1. Quantifying Variational Solutions † University of Central Florida Institute for Simulation & Training Department of Mathematics and D.J. Kaup and Thomas K. Vogel † Research supported in part by NSF and AFOSR and CREOL (Preprint available at http://gauss.math.ucf.edu/~kaup/)

2. OUTLINE History of Variation Methods Uses and Variational Approach Derivation of Variational Corrections Linear Example Nonlinear Example Summary

3. History of Variational Methods Early Greeks – Max. area/perimeter Hero of Alexandria – Equal angles of incidence /reflection Fermat - least time principle (Early 17 th Century) Newton and Leibniz – Calculus (Mid 17th Century) Johann Bernoulli - brachistochrone problem (1696) Euler - calculus of variations (1744) Joseph-Louis Lagrange – Euler-Lagrange Equations (??) William Hamilton – Hamilton's Principle (1835) Raleigh-Ritz method – VA for linear eigenvalue problems (late 19 th Century) Quantum Mechanics - Computational methods – (early 20 th Century) Morse & Feshbach – technology of variational methods (1953) Solid State Physics, Chemistry, Engineering – (mid-late 20 th Century) Personal computers – new computational power (1980’s) Technology of variational methods essentially lost (1980-2000) D. Anderson – VA for perturbations of solitons (1979) Malomed, Kaup – VA for solitary wave solutions (1994 – present)

4. Why Use Variational Methods? Linear problems are very well understood. Nonlinear problems are very different. Nonlinear waves have solitary wave (soliton) solutions. They exist in a limited parameter space. Where should one look? Amplitude=?, width=?, phase=?, etc. Equation coefficients for solitons=? These Q’s mostly irrelevant for linear systems. VA for nonlinear system is same as for linear system. Simple ansatzes point to regions where solitons are. Basic functional relations found from ansatzes. No need to search entire parameter space. Each parameter in ansatz reduces parameter space. Cascading knowledge.

5. Variational Approximations Is based on a Minimization Principle Solution = path that extremizes an “Action” Action = time-integral over a Lagrangian Lagrangian is specified by the system By freezing out specific modes, one can obtain reduced systems The method will still find the path which is closest to the actual solution Definite need for quantitative measure

6. Variational Corrections Definition of Action is: Definition of variational derivative is: Euler – Lagrange Equations are: Now consider Variational Perturbations about an ansatz: Ansatz Variational Parameters Corrections  = ?

7. Expansion Zeroth order is the VA: Calculate Action and Expand: Next order is (vary u 1 ): 0  is determined by E-L Eq. R is thereby defined

8. Equation for Correction Perturbed Euler-Lagrange Equation with Source SUMMARY: Drop Ansatz into Action Calculate new E-L equations to determine q’s Drop Ansatz plus correction into the full E-L equations Solve for u 1 Determine quantitative accuracy

9. Vibrating String Eigenmodes Examples -- two different Ansatzes: Only need fundamental mode Will normalize intensity to unity

10. Variational Eigenvalues Variation of and u results in Euler- Lagrange equation. ``Action” for eigenvalue problems is eigenvalue itself. For our models:

11. Ansatzes and Corrections where:

12. Quantitative Estimates Eigenvalues and corrections: RMS measure: which gives:

13. KdV Example Look for soliton solution and integrate once: Then the action is: Take the Lagrangian and Ansatz to be: With the variational solution:

14. KdV correction The correction equation can be scaled: In which case, it reduces to: where

15. Ansatz and Correction E rms = 0.038

16. Soliton separation Want soliton separation such that tails = 0.001 Ansatz = 1.56; plus correction = 2.1 Ratio = 2.1 / 1.56 = 1.35; whence 35% error

17. Quantitative Variational Can calculate variational corrections Can quantify variational approximations Do not need exact solutions Only need to solve linear equation Quantitative estimate depends on what use is Most VA’s will be poor/excellent depending on use