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IEEE PES Power Systems Conference and Exposition

作者： Pregelj, A Begovic, M Rohatgi, A Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)078038718X

Summary form only given. Distributed generation (DG) reduces losses and eliminates some of the transmission and distribution costs. It may also reduce fossil fuel emissions, defer capital costs, and improve the distribution feeder voltage conditions. The calculation of the effects of small residential photovoltaic (PV) and wind DG systems on various feeder operating variables is complicated by both the probabilistic nature of their output and the variety of their possible spatial allocations. A method based on a combination of clustering techniques and a convex hull algorithm is proposed that may reduce the computational burden by an order of magnitude, while still allowing accurate estimation of DG-enhanced feeder operation.

关键词： distributed power generation power distribution economics power transmission economics wind power plants photovoltaic power systems fossil fuels convex programming statistical analysis air pollution control cost reduction renewable distributed generation DG loss reduction transmission cost elimination distribution cost elimination fossil fuel emission reduction capital cost distribution feeder voltage condition residential photovoltaic system wind DG system clustering technique convex hull algorithm feeder operation large data set quantitative technique

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An improved weighting method with multibounds formulation and convex programming for multicriteria structural optimization

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING 2001年第9期52卷 889-902页

作者： Zhang, WH Domaszewski, M Fleury, C Northwestern Polytech Univ Dept Aircraft Mfg Engn Sino French Lab Concurrent Engn Xian 710072 Peoples R China Univ Technol Belfort Montbeliard Lab Modelling Mech F-90010 Belfort France Univ Liege Inst Mech LTAS Aerosp Lab B-4000 Liege Belgium

This paper presents an improved weighting method for multicriteria structural optimization. By introducing artificial design variables, here called as multibounds formulation (MBF), we demonstrate mathematically that the weighting combination of criteria can be transformed into a simplified problem with a linear objective function. This is a unified formulation for one criterion and multicriteria problems. Due to the uncoupling of involved criteria after the transformation, the extension and the adaptation of monotonic approximation-based convex programming methods such as the convex linearization (CONLIN) or the method of moving asymptotes (MMA) are made possible to solve multicriteria problems as efficiently as for one criterion problems. In this work, a multicriteria optimization tool is developed by integrating the multibounds formulation with the CONLIN optimizer and the ABAQUS finite element analysis system. Some numerical examples are taken into account to show the efficiency of this approach. Copyright (C) 2001 John Wiley & Sons, Ltd.

关键词： multicriteria optimization weighting method convex programming structural design

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Logarithmic SUMT limits in convex programming

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MATHEMATICAL programming 2001年第1期90卷 113-145页

作者： McCormick, GP Witzgall, C George Washington Univ SEAS Dept Operat Res Washington DC 20052 USA Natl Inst Stand & Technol Math & Computat Sci Gaithersburg MD 20899 USA

The limits of a class of primal and dual solution trajectories associated with the Sequential Unconstrained Minimization Technique (SUMT) are investigated for convex programming problems with non-unique optima. Logarithmic barrier terms are assumed. For linear programming problems, such limits of both primal and dual trajectories - are strongly optimal, strictly complementary, and can be characterized as analytic centers of, loosely speaking, optimality regions. Examples are given, which show that those results do not hold in general for convex programming problems. If the latter are weakly analytic (Bank et al. [3]), primal trajectory limits can be characterized in analogy to the linear programming case and without assuming differentiability. That class of programming problems contains faithfully convex, linear, and convex quadratic programming problems as strict subsets. In the differential case, dual trajectory limits can be characterized similarly, albeit under different conditions, one of which suffices for strict complementarity.

关键词： analytic center barrier function convex programming degeneracy faithfully convex interior point method optimization self-concordant strictly complementary strongly optimal SUMT weakly analytic trajectory Wolfe-dual program

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PENNON: A code for convex nonlinear and semidefinite programming

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OPTIMIZATION METHODS & SOFTWARE 2003年第3期18卷 317-333页

作者： Kocvara, M Stingl, M Univ Erlangen Nurnberg Inst Appl Math D-91058 Erlangen Germany

We introduce a computer program PENNON for the solution of problems of convex Nonlinear and Semidefinite programming (NLP-SDP). The algorithm used in PENNON is a generalized version of the Augmented Lagrangian method, originally introduced by Ben-Tal and Zibulevsky for convex NLP problems. We present generalization of this algorithm to convex NLP-SDP problems, as implemented in PENNON and details of its implementation. The code can also solve second-order conic programming (SOCP) problems, as well as problems with a mixture of SDP, SOCP and NLP constraints. Results of extensive numerical tests and comparison with other optimization codes are presented. The test examples show that PENNON is particularly suitable for large sparse problems.

