This paper presents a new interior point quadratic programming algorithm which can solve power system optimization problems with significantly less computational efforts. The proposed algorithm has the following two s...
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This paper presents a new interior point quadratic programming algorithm which can solve power system optimization problems with significantly less computational efforts. The proposed algorithm has the following two special features. First, it is based on the path-following interior point algorithm whose search direction is the Newton direction, and therefore the algorithm has quadratic convergence. In the second place, it solves directly a symmetric indefinite system and thus the algorithm avoids the formation of[AD-(1)A(T)] and as a result generates fewer fill-ins than the case of factorizing the positive definite system matrix for large scale systems. This has brought about a profound speed-up. Since the formula of the interior point method have been deduced more generally, the proposed algorithm can start from either a feasible (interior point) or an infeasible point (non-interior point). Numerical results on the IEEE test systems and a Japanese 344 bus system have verified that the proposed algorithm possesses enough robustness and needs significantly less solution time compared with already reported applications of the interior point method.
Ariyawansa and Zhu have introduced a class of volumetric barrier decomposition algorithms [K.A. Ariyawansa, Y. Zhu. A class of polynomial volumetric barrier decomposition algorithms for stochastic semidefinite program...
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Ariyawansa and Zhu have introduced a class of volumetric barrier decomposition algorithms [K.A. Ariyawansa, Y. Zhu. A class of polynomial volumetric barrier decomposition algorithms for stochastic semidefinite programming, Available as Technical Report 2006-7, Department of Mathematics, Washington State University, Pullman, WA, submitted for publication. Available from: < http://***/math/TRS/*** >] for solving two-stage stochastic semidefinite programs with recourse (SSDPs) [K.A. Ariyawansa, Y. Zhu, Stochastic semidefinite programming: a new paradigm for stochastic optimization, 40R-The Quarterly Journal of the Belgian, French and Italian OR Societies, (in press). Available as Technical Report 2004-10, Department of Mathematics, Washington State University, Pullman, WA, October 2004. Available from: < http://***/math/TRS/*** >]. In this paper we utilize their work for SSDPs to derive a class of volumetric barrier decomposition algorithms for solving two-stage stochastic quadratic programs with recourse and to establish polynomial complexity of certain members of the class of algorithms. (c) 2006 Elsevier Inc. All rights reserved.
An approach to determine primal and dual stepsizes in the infeasible-interior-point primal-dual method for convex quadratic problems is presented. The approach reduces the primal and dual infeasibilities in each step ...
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An approach to determine primal and dual stepsizes in the infeasible-interior-point primal-dual method for convex quadratic problems is presented. The approach reduces the primal and dual infeasibilities in each step and allows different stepsizes. The method is derived by investigating the efficient set of a multiobjective optimization problem. Computational results are also given. (C) 1999 Elsevier Science B.V. All rights reserved.
A new torque control strategy for brushless motors is presented, which results in minimum torque ripple and copper losses. The motor model assumes linear magnetics, but contains a current limit which can delimit the o...
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A new torque control strategy for brushless motors is presented, which results in minimum torque ripple and copper losses. The motor model assumes linear magnetics, but contains a current limit which can delimit the onset of magnetic saturation, or be the motor amplifier current limit, whichever is reached first. The control problem is formulated and solved as a quadratic programming problem with equality and inequality constraints to find the nonlinear mapping from desired torque and position to the motor's phase currents. The optimal solution is found in closed form using the Kuhn-Tucker theorem. The solution shows that, unlike the conventional commutation with a fixed current-position waveform, the waveforms of the proposed controller vary in order to respect the current limitation in one phase by boosting the current in the other phases. This increases the maximum torque capability of the motor-in our particular system by 20%-compared to fixed waveform commutation. Experimental data from our brushless direct-drive motor demonstrates that the controller produces virtually ripple-free torque and enhances remarkably the tracking accuracy of the motion controller.
This paper presents a method to estimate the bounds of the radius of the feasible space for a class of constrained nonconvex quadratic programmings. Results show that one may compute a bound of the radius of the feasi...
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This paper presents a method to estimate the bounds of the radius of the feasible space for a class of constrained nonconvex quadratic programmings. Results show that one may compute a bound of the radius of the feasible space by a linear programming which is known to be a P-problem [N. Karmarkar, A new polynomial-time algorithm for linear programming, Combinatorica 4 (1984) 373-395]. It is proposed that one applies this method for using the canonical dual transformation [D.Y. Gao, Canonical duality theory and solutions to constrained nonconvex quadratic programming, J. Global Optimization 29 (2004) 377-399] for solving a standard quadratic programming problem. (c) 2007 Elsevier B.V. All rights reserved.
