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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(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 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.
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 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.
In this paper, we address the short-term scheduling of multiproduct batch plants with parallel identical/ nonidentical reactors, a challenging problem that has been motivated by a real-world application at the Dow Che...
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In this paper, we address the short-term scheduling of multiproduct batch plants with parallel identical/ nonidentical reactors, a challenging problem that has been motivated by a real-world application at the Dow Chemical Company. The main challenges include handling sequence-dependent changeovers with high variance as well as two-stage production with shared intermediate storage and batch splitting. In order to deal with these issues, we propose a new continuous-time mixed-integer linear programming (MILP) model based oil the concept of asynchronized time slots. The proposed model has the unique feature that it incorporates slot-based mass balances and accounts for sequence-dependent changeovers. While effective for small problems, the proposed model becomes computationally expensive to solve for larger problems, mainly because of the fact that the necessary number of slots is not known a priori and the model requires postulating an upper bound on the slots. Therefore, we propose a bilevel decomposition algorithm in which the original problem is decomposed into all upper-level sequencing and a lower-level scheduling and sequencing problem. The upper-level model is a recent planning model where mass balances are aggregated over-time periods and detailed timing constraints are dropped. However, the effects of changeovers are accounted for by incorporating scheduling constraints. Thus, the upper-level model yields very accurate predictions and a tight upper bound. In the lower level, the original problem is solved for the number of slots and subsets of products as predicted by the upper level, yielding a lower bound. These subproblems are solved iteratively until the bounds converge. In order to reduce the number of total iterations, we propose a new class of symmetry breaking constraints. The application of: the proposed approach is illustrated with several examples.
This paper presents the cost optimization of an urban drainage and wastewater treatment system. The mixed sewer urban drainage (including combined sewer overflows and retention basins), the activated sludge wastewater...
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This paper presents the cost optimization of an urban drainage and wastewater treatment system. The mixed sewer urban drainage (including combined sewer overflows and retention basins), the activated sludge wastewater treatment plant (WWTP), and the permissible loading of the receiving water were optimized simultaneously by the nonlinear programming approach. For this purpose, the integrated optimization model OPTIMALWWT was developed. The economic objective function of the defined investment and operational costs is subjected to rigorous design and ecological constraints. A practical example of the cost optimization of an existing urban drainage and WWTP, located in Slovenia, is presented to demonstrate the efficiency of the proposed method. For each of the two different design approaches, three different optimization cases were carried out for three different technological alternatives. As a result, the optimal technological process was finally selected for the reconstruction of the system, as a result of its suitable costs and operational safety. Water Environ. Res., 80, 581 (2008).
This paper presents a canonical duality theory and optimal solutions to a class of global optimization problems subjected to linear inequality constraints. By using the canonical dual transformation developed recently...
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This paper presents a canonical duality theory and optimal solutions to a class of global optimization problems subjected to linear inequality constraints. By using the canonical dual transformation developed recently, a canonical dual problem is formulated, which is perfectly dual to the primal problem. The global minmizer can be identified by the triality theory. Results show that if the global extrema of the original problem are located on the boundary of the primal feasible space, the dual solution should be interior point of the dual feasible set. Several examples are illustrated to show how this theory works. (c) 2008 Elsevier Inc. All rights reserved.
This paper presents a nonlinear, multi-phase and stochastic dynamical system according to engineering background. We show that the stochastic dynamical system exists a unique solution for every initial state. A stocha...
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This paper presents a nonlinear, multi-phase and stochastic dynamical system according to engineering background. We show that the stochastic dynamical system exists a unique solution for every initial state. A stochastic optimal control model is constructed and the sufficient and necessary conditions for optimality are proved via dynamic programming principle. This model can be converted into a parametric nonlinear stochastic programming by integrating the state equation. It is discussed here that the local optimal solution depends in a continuous way on the parameters. A revised Hooke-Jeeves algorithm based on this property has been developed. Computer simulation is used for this paper, and the numerical results illustrate the validity and efficiency of the algorithm. (C) 2007 Elsevier B.V. All rights reserved.
Choosing proper locations for urban transit hubs has always been a critical concern facing urban transportation planning agencies in China. This study proposes a mixed integer optimal location model for urban transit ...
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Choosing proper locations for urban transit hubs has always been a critical concern facing urban transportation planning agencies in China. This study proposes a mixed integer optimal location model for urban transit hubs, with the objective to minimize the demand-weighted total travel time, when explicitly taking into account traffic analysis zones as demand origins or destinations in a target urban area. An integer non-linear programming (INLP) reformulation was developed to reduce the number of variables significantly. Bilinear constraints in the proposed INLP formulation were then remodeled into linear functions to ensure that global optimal solutions were obtained. The model was successfully applied to optimize the hub locations in Suzhou Industrial Park, China, with the result of significantly improved system performance. The effects of several critical factors, such as the number of hubs and the travel time discount coefficient on the system performance, were also investigated.
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