As the water depth of offshore oil fields increases, a position keeping control system on a platform equipped with thrusters has become essential. In previous control studies for position keeping, the control algorith...
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ISBN:
(纸本)1880653311
As the water depth of offshore oil fields increases, a position keeping control system on a platform equipped with thrusters has become essential. In previous control studies for position keeping, the control algorithm was restricted by the type of the platform, the number and the type of its actuators, and nonlinear characteristics of its system. The authors developed a new control algorithm to improve position keeping of a platform, and tested it by numerical simulations and tank experiments. They were the first experiments in the world conducted by reat time control of nonlinear programming.
A nonlinear programming formulation is introduced to solve infinite-horizon dynamic programming problems. This extends the linear approach to dynamic programming by using ideas from approximation theory to approximate...
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A nonlinear programming formulation is introduced to solve infinite-horizon dynamic programming problems. This extends the linear approach to dynamic programming by using ideas from approximation theory to approximate value functions. Our numerical results show that this nonlinear programming is efficient and accurate, and avoids inefficient discretization.
We analyze the sample complexity of single-loop quadratic penalty and augmented Lagrangian algorithms for solving nonconvex optimization problems with functional equality constraints. We consider three cases, in all o...
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We analyze the sample complexity of single-loop quadratic penalty and augmented Lagrangian algorithms for solving nonconvex optimization problems with functional equality constraints. We consider three cases, in all of which the objective is stochastic, that is, an expectation over an unknown distribution that is accessed by sampling. The nature of the equality constraints differs among the three cases: deterministic and linear in the first case, deterministic and nonlinear in the second case, and stochastic and nonlinear in the third case. Variance reduction techniques are used to improve the complexity. To find a point that satisfies e-approximate first-order conditions, we require (O) over tilde (epsilon(-3)) complexity in the first case, (O) over tilde (epsilon(-4)) in the second case, and (O) over tilde(epsilon(-5)) in the third case. For the first and third cases, they are the first algorithms of "single loop" type that also use O(1) samples at each iteration and still achieve the best-known complexity guarantees.
This paper, a new class of augmented Lagrange functions with the new NCP function is proposed for the minimization of a smooth function subject to inequality constraints. Under some conditions, we prove of the equival...
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ISBN:
(纸本)9783038352679
This paper, a new class of augmented Lagrange functions with the new NCP function is proposed for the minimization of a smooth function subject to inequality constraints. Under some conditions, we prove of the equivalences of the KKT point and local point and globe point between primal constrained nonlinear programming problem and the new unconstrained problem. By the character of augmented Lagrange function, the algorithm which uses alternating direction method is constructed and proved convergence.
An algorithm for solving nonlinear optimization problems is described along with its implementation as a Fortran program. Computational results are provided which compare its performance with that of other algorithms....
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The increase in efficiency of container terminals is addressed via an approach based on the optimisation of logistics operations. Toward this end, a discrete-time dynamic model of the various flows of containers that ...
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The increase in efficiency of container terminals is addressed via an approach based on the optimisation of logistics operations. Toward this end, a discrete-time dynamic model of the various flows of containers that are inter-modally routed from arriving carriers to carriers ready for departure is proposed. On the basis of such a model, the decisions on the allocation of the available handling resources inside a container terminal are made according to the predictive-control approach by minimising a performance cost function over a forward horizon from the current time instant. Since both the dynamic equations and the cost function are in general nonlinear and since binary variables are used to model the departure or stay of a carrier, such decisions result from the on-line solution of a mixed-integer nonlinear programming problem at each time step. To solve this problem, two techniques are proposed that have to deal explicitly with the binary variables and with the nonlinearities of the model and the cost function. The first relies on the application of a standard branch-and-bound algorithm. The second is based on the idea of treating the decisions associated with the binary variables as step functions. Simulation results are reported to illustrate the pros and cons of such methodologies in a case study.
This paper presents a neurogenetic approach for solving nonlinear programming problems. Genetic algorithm must its popularity to make possible cover nonlinear and extensive search spaces. Neural networks with feedback...
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ISBN:
(纸本)9783642106767
This paper presents a neurogenetic approach for solving nonlinear programming problems. Genetic algorithm must its popularity to make possible cover nonlinear and extensive search spaces. Neural networks with feedback connections provide a computing model capable of solving a large class of optimization problems. The association of a modified Hopfield network with genetic algorithm guarantees the convergence of the system to the equilibrium points, which represent;feasible solutions for nonlinear programming problems.
In B2B markets, value-based pricing and selling has become an important alternative to discounting. This study outlines a modeling method that uses customer data (product offers made to each current or potential custo...
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In B2B markets, value-based pricing and selling has become an important alternative to discounting. This study outlines a modeling method that uses customer data (product offers made to each current or potential customer, features, discounts, and customer purchase decisions) to estimate a mixed logit choice model. The model is estimated via hierarchical Bayes and machine learning, delivering customer-level parameter estimates. Customer-level estimates are input into a nonlinear programming next-offer maximization problem to select optimal features and discount level for customer segments, where segments are based on loyalty and discount elasticity. The mixed logit model is integrated with economic theory (the random utility model), and it predicts both customer perceived value for and response to alternative future sales offers. The methodology can be implemented to support value-based pricing and selling efforts. Contributions to the literature include: (a) the use of customer-level parameter estimates from a mixed logit model, delivered via a hierarchical Bayes estimation procedure, to support value-based pricing decisions;(b) validation that mixed logit customer-level modeling can deliver strong predictive accuracy, not as high as random forest but comparing favorably;and (c) a nonlinear programming problem that uses customer-level mixed logit estimates to select optimal features and discounts.
Mathematical programming approaches, such as Lagrangian relaxation, have the advantage of computational efficiency when the optimization problems are decomposable. Lagrangian relaxation belongs to a class of primal-du...
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ISBN:
(纸本)9781467330374;9781467330367
Mathematical programming approaches, such as Lagrangian relaxation, have the advantage of computational efficiency when the optimization problems are decomposable. Lagrangian relaxation belongs to a class of primal-dual algorithms. Subgradient-based optimization methods can be used to optimize the dual functions in Lagrangian relaxation. In this paper, three subgradient-based methods, the subgradient (SG), the surrogate subgradient (SSG) and the surrogate modified subgradient (SMSG), are adopted to solve a demonstrative nonlinear programming problem to assess the performances on optimality in order to demonstrate its applicability to the realistic problem.
The computation of the maximum loading point (MLP) is crucial to power systems operation and control, There are several methodologies proposed to compute it. The continuation load flow (CLF) is very robust, widely kno...
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ISBN:
(纸本)9781424421893
The computation of the maximum loading point (MLP) is crucial to power systems operation and control, There are several methodologies proposed to compute it. The continuation load flow (CLF) is very robust, widely known to draw PV curves, and can be also used for computing the MLP. However it has some drawbacks, the procedure may diverge for some cases and it is very conservative for some networks, taking many iterations. Other efficient technique using nonlinear programming (NLP) has been proposed recently. This method presents clear advantages due to the orientation of the process in direction to the MLP. This paper presents a method based on the CLF and NLP. The idea is to compute the MLP taking some features of the CLF combined with characteristics of NLP. With this combination the MLP can be evaluated with more accuracy and efficiency. Simulations for different systems including IEEE test systems are shown to evaluate the performance of the method. Some comparisons of the methodologies are also shown.
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