This paper considers Kuhn–Tucker duality without differentiability conditions for the types of domains recently treated in the literature in which the two orthants of the classical Kuhn–Tucker theory are replaced by...
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This paper considers Kuhn–Tucker duality without differentiability conditions for the types of domains recently treated in the literature in which the two orthants of the classical Kuhn–Tucker theory are replaced by a convex cone L and a set C that is typically, but not necessarily, convex. A natural modification of the Slater condition, in addition to the convexity of a certain auxiliary set, yields sufficiency of the constrained optimization problem for the associated saddle-point problem. No conditions are required for the converse.
This paper searches the best solution for the stages of noise and bandwidth of negative feedback amplifiers by resorting to Structured Electronic Design, through optimization methods. On one side, noise optimization i...
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ISBN:
(纸本)9780769529745
This paper searches the best solution for the stages of noise and bandwidth of negative feedback amplifiers by resorting to Structured Electronic Design, through optimization methods. On one side, noise optimization is achieved by establishing the noise-characteristic as a function of bias current. On the other side, bandwidth optimization is obtained by establishing the equation for the open loop gain pole-product (LP product). Both aspects are defined as nonlinear programming (NLP) problems, where the design variables are related with the parameters of the device (bipolar transistors) used to synthesize the amplifiers. Differential Evolution is used to solve the noise NLP problem and the Hooke-Jeeves method is used to solve the bandwidth NLP problem. The obtained results are presented and some conclusions are established.
An algorithm is developed that will select a hospital's charge structure to maximize reimbursements. Although the algorithm relies on the Kuhn-Tucker theory of nonlinear programming, it may be implemented using el...
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An algorithm is developed that will select a hospital's charge structure to maximize reimbursements. Although the algorithm relies on the Kuhn-Tucker theory of nonlinear programming, it may be implemented using elementary sorting techniques.
When one solves nonlinear programming problems by means of algorithms that use merit criteria combining the objective function and penalty feasibility terms, a phenomenon called greediness may occur. Unconstrained min...
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When one solves nonlinear programming problems by means of algorithms that use merit criteria combining the objective function and penalty feasibility terms, a phenomenon called greediness may occur. Unconstrained minimizers attract the iterates at early stages of the calculations and, so, the penalty parameter needs to grow excessively, in such a way that ill-conditioning harms the overall convergence. In this paper a regularization approach is suggested to overcome this difficulty. An Augmented Lagrangian method is defined with the addition of a regularization term that inhibits the possibility that the iterates go far from a reference point. Convergence proofs and numerical examples are given.
Numerical procedures for dynamic system identification are discussed. Efficient algorithms for static least-squares problems provide a starting point for dynamic systems nonlinear programming methods. This paper shows...
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Numerical procedures for dynamic system identification are discussed. Efficient algorithms for static least-squares problems provide a starting point for dynamic systems nonlinear programming methods. This paper shows that in dynamic systems, the additional computation time required for the first and the second gradients over function evaluation is small compared to static systems. This makes gradient procedures very attractive for dynamic system parameter estimation. Additional simplifications are made for linear systems. Finally, some practical simplifications are suggested to enable identification in large scale systems using current computers.
In this paper, we present two parallel multiplicative algorithms for convex programming. If the objective function is differentiable and convex on the positive orthant of R(n), and it has compact level sets and has a ...
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ISBN:
(纸本)9783037850091
In this paper, we present two parallel multiplicative algorithms for convex programming. If the objective function is differentiable and convex on the positive orthant of R(n), and it has compact level sets and has a locally Lipschitz continuous gradient, we prove these algorithms converge to a solution of minimization problem. For the proofs there are essentially used the results of sequential methods shown by Eggertnont([1]).
When solving optimal control problem by nonlinear programming algorithms, the main tasks are the computation of ordinary differential equations and of definite integrals. It is shown how to make a best use of the very...
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In this paper, we discuss an exact augumented Lagrangian functions for the nonlinear programming problem with both equality and inequality constraints, which is the generation of the augmented Lagrangian function in c...
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ISBN:
(纸本)9783037851579
In this paper, we discuss an exact augumented Lagrangian functions for the nonlinear programming problem with both equality and inequality constraints, which is the generation of the augmented Lagrangian function in corresponding reference only for inequality constraints nonlinear programming problem. Under suitable hypotheses, we give the relationship between the local and global unconstrained minimizers of the augumented Lagrangian function and the local and global minimizers of the original constrained problem. From the theoretical point of view, the optimality solution of the nonlinear programming with both equality and inequality constraints and the values of the corresponding Lagrangian multipliers can be found by the well known method of multipliers which resort to the unconstrained minimization of the augumented Lagrangian function presented in this paper.
In the traditional signal control design method, cycle length is calculated by Webster's (1957) approximate optimum cycle length formula and each phase's green time is distributed by the traffic demand rate ra...
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ISBN:
(纸本)9780784483565
In the traditional signal control design method, cycle length is calculated by Webster's (1957) approximate optimum cycle length formula and each phase's green time is distributed by the traffic demand rate ratio. This study develops a nonlinear programming model aimed at minimizing the average delay by calculating the delay using the traditional signal control calculation method. To verify the model, the open source traffic simulation tool SUMO which performs micro-traffic simulations, is used to simulate traffic at a four-leg intersection with signal under several assumed traffic demand scenarios. The results of applying the traditional signal control design method and the method developed in this study are compared. As a result, the method proposed in this study has a longer cycle time but shorter average delay time and shorter coefficient of variation of delay time than the traditional method for scenarios 1-3, which have higher traffic demand than the remaining scenario.
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