Kényszerek alatti egyensúlyi állapotok stabilitásának vizsgálata Tamás Gál Department of Physics, University of Florida, Gainesville, USA.

Slides:



Advertisements
Similar presentations
Equations as Relations y = 2x is an equation with 2 variables When you substitute an ordered pair into an equation with 2 variables then the ordered pair.
Advertisements

Differential Equations
Chapter 2: Second-Order Differential Equations
Physics 430: Lecture 16 Lagrange’s Equations with Constraints
Ch 5.7: Series Solutions Near a Regular Singular Point, Part II
EARS1160 – Numerical Methods notes by G. Houseman
Optimization of thermal processes2007/2008 Optimization of thermal processes Maciej Marek Czestochowa University of Technology Institute of Thermal Machinery.
© 2011 Autodesk Freely licensed for use by educational institutions. Reuse and changes require a note indicating that content has been modified from the.
Engineering Optimization
1 MECH 221 FLUID MECHANICS (Fall 06/07) Tutorial 6 FLUID KINETMATICS.
A reaction-advection-diffusion equation from chaotic chemical mixing Junping Shi 史峻平 Department of Mathematics College of William and Mary Williamsburg,
A Consistent Thermodynamic Treatment for Quark Mass Density-Dependent Model Ru-Keng Su Physics Department Fudan University.
Theoretical Mechanics - PHY6200 Chapter 6 Introduction to the calculus of variations Prof. Claude A Pruneau, Physics and Astronomy Department Wayne State.
Calculus of Variations
Constrained Optimization Rong Jin. Outline  Equality constraints  Inequality constraints  Linear Programming  Quadratic Programming.
DEPARTMENT OF MATHEMATI CS [ YEAR OF ESTABLISHMENT – 1997 ] DEPARTMENT OF MATHEMATICS, CVRCE.
III Solution of pde’s using variational principles
Numerical Solution of Ordinary Differential Equation
Lecture 35 Numerical Analysis. Chapter 7 Ordinary Differential Equations.
Classical Waves Calculus/Differential Equations Refresher.
Differential Equations 6 Copyright © Cengage Learning. All rights reserved. 6.1 Day
Section 2: Finite Element Analysis Theory
Nanostructures Research Group CENTER FOR SOLID STATE ELECTRONICS RESEARCH Time-Dependent Perturbation Theory David K. Ferry and Dragica Vasileska Arizona.
A PPLIED M ECHANICS Lecture 02 Slovak University of Technology Faculty of Material Science and Technology in Trnava.
Math 3120 Differential Equations with Boundary Value Problems Chapter 4: Higher-Order Differential Equations Section 4-9: Solving Systems of Linear Differential.
Prepared by Mrs. Azduwin Binti Khasri
Mathematics. Session Differential Equations - 2 Session Objectives  Method of Solution: Separation of Variables  Differential Equation of first Order.
Chemical Reactions in Ideal Gases. Non-reacting ideal gas mixture Consider a binary mixture of molecules of types A and B. The canonical partition function.
Serge Andrianov Theory of Symplectic Formalism for Spin-Orbit Tracking Institute for Nuclear Physics Forschungszentrum Juelich Saint-Petersburg State University,
EASTERN MEDITERRANEAN UNIVERSITY Department of Industrial Engineering Non linear Optimization Spring Instructor: Prof.Dr.Sahand Daneshvar Submited.
D Nagesh Kumar, IIScOptimization Methods: M2L4 1 Optimization using Calculus Optimization of Functions of Multiple Variables subject to Equality Constraints.
Part 4 Nonlinear Programming 4.1 Introduction. Standard Form.
Mathe III Lecture 7 Mathe III Lecture 7. 2 Second Order Differential Equations The simplest possible equation of this type is:
Dynamic Modeling II Drawn largely from Sage QASS #27, by Huckfeldt, Kohfeld, and Likens. Courtney Brown, Ph.D. Emory University.
Mathe III Lecture 8 Mathe III Lecture 8. 2 Constrained Maximization Lagrange Multipliers At a maximum point of the original problem the derivatives of.
Optimization and Lagrangian. Partial Derivative Concept Consider a demand function dependent of both price and advertising Q = f(P,A) Analyzing a multivariate.
STE 6239 Simulering Wednesday, Week 2: 8. More models and their simulation, Part II: variational calculus, Hamiltonians, Lagrange multipliers.
Chapter 7: Equilibrium and Stability in One-Component Systems
Lecture 39 Numerical Analysis. Chapter 7 Ordinary Differential Equations.
Work and energy for one-dimensional systems For one-dimensional motion work is path independent. 7. One-dimensional systems This follows from the fact.
Mathe III Lecture 8 Mathe III Lecture 8. 2 Constrained Maximization Lagrange Multipliers At a maximum point of the original problem the derivatives of.
Searching a Linear Subspace Lecture VI. Deriving Subspaces There are several ways to derive the nullspace matrix (or kernel matrix). ◦ The methodology.
Ch. 2: Variational Principles & Lagrange’s Eqtns Sect. 2.1: Hamilton’s Principle Our derivation of Lagrange’s Eqtns from D’Alembert’s Principle: Used.
D Nagesh Kumar, IISc Water Resources Systems Planning and Management: M2L2 Introduction to Optimization (ii) Constrained and Unconstrained Optimization.
Ch 9.6: Liapunov’s Second Method In Section 9.3 we showed how the stability of a critical point of an almost linear system can usually be determined from.
1 Variational and Weighted Residual Methods. 2 Introduction The Finite Element method can be used to solve various problems, including: Steady-state field.
Another sufficient condition of local minima/maxima
Hamiltonian principle and Lagrange’s equations (review with new notations) If coordinates q are independent, then the above equation should be true for.
EEE 431 Computational Methods in Electrodynamics
Chapter 14 Partial Derivatives.
DIFFERENTIAL EQUATIONS
Chapter 4 Fluid Mechanics Frank White
Part 4 Nonlinear Programming
Mathematical Modeling of Control Systems
Quantum Two.
Modern Control Systems (MCS)
Victor Edneral Moscow State University Russia
The Rayleigh-Plateau Instability
The Lagrange Multiplier Method
Continuous Systems and Fields
7.5 – Constrained Optimization: The Method of Lagrange Multipliers
Part 4 Nonlinear Programming
Numerical Analysis Lecture 37.
Basic Concepts, Necessary and Sufficient conditions
Numerical Analysis Lecture 38.
Signals and Systems Lecture 3
Numerical Analysis Lecture 36.
Multivariable optimization with no constraints
L8 Optimal Design concepts pt D
Presentation transcript:

