Lyapunov–Schmidt reduction explained

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

Let

f(x,λ)=0

be the given nonlinear equation,

X,Λ,

and

Y

areBanach spaces (

Λ

is the parameter space).

f(x,λ)

is the

Cp

-map from a neighborhood of some point

(x0,λ0)\inX x Λ

to

Y

and the equation is satisfied at this point

f(x0,λ0)=0.

For the case when the linear operator

fx(x,λ)

is invertible, the implicit function theorem assures that there existsa solution

x(λ)

satisfying the equation

f(x(λ),λ)=0

at least locally close to

λ0

.

In the opposite case, when the linear operator

fx(x,λ)

is non-invertible, the Lyapunov–Schmidt reduction can be applied in the followingway.

Assumptions

One assumes that the operator

fx(x,λ)

is a Fredholm operator.

\kerfx(x0,λ0)=X1

and

X1

has finite dimension.

The range of this operator

ranfx(x0,λ0)=Y1

has finite co-dimension andis a closed subspace in

Y

.

Without loss of generality, one can assume that

(x0,λ0)=(0,0).

Lyapunov–Schmidt construction

Let us split

Y

into the direct product

Y=Y1Y2

, where

\dimY2<infty

.

Let

Q

be the projection operator onto

Y1

.

Consider also the direct product

X=X1X2

.

Applying the operators

Q

and

I-Q

to the original equation, one obtains the equivalent system

Qf(x,λ)=0

(I-Q)f(x,λ)=0

Let

x1\inX1

and

x2\inX2

, then the first equation

Qf(x1+x2,λ)=0

can be solved with respect to

x2

by applying the implicit function theorem to the operator

Qf(x1+x2,λ):X2 x (X1 x Λ)\toY1

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

Thus, there exists a unique solution

x2(x1,λ)

satisfying

Qf(x1+x2(x1,λ),λ)=0.

Now substituting

x2(x1,λ)

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

(I-Q)f(x1+x2(x1,λ),λ)=0.

Indeed, the last equation is now finite-dimensional, since the range of

(I-Q)

is finite-dimensional. This equation is now to be solved with respect to

x1

, which is finite-dimensional, and parameters :

λ

Applications

Lyapunov–Schmidt reduction has been used in economics, natural sciences, and engineering[1] often in combination with bifurcation theory, perturbation theory, and regularization.[2] 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.[2] [3] [4]

References

Bibliography

Notes and References

  1. Book: Sidorov, Nikolai. Lyapunov-Schmidt methods in nonlinear analysis and applications. 2011. Springer. 9789048161508. 751509629.
  2. 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. 10.1016/j.cej.2008.08.025. 1385-8947.
  3. Balakotaiah. Vemuri. March 2004. Hyperbolic averaged models for describing dispersion effects in chromatographs and reactors. Korean Journal of Chemical Engineering. 21. 2. 318–328. 10.1007/bf02705415. 0256-1115.
  4. 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. 10.2202/1934-2659.1135. 1934-2659.