Conjugate gradient optimization algorithms depend on the search directions with different choices for the parameter in the search directions. In this note, conditions are given on the parameter in the conjugate gradie...
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Conjugate gradient optimization algorithms depend on the search directions with different choices for the parameter in the search directions. In this note, conditions are given on the parameter in the conjugate gradient directions to ensure the descent property of the search directions. Global convergence of such a class of methods is discussed. It is shown that, using reverse modulus of continuity function and forcing function, the new method for solving unconstrained optimization can work for a continuously differentiable function with a modification of the Curry-Altman's step-size rule and a bounded level set. Combining PR method with our new method, PR method is modified to have global convergence *** experiments show that the new methods are efficient by comparing with FRconjugate gradient method.
A nonlinear integer bilevel programming is well tackled by a new global optimization method proposed herein. The proposed method is performed within a two-level frame. The upper-level problem is first continualized, a...
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A nonlinear integer bilevel programming is well tackled by a new global optimization method proposed herein. The proposed method is performed within a two-level frame. The upper-level problem is first continualized, and the continualized problem is then solved using the simulated annealing algorithm. Meanwhile given the information from the upper level, the lower level programming is easily solved after being changed into a non-constrained optimization problem with the help of a discrete searching technique. The resultant solution from the proposed method is the global optimum. The new method is finally demonstrated by a numerical example, with solution satisfied and the method well verified.
This paper discussed the application of the method of the simplex-lattice design for predicting the properties of cement-based composites. On the basis of the compressive strength, its use was demonstrated on ternary ...
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This paper discussed the application of the method of the simplex-lattice design for predicting the properties of cement-based composites. On the basis of the compressive strength, its use was demonstrated on ternary paste systems composed of cement, silica fume and fly ash with constant water to binder ratio and a mass fraction of mineral admixtures not exceeding 30%. The regression model between compressive strength of paste and binder proportion was built up. The F-test method was utilized for validation of the regression model. The nonlinear programming system with upper and lower bound was solved. This. allowed assessment of optimum mixture proportions and corresponding maximum compressive strength. It was shown that: (1) the 3rd-order regression model constructed by using the simplex-lattice design method could accurately predict the compressive strength in ternary paste system made of cement, silica fume and fly ash (the total mass fraction of all mineral admixtures was up to 30%);(2) to solve the nonlinear programming with the constraints of upper and lower bound played an important role in getting the optimum compressive strength and its corresponding mixture proportion at different ages;(3) the combination of the simplex-lattice design method and the optimization theory could be valuable tool for optimization of cement-based composites' properties.
The concept for fuzzy estimators of fuzzy parameters in fuzzy random variables is proposed in this paper. Due to the "Resolution Identity", the construction of a fuzzy number from a family of closed interval...
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The concept for fuzzy estimators of fuzzy parameters in fuzzy random variables is proposed in this paper. Due to the "Resolution Identity", the construction of a fuzzy number from a family of closed intervals is developed. The construction of the fuzzy estimators of fuzzy parameters from the known conventional estimators inherits evidently from the aforementioned technique. In the meantime, we prove that the fuzzy estimator is a fuzzy random variable in order to provide the notion of fuzzy expectation and variance of fuzzy estimator. The computational procedure is also provided by solving the related nonlinear programming problems that are transformed from the original problem for the purpose of evaluation of the membership values of the fuzzy estimates. (C) 2002 Elsevier Science B.V. All rights reserved.
作者：
Park, SYMazanek, DDYonsei Univ
Ctr Astrodynam & Space Technol Dept Astron Seoul 120749 South Korea NASA
Langley Res Ctr Spacecraft & Sensors Branch Hampton VA 23681 USA
A detailed optimization problem is formulated to calculate optimal impulses for deflecting Earth-crossing asteroids/comets,using nonlinear programming. The constrained optimization problem is based on a three-dimensio...
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A detailed optimization problem is formulated to calculate optimal impulses for deflecting Earth-crossing asteroids/comets,using nonlinear programming. The constrained optimization problem is based on a three-dimensional patched conic method to include the Earth's gravitational effects and the asteroid/comet's orbital inclination. The magnitudes and impulse angles of optimal DeltaV are accurately computed at various points on the asteroid/comet's orbit to provide a given target separation distance. Interceptor mass (or energy) is estimated for various deflection strategies such as high-thrust engine, kinetic deflection, nuclear detonation, and laser ablation. The potential ability of each mitigation scheme, in conjunction with several future spacecraft concepts, is also described. The optimal DeltaV and deflection strategy are dependent on the size and the orbital elements of the asteroid/comet, as well as the amount of warning time.
