Recently, Fletcher and Leyffer proposed using filter methods instead of a merit function to control steplengths in a sequential quadratic programming algorithm. In this paper, we analyze possible ways to implement a f...
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Recently, Fletcher and Leyffer proposed using filter methods instead of a merit function to control steplengths in a sequential quadratic programming algorithm. In this paper, we analyze possible ways to implement a filter-based approach in an interior-point algorithm. Extensive numerical testing shows that such an approach is more efficient than using a merit function alone.
In this paper, we introduce a transformation that converts a class of linear and nonlinear semidefinite programming (SDP) problems into nonlinear optimization problems. For those problems of interest, the transformati...
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In this paper, we introduce a transformation that converts a class of linear and nonlinear semidefinite programming (SDP) problems into nonlinear optimization problems. For those problems of interest, the transformation replaces matrix-valued constraints by vector-valued ones, hence reducing the number of constraints by an order of magnitude. The class of transformable problems includes instances of SDP relaxations of combinatorial optimization problems with binary variables as well as other important SDP problems. We also derive gradient formulas for the objective function of the resulting nonlinear optimization problem and show that both function and gradient evaluations have affordable complexities that effectively exploit the sparsity of the problem data. This transformation, together with the efficient gradient formulas, enables the solution of very large-scale SDP problems by gradient-based nonlinear optimization techniques. In particular, we propose a first-order log-barrier method designed for solving a class of large-scale linear SDP problems. This algorithm operates entirely within the space of the transformed problem while still maintaining close ties with both the primal and the dual of the original SDP problem. Global convergence of the algorithm is established under mild and reasonable assumptions.
The optimum receiver to detect the bits of multiple code-divison multiple access (CDMA) users has exponential complexity in the number of active users in the system. Consequently, many suboptimum receivers have been d...
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The optimum receiver to detect the bits of multiple code-divison multiple access (CDMA) users has exponential complexity in the number of active users in the system. Consequently, many suboptimum receivers have been developed to achieve good performance with less complexity. In this paper, we take the approach of approximating the solution of the optimum multiuser detection problem (OMUD) using nonlinear programming relaxations. First, we observe that some popular suboptimum receivers indeed correspond to relaxations of the optimal detection problem. In particular, one proposed approximation method yields to iterative solutions which correspond to previously proposed heuristic nonlinear detectors. Using a nonlinear programming approach, we identify the convergence properties of these iterative detectors. Secondly, we propose a relaxation that yields a receiver which we call the generalized minimum mean squared error detector. We give a simple iterative implementation of the detector. Its performance is evaluated and comparisons to other suboptimum detection schemes are given.
The identification of the parameters contained in an elastic-plastic material model, apt to simulate steady-state ratchetting, is studied herein with reference to cyclic biaxial tests on cylindrical compact specimens....
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The identification of the parameters contained in an elastic-plastic material model, apt to simulate steady-state ratchetting, is studied herein with reference to cyclic biaxial tests on cylindrical compact specimens. The material considered is steel employed for high-speed train wheels. In order to generate strain states close to those expected in severe service conditions due to wheel-rail rolling-sliding contact, out-of-phase tension-torsion unsymmetric cycles are applied in laboratory tests. The experiments are simulated by conventional finite elements and Chaboche model with nonlinear kinematic hardening. The material parameters are identified through a deterministic, batch (non-sequential) inverse analysis in two stages (genetic and first-order algorithms) in view of the peculiar constraints in the minimization problem. This study leads to the following conclusions of practical use: multiaxial ratchetting tests are desirable to characterize cyclic material behavior in the industrial context considered;compact (instead of thin-walled tubular) specimen tests, combined with parameter identification by inverse analysis, exhibit the advantages of cost effectiveness and of small size needed to assess material properties locally. (c) 2005 Elsevier Ltd. All rights reserved.
In a deregulated power industry, power producing companies bid in the hour-ahead and day-ahead power markets in an attempt to maximize their profit. For a successful bidding strategy, each power-producing company has ...
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In a deregulated power industry, power producing companies bid in the hour-ahead and day-ahead power markets in an attempt to maximize their profit. For a successful bidding strategy, each power-producing company has to generate bidding curves derived from an optimal self-schedule. This self-schedule is commonly obtained from a profit-maximizing optimal power flow model based on predicted locational marginal prices (LMPs). However, at the time of self-scheduling, the predicted values of the LMPs are largely uncertain. Therefore, it is desired to produce robust self-schedules that can be used to lessen the risk resulting from exposure to fluctuating prices. In portfolio optimization theory, methods of risk management include Value-at-Risk (VaR) and conditional Value-at-Risk (CVaR). CVaR is known to be a more consistent measure of risk than VaR. In fact, whilst CVaR is the mean excess loss, the VaR provides no indication on the extent of losses that might be suffered beyond the amount indicated by this measure. This research proposes a method for robust self-scheduling based on CVaR. It will be shown that polynomial interior-point methods can be used to obtain the robust self-schedules from a second-order cone program. The obtained schedules provide a compromise solution between maximum profit and minimum risk. Simulation results on a standard IEEE bus test system will be used to demonstrate the scheduling model based on CVaR.
