then, some severe contingencies are incorporated into optimal power flow problem. All considered states, normal and contingency states, are simulated simultaneously. Thus, if contingency occurs, it can be ensured that all constraints such as generation limit, transmission limit and ramp rate will be satisfied. To solve the problem, quadratic programming is applied. IEEE 30 bus system is used as test system to show the ability of the proposed approach.
In this paper, we present an inverse kinematics control law to control the motion of a six-legged robot intended for use in humanitarian demining. We exploit a quadratic programming (QP) method to resolve the constrai...
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ISBN:
(纸本)9781509033423
In this paper, we present an inverse kinematics control law to control the motion of a six-legged robot intended for use in humanitarian demining. We exploit a quadratic programming (QP) method to resolve the constrained kinematic redundancy problem and we include both inequality and equality constraints such as joint rate and joint angle limits. The problem of redundancy resolution is considered at the inverse differential kinematics level. Task prioritization for the hexapod robot with sensor head has been used to address the issue of robot balance, walking and manipulator motion. This work describes the whole-body control framework, based on task prioritization with inequality constrained QP applied at different levels. The algorithm has been tested on a three degrees of freedom leg as part of a six legged robot with sensor head carrier arm. We present a simulation on the robot using MatLab.
In this paper, we develop an efficient quadratic programming (QP) decoding algorithm via the alternating direction method of multipliers (ADMM) technique for binary low density parity check (LDPC) codes. Its main cont...
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ISBN:
(纸本)9781728150895
In this paper, we develop an efficient quadratic programming (QP) decoding algorithm via the alternating direction method of multipliers (ADMM) technique for binary low density parity check (LDPC) codes. Its main content is as follows: first, through transforming the three-variables parity check equation to its equivalent expression, we relax the maximum likelihood decoding problem to a quadratic program. Second, the ADMM technique is exploited to design the solving algorithm of the resulting QP decoding model. Compared with the existing ADMMbased mathematical programming (MP) decoding algorithms, our proposed algorithm eliminates complex Euclidean projection onto the check polytope. Third, we prove that the proposed algorithm satisfies the favorable property of all-zeros assumption. Moreover, by exploiting the inside structure of the QP model, we show that the decoding complexity of our proposed algorithm in each iteration is linear in terms of LDPC code length. Simulation results demonstrate that the proposed QP decoder attains better error-correction performance than the sum-product BP decoder and costs the least amount of decoding time amongst the stateof-the-art ADMM-based MP decoding algorithms.
The dual quadratic programming algorithm of Goldfarb and Idnani is implemented as a solver for a sequential quadratic programming algorithm. Initially the algorithm is briefly described. As the algorithm requires the ...
详细信息
The dual quadratic programming algorithm of Goldfarb and Idnani is implemented as a solver for a sequential quadratic programming algorithm. Initially the algorithm is briefly described. As the algorithm requires the inverse of the Cholesky factor of the Hessian matrix at each iteration a procedure is presented to directly obtain a matrix that multiplied by its transpose gives the BFGS update of the Hessian. A procedure is then presented to triangularise the updated factor using two series of Givens rotations. In order to increase efficiency a 'warm start' strategy is proposed whereby the choice of constraints to enter the active set is based on information of previous SQP iterations. Finally two examples are given to demonstrate the efficiency and robustness of the implementation. (C) 2002 Civil-Comp Ltd and Elsevier Science Ltd. All rights reserved.
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