关键词： convex programming semidefinite programming large-scale problems

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WAVELET-CONSTRAINED IMAGE RESTORATION

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International Journal of Wavelets, Multiresolution and Information Processing 2004年第4期2卷 371-389页

作者： PATRICK L. COMBETTES JEAN-CHRISTOPHE PESQUET Laboratoire Jacques-Louis Lions Université Pierre et Marie Curie – Paris 6 75005 Paris France Institut Gaspard Monge and CNRS UMR 8049 Université de Marne la Vallée 77454 Marne la Vallée Cedex 2 France

Image restoration problems can naturally be cast as constrained convex programming problems in which the constraints arise from a priori information and the observation of signals physically related to the image to be recovered. In this paper, the focus is placed on the construction of constraints based on wavelet representations. Using a mix of statistical and convex-analytical tools, we propose a general framework to construct wavelet-based constraints. The resulting optimization problem is then solved with a block-iterative parallel algorithm which offers great flexibility in terms of implementation. Numerical results illustrate an application of the proposed framework.

关键词： convex programming image restoration image denoising Stein unbiased estimate subgradient projection wavelets

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MBC Algorithms for Nonlinear Systems: An Embedding Approach

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IFAC Proceedings Volumes 2004年第13期37卷 1331-1336页

作者： Alessandro Casavola Domenico Famularo Giuseppe Franzé DEIS - Universitá. degli studi della Calabria Rende(CS). 87036 Italy ICAR - Consiglio Nazionale delle Ricerche Rende (CS) 87036 Italy

This paper considers nonlinear predictive. control via embedding strategies. The nonlinear systems dealt with are the ones whose trajectories can be embedded within those of an auxiliary uncertain LTV system. Both polytopic and norm-bounded robust linear MPC schemes can be used for the embedding. In this paper we aim at presenting a novel normbounded robust predictive control algorithm for input-saturated uncertain discrete-time linear systems and in contrasting its achievements with those pertaining to robust poly topic MPC methods in a final nonlinear example.

关键词： Uncertain linear systems Model predictive control Embedding approach convex programming Min-max techniques

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convex quadratic programming approach

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JOURNAL OF GLOBAL OPTIMIZATION 2001年第3期19卷 291-306页

作者： Cardoso, DM Univ Aveiro Dept Matemat P-3810139 Aveiro Portugal

The problem of determining a maximum matching or whether there exists a perfect matching, is very common in a large variety of applications and as been extensively studied in graph theory. In this paper we start to introduce a characterisation of a family of graphs for which its stability number is determined by convex quadratic programming. The main results connected with the recognition of this family of graphs are also introduced. It follows a necessary and sufficient condition which characterise a graph with a perfect matching and an algorithmic strategy, based on the determination of the stability number of line graphs, by convex quadratic programming, applied to the determination of a perfect matching. A numerical example for the recognition of graphs with a perfect matching is described. Finally, the above algorithmic strategy is extended to the determination of a maximum matching of an arbitrary graph and some related results are presented.

关键词： convex programming graph theory maximum matching graph eigenvalues

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A sequential quadratically constrained quadratic programming method for differentiable convex minimization

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SIAM JOURNAL ON OPTIMIZATION 2003年第4期13卷 1098-1119页

作者： Fukushima, M Luo, ZQ Tseng, P Kyoto Univ Grad Sch Informat Dept Appl Math & Phys Kyoto 6068501 Japan McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L7 Canada Univ Washington Dept Math Seattle WA 98195 USA

This paper presents a sequential quadratically constrained quadratic programming (SQCQP) method for solving smooth convex programs. The SQCQP method solves at each iteration a subproblem that involves convex quadratic inequality constraints as well as a convex quadratic objective function. Such a quadratically constrained quadratic programming problem can be formulated as a second-order cone program, which can be solved efficiently by using interior point methods. We consider the following three fundamental issues on the SQCQP method: the feasibility of subproblems, the global convergence, and the quadratic rate of convergence. In particular, we show that the Maratos effect is avoided without any modification to the search direction, even though we use an ordinary l(1) exact penalty function as the line search merit function.

关键词： convex programming quadratically constrained quadratic programming Maratos effect quadratic convergence global convergence

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convex quadratic programming approach to the maximum matching problem

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Journal of Global Optimization 2001年第1期21卷 91-106页

作者： Cardoso, D.M. Departamento de Matemática Universidade de Aveiro Aveiro Portugal

关键词： convex programming Graph eigenvalues Graph theory Maximum matching

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On convex probabilistic programming with discrete distributions

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NONLINEAR ANALYSIS-THEORY METHODS & APPLICATIONS 2001年第3期47卷 1997-2009页

作者： Dentcheva, D Prékopa, A Ruszczynski, A Rutgers State Univ RUTCOR Piscataway NJ 08854 USA

We consider convex stochastic programming problems with probabilistic constraints involving integer-valued random variables. The concept of a p-efficient point of a probability distribution is used to derive various equivalent problem formulations. Next we introduce the concept of r-concave discrete probability distributions and analyse its relevance for problems under consideration. These notions are used to derive lower and upper bounds for the optimal value of probabilistically constrained convex stochastic programming problems with discrete random variables.

关键词： stochastic programming convex programming probabilistic constraints discrete distributions

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