scholarly journals Vapour-liquid phase transition and metastability

2019 ◽  
Vol 66 ◽  
pp. 22-41
Author(s):  
Hala Ghazi ◽  
Francois James ◽  
Hélène Mathis
Keyword(s):  
Dynamical System ◽  
Optimization Problem ◽  
Type System ◽  
Metastable States ◽  
Relaxation Processes ◽  
Constrained Optimization Problem ◽  
Liquid Phase Transition ◽  
Van Der Waals Equation ◽  
Gibbs Formalism ◽  
Pure Phases

The paper deals with the modelling of the relaxation processes towards thermodynamic equilibrium in a liquid-vapour isothermal mixture. Focusing on the van der Waals equation of state, we construct a constrained optimization problem using Gibbs' formalism and characterize all possible equilibria: coexistence states, pure phases and metastable states. Coupling with time evolution, we develop a dynamical system whose equilibria coincide with the minimizers of the optimization problem. Eventually we consider the coupling with hydrodynamics and use the dynamical system as a relaxation source terms in an Euler-type system. Numerical results illustrate the ability of the whole model to depict coexistence and metastable states as well.

scholarly journals Application of Lagrange Multiplier Method for Computing Fold Bifurcation Point in A Two-Prey One Predator Dynamical System

2018 ◽  
Vol 24 (2) ◽  
pp. 7-19
Author(s):  
Marwan Marwan ◽  
Johan Matheus Tuwankotta ◽  
Eric Harjanto
Keyword(s):  
Dynamical System ◽  
Lagrange Multiplier ◽  
Bifurcation Point ◽  
Optimization Problem ◽  
The Other ◽  
Lagrange Multiplier Method ◽  
Multiplier Method ◽  
Constrained Optimization Problem ◽  
Fold Bifurcation ◽  
The Cost

We propose by means of an example of applications of the classical Lagrange Multiplier Method for computing fold bifurcation point of an equilibrium ina one-parameter family of dynamical systems. We have used the fact that an equilibrium of a system, geometrically can be seen as an intersection between nullcline manifolds of the system. Thus, we can view the problem of two collapsing equilibria as a constrained optimization problem, where one of the nullclines acts as the cost function while the other nullclines act as the constraints.

scholarly journals Nonconvex constrained optimization by a filtering branch and bound

2020 ◽  
Author(s):  
Gabriele Eichfelder ◽  
Kathrin Klamroth ◽  
Julia Niebling
Keyword(s):  
Constrained Optimization ◽  
Optimization Problem ◽  
Feasible Solution ◽  
Constrained Optimization Problem ◽  
Numerical Tests ◽  
Nonconvex Constrained Optimization ◽  
Objective Value ◽  
Feasible Solutions ◽  
Quality Guarantee ◽  
Single Objective

AbstractA major difficulty in optimization with nonconvex constraints is to find feasible solutions. As simple examples show, the $$\alpha $$ α BB-algorithm for single-objective optimization may fail to compute feasible solutions even though this algorithm is a popular method in global optimization. In this work, we introduce a filtering approach motivated by a multiobjective reformulation of the constrained optimization problem. Moreover, the multiobjective reformulation enables to identify the trade-off between constraint satisfaction and objective value which is also reflected in the quality guarantee. Numerical tests validate that we indeed can find feasible and often optimal solutions where the classical single-objective $$\alpha $$ α BB method fails, i.e., it terminates without ever finding a feasible solution.

scholarly journals Optimal P-Q Control of Grid-Connected Inverters in a Microgrid Based on Adaptive Population Extremal Optimization

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2107 ◽  
Author(s):  
Min-Rong Chen ◽  
Huan Wang ◽  
Guo-Qiang Zeng ◽  
Yu-Xing Dai ◽  
Da-Qiang Bi
Keyword(s):  
Constrained Optimization ◽  
Optimization Problem ◽  
Reactive Power ◽  
Active Power ◽  
Adaptive Genetic Algorithm ◽  
Constrained Optimization Problem ◽  
Extremal Optimization ◽  
Active And Reactive Power ◽  
Three Phase ◽  
Grid Connected Inverter

The optimal P-Q control issue of the active and reactive power for a microgrid in the grid-connected mode has attracted increasing interests recently. In this paper, an optimal active and reactive power control is developed for a three-phase grid-connected inverter in a microgrid by using an adaptive population-based extremal optimization algorithm (APEO). Firstly, the optimal P-Q control issue of grid-connected inverters in a microgrid is formulated as a constrained optimization problem, where six parameters of three decoupled PI controllers are real-coded as the decision variables, and the integral time absolute error (ITAE) between the output and referenced active power and the ITAE between the output and referenced reactive power are weighted as the objective function. Then, an effective and efficient APEO algorithm with an adaptive mutation operation is proposed for solving this constrained optimization problem. The simulation and experiments for a 3kW three-phase grid-connected inverter under both nominal and variable reference active power values have shown that the proposed APEO-based P-Q control method outperforms the traditional Z-N empirical method, the adaptive genetic algorithm-based, and particle swarm optimization-based P-Q control methods.

scholarly journals Optimization based Layer-wise Magnitude-based Pruning for DNN Compression

2018 ◽  
Author(s):  
Guiying Li ◽  
Chao Qian ◽  
Chunhui Jiang ◽  
Xiaofen Lu ◽  
Ke Tang
Keyword(s):  
Deep Neural Network ◽  
Optimization Problem ◽  
State Of The Art ◽  
Automatic Tuning ◽  
Constrained Optimization Problem ◽  
Derivative Free Optimization ◽  
Derivative Free ◽  
Threshold Tuning ◽  
Model Subject ◽  
Pruning Methods

Layer-wise magnitude-based pruning (LMP) is a very popular method for deep neural network (DNN) compression. However, tuning the layer-specific thresholds is a difficult task, since the space of threshold candidates is exponentially large and the evaluation is very expensive. Previous methods are mainly by hand and require expertise. In this paper, we propose an automatic tuning approach based on optimization, named OLMP. The idea is to transform the threshold tuning problem into a constrained optimization problem (i.e., minimizing the size of the pruned model subject to a constraint on the accuracy loss), and then use powerful derivative-free optimization algorithms to solve it. To compress a trained DNN, OLMP is conducted within a new iterative pruning and adjusting pipeline. Empirical results show that OLMP can achieve the best pruning ratio on LeNet-style models (i.e., 114 times for LeNet-300-100 and 298 times for LeNet-5) compared with some state-of-the- art DNN pruning methods, and can reduce the size of an AlexNet-style network up to 82 times without accuracy loss.

Robust Layout Synthesis of a MEM Crab-Leg Resonator Using a Constrained Genetic Algorithm

2007 ◽  
Author(s):  
Zhun Fan ◽  
Sofiane Achiche
Keyword(s):  
Genetic Algorithm ◽  
Robust Design ◽  
Optimization Problem ◽  
Design Method ◽  
Research Work ◽  
Constrained Optimization Problem ◽  
Geometric Uncertainties ◽  
Layout Synthesis ◽  
Target Performance ◽  
Robust Design Method

The research work carried out in this paper introduces a robust design method for layout synthesis of MEM resonator subject to inherent geometric uncertainties such as the fabrication error on the sidewall of the structure. The robust design problem is formulated as a multi-objective constrained optimization problem with certain assumptions and treated by a special constrained genetic algorithm. The MEM design used for validation is a crab-leg resonator taken from the literature. The results show that the approach proposed in this research can lead to design results that meet the target performance and are less sensitive to geometric uncertainties than typical designs.

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