This paper investigates the series-parallel redundant reliability problems subject to multiple separable linear constraints, where each subsystem has multiple component choices. The problems generalize the typical ser...
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This paper investigates the series-parallel redundant reliability problems subject to multiple separable linear constraints, where each subsystem has multiple component choices. The problems generalize the typical series-parallel reliability problems when the number of component choices for each subsystem is set to one. Instead of conventional approaches, e.g. dynamic programming, geometric programming and piecewise linear approximation, a simple linear programming approach is proposed to approximate the integer nonlinear programming problem. Numerical results for test problems with single (multiple) component choice(s) are reported and compared. Limited numerical results demonstrate the efficiency and the effectiveness of the proposed approach. Additionally, results obtained from the approach proposed herein might provide an effective lower bound for branch-and-bound methods to obtain the global optimum for the problem. (C) 2002 Elsevier Science Ltd. All rights reserved.
A method for optimizing the variety of a modular products, manufactured in a Reconfigurable Manufacturing System, is proposed. The optimization is achieved through appropriately selecting the subsets of module instanc...
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A method for optimizing the variety of a modular products, manufactured in a Reconfigurable Manufacturing System, is proposed. The optimization is achieved through appropriately selecting the subsets of module instances from given sets. The problem is formulated as an integer nonlinear programming problem to find a trade-off between the quality loss due to modularity and the cost of reconfiguration for given sets of customer requirements. The proposed formulation is general in the sense that products can have any number of modules. The formulation is an extension to the available formulation that was developed for products with only two modules. Moreover, the current work addresses the effect of different order priorities, customer importance, and demands. The proposed method has been applied to a modular assembly problem and found to be efficient in determining optimum subsets of module instances.
In this paper we consider the purchasing decisions facing a buying firm which receives incrementally discounted price schedules for a group of items in the presence of constraints such as budgets and space limitations...
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In this paper we consider the purchasing decisions facing a buying firm which receives incrementally discounted price schedules for a group of items in the presence of constraints such as budgets and space limitations. The constraints take the form of upper bounds on positively weighted linear combinations of order size, dollar volume, unit price and order frequency. We adapt the solution procedures from an earlier paper [Naval Res. Logis. 40 (2) (1993) 255] to treat this case. The results show that simultaneous order quantities for multiple items, offered with separate incremental discounts and subject to joint resource constraints, can be calculated efficiently using a combination of Lagrangean relaxation and, as needed, partial enumeration. This problem proved to be no more and no less tractable than its counterpart with all units discounts. (C) 2002 Elsevier Science B.V. All rights reserved.
Mathematical programs with equilibrium constraints (MPECs) form a relatively new and interesting subclass of nonlinear programming problems. In this paper we propose a novel method of solving MPECs by appropriate refo...
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Mathematical programs with equilibrium constraints (MPECs) form a relatively new and interesting subclass of nonlinear programming problems. In this paper we propose a novel method of solving MPECs by appropriate reformulation of the equilibrium conditions. The reformulation can be easily incorporated in a certain class of interior point algorithms for nonlinear optimization. The algorithm used in the study follows a primal-dual interior point approach and shows encouraging results on a test suite of MPECs. The algorithm is also able to perform optimization of distillation columns with phase changes and tray optimization using only continuous variables. We also consider a number of topics to improve performance of the algorithm and to identify classes of process engineering problems that can be handled as MPECs. (C) 2003 Elsevier Science Ltd. All rights reserved.
In this paper, we propose a method to solve the distributed constrained state-estimation problem and discuss the associated implementation. This method includes a successive quadratic programming process and a paralle...
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In this paper, we propose a method to solve the distributed constrained state-estimation problem and discuss the associated implementation. This method includes a successive quadratic programming process and a parallel dual-type method possessing decomposition effects. With the decomposition, our method achieves a dramatic speed-up ratio compared with the commercial IMSL subroutines in solving CWLS problems. (C) 2003 Elsevier Science B.V. All rights reserved.
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