This paper presents design and implementation of a single-phase three-wire (1phi3W) inverter with grid connection and active power filtering which is based on nonlinear programming and fast-zero-phase detection (FZPD)...
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This paper presents design and implementation of a single-phase three-wire (1phi3W) inverter with grid connection and active power filtering which is based on nonlinear programming and fast-zero-phase detection (FZPD) algorithm. The proposed inverter system can transmit photovoltaic power, compensate harmonic currents, supply reactive power, and balance power at source side even when the line voltages are highly distorted. Simulated and experimental results have verified the FZPD algorithm and demonstrated that when the voltage sources are highly distorted, the proposed inverter system can yield better power quality over that with conventional zero-crossing-detection algorithm.
This paper presents a nonlinear optimal control scheme for the retrieval of an elastically tethered subsatellite model. The scheme accounts for in-plane and out-of-plane motions. The control design is formulated over ...
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This paper presents a nonlinear optimal control scheme for the retrieval of an elastically tethered subsatellite model. The scheme accounts for in-plane and out-of-plane motions. The control design is formulated over an infinite horizon by using a domain-transformation technique and all the nonlinearities in the system model are taken into consideration. Two Legendre pseudospectral algorithms are explored to find an optimal trajectory that guides the subsatellite from an initial position far from the spaceship into a final position close to the spaceship. The first approach involves direct transcription and nonlinear programming, and the second one is based on the method of quasi linearization and matrix algebra. The case studies in the paper well demonstrate the effectiveness and dominant real-time merits of the proposed strategies.
In this paper, we investigate the problem of power optimization in CMOS circuits using gate sizing and voltage selection for a given clock period specification. Several solutions have been proposed for power optimizat...
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ISBN:
(纸本)076952365X
In this paper, we investigate the problem of power optimization in CMOS circuits using gate sizing and voltage selection for a given clock period specification. Several solutions have been proposed for power optimization during gate sizing and voltage selection. Since the problem formulation is nonlinear in nature, nonlinear programming (NLP) based solutions will yield better accuracy, however, convergence is difficult for large circuits. On the other hand, heuristic solutions will result in faster but less accurate solutions. In this work, we propose a new algorithm for gate sizing and voltage selection based on NLP for power optimization. The algorithm uses gate level heuristics for delay assignment which disassociates the delays of all the paths to the individual gate level, and each gate is then separately optimized for power with its delay constraint. Since the optimization is done at the individual gate level, NLP converges quickly while maintaining accuracy. Experimental results are presented for ISCAS benchmarks which clearly illustrate the efficacy of the proposed solution.
We describe an optimization method to approximate the arrival-rate function of a nonhomogeneous Poisson process based on observed arrival data. We estimate the function by cubic splines, using an optimization model ba...
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We describe an optimization method to approximate the arrival-rate function of a nonhomogeneous Poisson process based on observed arrival data. We estimate the function by cubic splines, using an optimization model based on the maximum-likelihood principle. A critical feature of the model is that the splines are constrained to be nonnegative everywhere. We enforce these constraints by using a characterization of nonnegative polynomials by positive semidefinite matrices. We also describe versions of our model that allow for periodic arrival-rate functions and input data of limited time precision. We formulate the estimation problem as a convex nonlinear program, and solve it with standard nonlinear optimization packages. We present numerical results using both an actual record of e-mail arrivals over a period of 60 weeks, and artificially generated data sets. We also present a cross-validation procedure for determining an appropriate number of spline knots to model a set of arrival observations.
Optimization of the firm-level asset-liability model (ALM) is an important part of enterprise risk management. In the context of the property-liability insurer we increase the credibility of the ALM by explicitly unif...
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Optimization of the firm-level asset-liability model (ALM) is an important part of enterprise risk management. In the context of the property-liability insurer we increase the credibility of the ALM by explicitly unifying the efficient management of financial risk factors across both sides of the economic balance sheet. The ALM presented in this research produces a simultaneous solution to the Markowitz mean-variance (MV) allocation of asset- and liability-side resources within a complex hierarchical goal environment. The nonlinear optimization method applied to the dual MV problem that is defined within the overall ALM is a separable program that encapsulates a vector optimized goal-program (NLGP). In addition to the identification of efficient combinations of traded assets and not-traded liabilities within a complex goal environment, the NLGP ALM also proves suitable for the extant characterization of credit, liquidity, and profit margin objectives.