Kényszerek alatti egyensúlyi állapotok stabilitásának vizsgálata Tamás Gál Department of Physics, University of Florida, Gainesville, USA

At a local minimum/maximum of a functional A[ρ], In the presence of some constraint C[ρ]=C, the above Euler equation modifies according to the method of Lagrange multipliers, Question 1: how to account for constraints apart from a local extremum ? Question 2: how to account for constraints in a stationary point analysis, based on second derivatives ? Solution: introduction of the concept of constrained functional derivatives [T. Gál, Phys. Rev. A 63, (2001); J. Phys. A 35, 5899 (2002); J. Math. Chem. 42, 661 (2007); J. Phys. A 43, (2010)

● if Idea: Under constraints, the form of a functional derivative modifies. This gives a generalization of the method of Lagrange multipliers:

Under constraints, the Taylor expansion of a functional A[ρ] becomes In the case A[ρ] has a local extremum under a constraint, while the second-order (necessary) condition for a local minimum/maximum will become

The constrained derivative formula emerges from two essential conditions: (i) The derivatives of two functionals that are equal over a given constrained domain of the functional variables should have equal derivatives over that domain: (ii) If a functional is independent of N, an N-conservation constraint does not affect the differentiation of the functional:

From condition (i), where u(x) is an arbitrary function that integrates to 1. Condition (ii) then fixes u(x) as ● This follows from the fact that for a functional for which A[λρ]=A[ρ] for any λ,

How to obtain, in practice, the constrained derivatives corresponding to a given constraint(s) ? Find a functional ρ C [ ρ] that (i) satisfies the given constraint for any ρ(x), (ii) gives an identity for any ρ ( x) that satisfies the constraint That is, and With the use of this, then, the constrained first & second derivatives: &

Why is this the proper way to obtain the constrained derivatives ? Expand ρ C [ ρ] into its Taylor series: Then, substitute this into the Taylor series expansion of A[ρ] above the constrained domain, This will give

Applications ● in the dynamical description of ultra-thin polymer binary mixtures, by Clarke [Macromolecules 38, 6775 (2005); also Thomas et al., Soft Matter 6, 3517 (2010)] – two variables describing the motion of the fluid, under the constraints of volume and material conservation: and

● in the stability analysis of droplet growth in supercooled vapors, by Uline & Corti [J. Chem. Phys. 129, (2008); also Uline et al., J. Chem. Phys. 133, (2010)] – they used fluid-dynamical DFT, with a simple particle-number conservation constraint – to determine whether the given equilibrium is stable (i.e., there is a local minimum of the free-energy functional), they applied the eigenvalues λ of which should all be positive or zero in the case of a stable stationary point of F[ρ]