The paper considers the objective of optimally specifying redundant control effectors under constraints, a problem commonly referred to as control allocation. The problem is posed as a mixed l(2)-norm optimization obj...
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The paper considers the objective of optimally specifying redundant control effectors under constraints, a problem commonly referred to as control allocation. The problem is posed as a mixed l(2)-norm optimization objective and converted to a quadratic programming formulation. The implementation of an interior-point algorithm is presented. Alternative methods including fixed-point and active set methods are used to evaluate the reliability, accuracy and efficiency of a primal-dual interior-point method. While the computational load of the interior-point method is found to be greater for problems of small size, convergence to the optimal solution is also more uniform and predictable. In addition, the properties of the algorithm scale favorably with problem size.
Computing geodesic distances on polyhedral surfaces is an important task in digital geometry processing. Speed and accuracy are two commonly-used measurements of evaluating a discrete geodesic algorithm. In applicatio...
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Computing geodesic distances on polyhedral surfaces is an important task in digital geometry processing. Speed and accuracy are two commonly-used measurements of evaluating a discrete geodesic algorithm. In applications, such as parametrization and shape analysis, a smooth distance field is often preferred over the exact, non-smooth geodesic distance field. We use the term Quasi-geodesic Distance Field (QGDF) to denote a smooth scalar field that is as close as possible to an exact geodesic distance field. In this paper, we formulate the problem of computing QGDF into a standard quadratic programming (QP) problem which maintains a trade-off between accuracy and smoothness. The proposed QP formulation is also flexible in that it can be naturally extended to point clouds and tetrahedral meshes, and support various user-specified constraints. We demonstrate the effectiveness of QGDF in defect-tolerant distances and symmetry-constrained distances. (C) 2020 Elsevier Ltd. All rights reserved.
In deregulated electricity markets, transactions take place between suppliers and buyers located at different nodes of the network. The electrical network connecting such transacting nodes may form complex paths betwe...
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In deregulated electricity markets, transactions take place between suppliers and buyers located at different nodes of the network. The electrical network connecting such transacting nodes may form complex paths between nodes with intermediate nodes and parallel paths. This paper maps the complex meshed electrical network to radial network equivalents to mirror the transactions. Such equivalent networks do not replace the existing power flow analyzes, simultaneous feasibility tests, or other procedures. However, the equivalents obtained by using quadratic programs, retain the properties and laws of physics of the original network while offering a convenient method of tracking market transactions to allocate costs and responsibilities. Applications to the allocation of losses, reactive power, and congestion contracts are shown.
Least squares problems occur frequently in nuclear science including the parameter identification of linear/nonlinear dynamic models, modeling the responses of the spatially distributed detectors, nuclear data treatme...
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Least squares problems occur frequently in nuclear science including the parameter identification of linear/nonlinear dynamic models, modeling the responses of the spatially distributed detectors, nuclear data treatment, response surface modeling of the thermal margin estimation, etc. Considering the inevitable measurement noise and transport kernel simplification, the ill-posedness of the least squares method can arise and limit the applicability of the assumed model structures. In this paper, a constructive method is proposed with the constrained quadratic programming approach to get the physically meaningful solution. The method is applied to the determination of the parameter vector used to estimate the axially 3-level average core power with ex-core detectors. The test results show the remarkable improvement in accuracy and robustness for the noisy measurement data. (C) 2004 Elsevier B.V. All rights reserved.
With an ever-increasing portion of the delay in highspeed CMOS chips attributable to the interconnect, interconnect-circuit design automation continues to grow in importance, By transforming the gate and multilayer wi...
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With an ever-increasing portion of the delay in highspeed CMOS chips attributable to the interconnect, interconnect-circuit design automation continues to grow in importance, By transforming the gate and multilayer wire sizing problem into a convex programming problem for the Elmore delay approximation, we demonstrate the efficacy of a sequential quadratic programming (SQP) solution method, For cases where accuracy greater than that provided by the Elmore delay approximation is required, we apply SQP to the gate and wire sizing problem with more accurate delay models, Since efficient calculation of sensitivities is of paramount importance during SQP, we describe an approach for efficient computation of the RC circuit delay sensitivities.
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