Combining the norm-relaxed method of feasible direction (MFD) with the idea of strongly sub-feasible direction method, we present a new convergent algorithm with arbitrary initial point for inequality constrained opti...
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Combining the norm-relaxed method of feasible direction (MFD) with the idea of strongly sub-feasible direction method, we present a new convergent algorithm with arbitrary initial point for inequality constrained optimization. At each iteration, the new algorithm solves one direction finding subproblem (DFS) which always possesses a solution. Some good properties of the new algorithm are that it can unify automatically the operations of initialization (Phase I) and optimization (Phase II) and the number of the functions satisfying the inequality constrains is nondecreasing, particularly, a feasible direction of descent can be obtained by solving DFS whenever the iteration point gets into the feasible set. Under some mild assumptions without the linear independence, the global and strong convergence of the algorithm can be obtained. (c) 2004 Elsevier Inc. All rights reserved.
A guidance scheme is proposed for orbital motion under continuous outward radial acceleration that is inversely proportional to the square of the radial distance from the sun. Such an acceleration regime would be real...
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A guidance scheme is proposed for orbital motion under continuous outward radial acceleration that is inversely proportional to the square of the radial distance from the sun. Such an acceleration regime would be realized under the minimagnetospheric plasma propulsion. The maximum attainable radial distance of the outbound trajectory is investigated, and a guidance scheme for achieving this target maximum distance is established under radial acceleration disturbances. The scheme not only provides a control law for continuous radial acceleration but also yields the amount and timing of impulsive maneuvers required to satisfy the guidance requirement at the terminal point.
Using Frank and Wolfe's algorithm, a new interesting nonlinear programming technique has been developed in an attempt to estimate the geometric shape factor of a buried polarized body from a residual self-potentia...
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Using Frank and Wolfe's algorithm, a new interesting nonlinear programming technique has been developed in an attempt to estimate the geometric shape factor of a buried polarized body from a residual self-potential anomaly. Furthermore, the depth, the polarization angle and the electrical dipole moment have also been derived. This algorithm is noted to be robust and its application to SP data converges rapidly towards the optimal solution. The developed technique is tested through studying synthetic data with and without random noise. As a result, the near agreement between the model geometric shape factor and the evaluated one is well recognized. The validity of this proposed technique is tested on a field example from the Ergani Copper district, Turkey. The superiority of the nonlinear programming technique over other recently published methods is shown.
In this work a complete framework is presented for solving nonlinear constrained optimization problems. based on the line-up differential evolution (LUDE) algorithm which is proposed for solving unconstrained problems...
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In this work a complete framework is presented for solving nonlinear constrained optimization problems. based on the line-up differential evolution (LUDE) algorithm which is proposed for solving unconstrained problems. Linear and/or nonlinear constraints are handled by embodying them in an augmented Lagrangian function, where the penalty parameters and multipliers are adapted as the execution of the algorithm proceeds. The LUDE algorithm maintains a population of solutions.. which is continuously improved as it thrives From generation to generation. In each generation the solutions are lined up according to the corresponding objective function values. The position's in the line are very important.. since they determine to What extent the crossover and the mutation operators are applied to each particular solution. The efficiency of the proposed methodoloy is illustrated by solving numerous unconstrained and constrained optimization problems and comparing it With other optimization techniques that can be found in the literature. (C) 2003 Elsevier Ltd. All rights reserved.
We endow the downstream firm in a supply chain with operating costs in addition to the traditional overage and underage costs. We reanalyze Lariviere and Porteus's "Selling to a Newsvendor" model (2001),...
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We endow the downstream firm in a supply chain with operating costs in addition to the traditional overage and underage costs. We reanalyze Lariviere and Porteus's "Selling to a Newsvendor" model (2001), allowing for non-linear production costs, and provide comparative statics. We then explore investment in reducing downstream operating costs. To overcome the fact that investment is lower in a decentralized chain than in an integrated one, we propose several coordination mechanisms-buybacks, revenue sharing and operating subsidy with a license fee. (C) 2003 Elsevier B.V. All rights reserved.
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