We consider the approximation of nonlinear bilevel mathematical programs by solvable programs of the same type, i.e., bilevel programs involving linear approximations of the upper-level objective and all constraint-de...
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We consider the approximation of nonlinear bilevel mathematical programs by solvable programs of the same type, i.e., bilevel programs involving linear approximations of the upper-level objective and all constraint-defining functions, as well as a quadratic approximation of the lower-level objective. We describe the main features of the algorithm and the resulting software. Numerical experiments tend to confirm the promising behavior of the method.
In this paper we describe an algorithm for solving nonlinear nonconvex programming problems, which is based on the interior point approach. The main theoretical results concern direction determination and step-length ...
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In this paper we describe an algorithm for solving nonlinear nonconvex programming problems, which is based on the interior point approach. The main theoretical results concern direction determination and step-length selection. We split inequality constraints into active and inactive parts to overcome problems with instability. Inactive constraints are eliminated directly, whereas active constraints are used for defining a symmetric indefinite linear system. Inexact solution of this system is obtained iteratively using indefinitely preconditioned conjugate gradient method. Theorems confirming efficiency of the indefinite preconditioner are introduced. Furthermore, a new merit function is defined and a filter principle is used for step-length selection. The algorithm was implemented in the interactive system for universal functional optimization UFO. Results of numerical experiments are reported.
The aim of this paper is to price an American option in a multiperiod binomial model, when there is uncertainty on the volatility of the underlying asset. American option valuation is usually performed, under the risk...
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The aim of this paper is to price an American option in a multiperiod binomial model, when there is uncertainty on the volatility of the underlying asset. American option valuation is usually performed, under the risk-neutral valuation paradigm, by using numerical procedures such as the binomial option pricing model of Cox et al. [J.C. Cox, S.A. Ross, S. Rubinstein, Option pricing, a simplified approach, Journal of Financial Economics 7 (1979) 229-263]. A key input of the multiperiod binomial model is the volatility of the underlying asset, that is an unobservable parameter. As it is hard to give a precise estimate for the volatility, in this paper we use a possibility distribution in order to model the uncertainty on the volatility. Possibility distributions are one of the most popular mathematical tools for modelling uncertainty. The standard risk-neutral valuation paradigm requires the derivation of the risk-neutral probabilities, that in a one-period binomial model boils down to the solution of a linear system of equations. As a consequence of the uncertainty in the volatility, we obtain a possibility distribution on the risk-neutral probabilities. Under these measures, we perform the risk-neutral valuation of the American option. (c) 2007 Elsevier Inc. All rights reserved.
The problem of optimal allocation of fast and slow reactive power VAR devices under different load levels is addressed. These devices are supposed to be utilised to maintain system security in normal and contingency s...
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The problem of optimal allocation of fast and slow reactive power VAR devices under different load levels is addressed. These devices are supposed to be utilised to maintain system security in normal and contingency states, where corrective and preventive controls are implemented for the contingency cases. Load shedding and fast VAR devices are used in the corrective state in order to restore the system stability very quickly, even though they are highly expensive, whereas cheap slow VAR devices can be used in the preventive state to obtain the desired security level. The main objective is to establish a trade-off between economy and security by determining the optimal combination of fast and slow controls (load shedding, new slow and fast VAR devices). To meet the desired steady-state security limits, a variety of constraints have to be considered during the investigated transition states. The overall problem is formulated as a large-scale mixed-integer nonlinear programming problem. Particle swarm optimisation as an efficient method for solving such problems is applied to solve the problem. The proposed approach has been successfully tested on the IEEE-14 as well as IEEE-57 bus systems.
In this paper, a one-layer recurrent neural network with a discontinuous hard-limiting activation function is proposed for quadratic programming. This neural network is capable of solving a large class of quadratic pr...
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In this paper, a one-layer recurrent neural network with a discontinuous hard-limiting activation function is proposed for quadratic programming. This neural network is capable of solving a large class of quadratic programming problems. The state variables of the neural network are proven to be globally stable and the output variables are proven to be convergent to optimal solutions as long as the objective function is strictly convex on a set defined by the equality constraints. In addition, a sequential quadratic programming approach based on the proposed recurrent neural network is developed for general nonlinear programming. Simulation results on numerical examples and support vector machine (SVM) learning show the effectiveness and performance of the neural network.
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