Combined the advantage of Hopfield neural network and filled function method, a dynamic filled algorithm will be presented for constrainted global optimization of nonlinear programming. The algorithm contains two phas...
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ISBN:
(纸本)9781424481040
Combined the advantage of Hopfield neural network and filled function method, a dynamic filled algorithm will be presented for constrainted global optimization of nonlinear programming. The algorithm contains two phases. The dynamic minimizing phase in which the dynamic minimizing system is used to find the local minimizer of the global optimization. And in the dynamic filled phase, a new initial condition in a lower basin can be determined by the dynamic filled system. By repeating two dynamic systems of the algorithm, a global minimal point can be obtained at last. The algorithm not only makes the computation simple, rapid, and criterion, but also prevents the Hopfield neural network from getting trapped in the local minima.
In this paper, we propose a primal-dual interior point method for solving general constrained nonlinear programming problems. To avoid the situation that the algorithm we use may converge to a saddle point or a local ...
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In this paper, we propose a primal-dual interior point method for solving general constrained nonlinear programming problems. To avoid the situation that the algorithm we use may converge to a saddle point or a local maximum, we utilize a merit function to guide the iterates toward a local minimum. Especially, we add the parameter ε to the Newton system when calculating the decrease directions. The global convergence is achieved by the decrease of a merit function. Furthermore, the numerical results confirm that the algorithm can solve this kind of problems in an efficient way.
We consider solving high-order semidefinite programming (SDP) relaxations of nonconvex polynomial optimization problems (POPs) that often admit degenerate rank-one optimal solutions. Instead of solving the SDP alone, ...
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This paper presents a mathematical model for a photovoltaic hydrogen production system, along with an optimal design study applied to this system. Precise modeling of hydrogen production has proven to be a challenging...
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This paper presents a mathematical model for a photovoltaic hydrogen production system, along with an optimal design study applied to this system. Precise modeling of hydrogen production has proven to be a challenging task. Based on previous work, a general framework is proposed for battery pack-assisted photovoltaic hydrogen production. Specifically, explicit mathematical expressions are given for the hydrogen production efficiency of the electrolyzer, and a current source model is established for the photovoltaic panels. Regarding the optimal design, a systematic framework is outlined to obtain the optimal strategy by converting the control problem into a boundary value problem, subsequently solved via nonlinear programming. Simulation results show that under the optimal strategy, the battery energy consumption by the photovoltaic hydrogen production system is significantly reduced, while maintaining minimal power consumption operation when the irradiation is insufficient, without the need for frequent start-stop cycles. In addition, the calculation time of the IPOPT solver to find the optimal strategy is discussed, proving its ability to address nonlinear planning problems at a millisecond-level calculation time.
We argue that reducing nonlinear programming problems to a simple canonical form is an effective way to analyze them, specially when the problem is degenerate and the usual linear independence hypothesis does not hold...
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In this paper, we present a two phase method for solving nonlinear programming problems called nonlinear Polyhedral Active Set Algorithm (NPASA) that has global and local convergence guarantees under reasonable assump...
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This paper presents a new and efficient method, referred to as an aggregate constraint method, for the solution of general non-linear programming problems in which a multi-constrained problem is converted to one with ...
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This paper presents a new and efficient method, referred to as an aggregate constraint method, for the solution of general non-linear programming problems in which a multi-constrained problem is converted to one with a single parametric constraint. This is derived by using the two concepts of constraint surrogation and maximum entropy, and represents a uniform approximation to the original constraint set. An augmented Lagrangean algorithm is then used to solve the resulting problem. Numerical examples are given to show the high efficiency of the present method.
Localization is used in location-aware applications such as navigation, autonomous robotic movement, and asset tracking to position a moving object on a coordinate system. In this paper, a nonlinear programming algori...
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This study proposes an analytical Delta-V approximation of short-timetransfers based on the linear relative motion and a gradient-based nonlinear programming model of multi-target rendezvous and fly by trajectories. I...
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LU and Cholesky matrix factorization algorithms are core subroutines used to solve systems of linear equations (SLEs) encountered while solving an optimization problem. Standard factorization algorithms are highly eff...
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