Linearization

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## Linearization of a function

## Example

## Linearization of a multivariable function

## Uses of linearization

### Stability analysis

### Microeconomics

### Optimization

### Multiphysics

## See also

## References

## External links

### Linearization tutorials

This article uses material from the Wikipedia page available here. It is released under the Creative Commons Attribution-Share-Alike License 3.0.

Linearization

In mathematics, **linearization** is finding the linear approximation to a function at a given point. The linear approximation of a function is the first order Taylor expansion around the point of interest. In the study of dynamical systems, linearization is a method for assessing the local stability of an equilibrium point of a system of nonlinear differential equations or discrete dynamical systems.^{[1]} This method is used in fields such as engineering, physics, economics, and ecology.

Linearizations of a function are lines--usually lines that can be used for purposes of calculation. Linearization is an effective method for approximating the output of a function at any based on the value and slope of the function at , given that is differentiable on (or ) and that is close to . In short, linearization approximates the output of a function near .

For example, . However, what would be a good approximation of ?

For any given function , can be approximated if it is near a known differentiable point. The most basic requisite is that , where is the linearization of at . The point-slope form of an equation forms an equation of a line, given a point and slope . The general form of this equation is: .

Using the point , becomes . Because differentiable functions are locally linear, the best slope to substitute in would be the slope of the line tangent to at .

While the concept of local linearity applies the most to points arbitrarily close to , those relatively close work relatively well for linear approximations. The slope should be, most accurately, the slope of the tangent line at .

Visually, the accompanying diagram shows the tangent line of at . At , where is any small positive or negative value, is very nearly the value of the tangent line at the point .

The final equation for the linearization of a function at is:

For , . The derivative of is , and the slope of at is .

To find , we can use the fact that . The linearization of at is , because the function defines the slope of the function at . Substituting in , the linearization at 4 is . In this case , so is approximately . The true value is close to 2.00024998, so the linearization approximation has a relative error of less than 1 millionth of a percent.

The equation for the linearization of a function at a point is:

The general equation for the linearization of a multivariable function at a point is:

where is the vector of variables, and is the linearization point of interest
.^{[2]}

Linearization makes it possible to use tools for studying linear systems to analyze the behavior of a nonlinear function near a given point. The linearization of a function is the first order term of its Taylor expansion around the point of interest. For a system defined by the equation

- ,

the linearized system can be written as

where is the point of interest and is the Jacobian of evaluated at .

In stability analysis of autonomous systems, one can use the eigenvalues of the Jacobian matrix evaluated at a hyperbolic equilibrium point to determine the nature of that equilibrium. This is the content of the linearization theorem. For time-varying systems, the linearization requires additional justification.^{[3]}

In microeconomics, decision rules may be approximated under the state-space approach to linearization.^{[4]} Under this approach, the Euler equations of the utility maximization problem are linearized around the stationary steady state.^{[4]} A unique solution to the resulting system of dynamic equations then is found.^{[4]}

In mathematical optimization, cost functions and non-linear components within can be linearized in order to apply a linear solving method such as the Simplex algorithm. The optimized result is reached much more efficiently and is deterministic as a global optimum.

In multiphysics systems--systems involving multiple physical fields that interact with one another--linearization with respect to each of the physical fields may be performed. This linearization of the system with respect to each of the fields results in a linearized monolithic equation system that can be solved using monolithic iterative solution procedures such as the Newton-Raphson method. Examples of this include MRI scanner systems which results in a system of electromagnetic, mechanical and acoustic fields.^{[5]}

- Linear stability
- Tangent stiffness matrix
- Stability derivatives
- Linearization theorem
- Taylor approximation
- Functional equation (L-function)

**^**The linearization problem in complex dimension one dynamical systems at Scholarpedia**^**Linearization. The Johns Hopkins University. Department of Electrical and Computer Engineering Archived 2010-06-07 at the Wayback Machine**^**Leonov, G. A.; Kuznetsov, N. V. (2007). "Time-Varying Linearization and the Perron effects".*International Journal of Bifurcation and Chaos*.**17**(4): 1079-1107. Bibcode:2007IJBC...17.1079L. doi:10.1142/S0218127407017732.- ^
^{a}^{b}^{c}Moffatt, Mike. (2008) About.com*State-Space Approach*Economics Glossary; Terms Beginning with S. Accessed June 19, 2008. **^**Bagwell, S.; Ledger, P. D.; Gil, A. J.; Mallett, M.; Kruip, M. (2017). "A linearised*hp*-finite element framework for acousto-magneto-mechanical coupling in axisymmetric MRI scanners".*International Journal for Numerical Methods in Engineering*.**112**(10): 1323-1352. Bibcode:2017IJNME.112.1323B. doi:10.1002/nme.5559.

This article uses material from the Wikipedia page available here. It is released under the Creative Commons Attribution-Share-Alike License 3.0.

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