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Lyapunov–Schmidt reduction

From Wikipedia, the free encyclopedia

In mathematics, the Lyapunov–Schmidt reduction or Lyapunov–Schmidt construction is used to study solutions to nonlinear equations in the case when the implicit function theorem does not work. It permits the reduction of infinite-dimensional equations in Banach spaces to finite-dimensional equations. It is named after Aleksandr Lyapunov and Erhard Schmidt.

Problem setup

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Let

be the given nonlinear equation, and are Banach spaces ( is the parameter space). is the -map from a neighborhood of some point to and the equation is satisfied at this point

For the case when the linear operator is invertible, the implicit function theorem assures that there exists a solution satisfying the equation at least locally close to .

In the opposite case, when the linear operator is non-invertible, the Lyapunov–Schmidt reduction can be applied in the following way.

Assumptions

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One assumes that the operator is a Fredholm operator.

and has finite dimension.

The range of this operator has finite co-dimension and is a closed subspace in .

Without loss of generality, one can assume that

Lyapunov–Schmidt construction

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Let us split into the direct product , where .

Let be the projection operator onto .

Consider also the direct product .

Applying the operators and to the original equation, one obtains the equivalent system

Let and , then the first equation

can be solved with respect to by applying the implicit function theorem to the operator

(now the conditions of the implicit function theorem are fulfilled).

Thus, there exists a unique solution satisfying

Now substituting into the second equation, one obtains the final finite-dimensional equation

Indeed, the last equation is now finite-dimensional, since the range of is finite-dimensional. This equation is now to be solved with respect to , which is finite-dimensional, and parameters :

Applications

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Lyapunov–Schmidt reduction has been used in economics, natural sciences, and engineering[1] often in combination with bifurcation theory, perturbation theory, and regularization.[1][2][3] LS reduction is often used to rigorously regularize partial differential equation models in chemical engineering resulting in models that are easier to simulate numerically but still retain all the parameters of the original model.[3][4][5]

References

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  1. ^ a b Sidorov, Nikolai (2011). Lyapunov-Schmidt methods in nonlinear analysis and applications. Springer. ISBN 9789048161508. OCLC 751509629.
  2. ^ Golubitsky, Martin; Schaeffer, David G. (1985), "The Hopf Bifurcation", Applied Mathematical Sciences, Springer New York, pp. 337–396, doi:10.1007/978-1-4612-5034-0_8, ISBN 9781461295334
  3. ^ a b Gupta, Ankur; Chakraborty, Saikat (January 2009). "Linear stability analysis of high- and low-dimensional models for describing mixing-limited pattern formation in homogeneous autocatalytic reactors". Chemical Engineering Journal. 145 (3): 399–411. doi:10.1016/j.cej.2008.08.025. ISSN 1385-8947.
  4. ^ Balakotaiah, Vemuri (March 2004). "Hyperbolic averaged models for describing dispersion effects in chromatographs and reactors". Korean Journal of Chemical Engineering. 21 (2): 318–328. doi:10.1007/bf02705415. ISSN 0256-1115.
  5. ^ Gupta, Ankur; Chakraborty, Saikat (2008-01-19). "Dynamic Simulation of Mixing-Limited Pattern Formation in Homogeneous Autocatalytic Reactions". Chemical Product and Process Modeling. 3 (2). doi:10.2202/1934-2659.1135. ISSN 1934-2659.

Bibliography

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  • Louis Nirenberg, Topics in nonlinear functional analysis, New York Univ. Lecture Notes, 1974.
  • Aleksandr Lyapunov, Sur les figures d’équilibre peu différents des ellipsoides d’une masse liquide homogène douée d’un mouvement de rotation, Zap. Akad. Nauk St. Petersburg (1906), 1–225.
  • Aleksandr Lyapunov, Problème général de la stabilité du mouvement, Ann. Fac. Sci. Toulouse 2 (1907), 203–474.
  • Erhard Schmidt, Zur Theory der linearen und nichtlinearen Integralgleichungen, 3 Teil, Math. Annalen 65 (1908), 370–399.