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Local linearization method

Local linearization method is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Local linearization method rather than just read about it. In short: In numerical analysis, the local linearization (LL) method is a general strategy for designing numerical integrators for differential equations based on a local (piecewise) linearization of the given equation on consecutive time intervals. The numerical integrators are then iteratively defined as the solution of the resulting piecewise linear equation at the end of each consecutive interval.

Local linearization method — main illustration
Local linearization method — illustration

Key takeaways

  • Local linearization method belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Local linearization method to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Local linearization method from memory before moving on to harder problems.

Reference excerpt

In numerical analysis, the local linearization (LL) method is a general strategy for designing numerical integrators for differential equations based on a local (piecewise) linearization of the given equation on consecutive time intervals. The numerical integrators are then iteratively defined as the solution of the resulting piecewise linear equation at the end of each consecutive interval. The LL method has been developed for a variety of equations such as the ordinary, delayed, random and stochastic differential equations. The LL integrators are key component in the implementation of inference methods for the estimation of unknown parameters and unobserved variables of differential equations given time series of (potentially noisy) observations. The LL schemes are ideals to deal with complex models in a variety of fields as neuroscience, finance, forestry management, control engineering, mathematical statistics, etc.

Background Differential equations have become an important mathematical tool for describing the time evolution of several phenomenon, e.g., rotation of the planets around the sun, the dynamic of assets prices in the market, the fire of neurons, the propagation of epidemics, etc. However, since the exact solutions of these equations are usually unknown, numerical approximations to them obtained by numerical integrators are necessary. Currently, many applications in engineering and applied sciences focused in dynamical studies demand the developing of efficient numerical integrators that preserve, as much as possible, the dynamics of these equations. With this main motivation, the Local Linearization integrators have been developed.

High-order local linearization method High-order local linearization (HOLL) method is a generalization of the Local Linearization method oriented to obtain high-order integrators for differential equations that preserve the stability and dynamics of the linear equations. The integrators are obtained by splitting, on consecutive time intervals, the solution x of the original equation in two parts: the solution z of the locally linearized equation plus a high-order approximation of the residual r = x − z {\displaystyle \mathbf {r} =\mathbf {x} -\mathbf {z} } .

Local linearization scheme A Local Linearization (LL) scheme is the final recursive algorithm that allows the numerical implementation of a discretization derived from the LL or HOLL method for a class of differential equations.

LL methods for ODEs Consider the d-dimensional Ordinary Differential Equation (ODE)

with initial condition x ( t 0 ) = x 0 {\displaystyle \mathbf {x} (t_{0})=\mathbf {x} _{0}} , where f {\displaystyle \mathbf {f} } is a differentiable function. Let ( t ) h = { t n : n = 0 , . . , N } {\displaystyle \left(t\right)_{h}=\{t_{n}:n=0,..,N\}} be a time discretization of the time interval [ t 0 , T ] {\displaystyle [t_{0},T]} with maximum stepsize h such that t n < t n + 1 {\displaystyle t_{n}<t_{n+1}} and h n = t n + 1 − t n ≤ h {\displaystyle h_{n}=t_{n+1}-t_{n}\leq h} . After the local linearization of the equation (4.1) at the time step t n {\displaystyle t_{n}} the variation of constants formula yields

where

results from the linear approximation, and

… excerpt ends here. Continue reading the full article.

Illustrations

Local linearization method: Fig. 2 Approximate paths of the Marchuk et al. (1991) antiviral immune model described by a stiff system of ten-dimensional nonlinear  DDEs with five time delays: top, continuous Runge–Kutta (2,3) scheme; bottom, LL scheme (5.3). Step-size h = 0.01 fixed, and p = q = 6.
Fig. 2 Approximate paths of the Marchuk et al. (1991) antiviral immune model described by a stiff system of ten-dimensional nonlinear DDEs with five time delays: top, continuous Runge–Kutta (2,3) scheme; bottom, LL scheme (5.3). Step-size h = 0.01 fixed, and p = q = 6.
Local linearization method: Fig. 3 Phase portrait of trajectories of the Euler and LL schemes in the integration of the nonlinear RDE (6.2)–(6.3) with step size h = 1/32, and p = q = 6.
Fig. 3 Phase portrait of trajectories of the Euler and LL schemes in the integration of the nonlinear RDE (6.2)–(6.3) with step size h = 1/32, and p = q = 6.
Local linearization method: Fig. 4, Top: Evolution of domains in the phase plane of the harmonic oscillator (7.6), with ε=0 and ω=σ=1. Images of the initial unit circle (green) are obtained at three time moments T by the exact solution (black), and by the schemes SLL1 (blue) and Implicit Euler (red) with h=0.05. Bottom: Expected value of the energy (solid line) along the solution of the nonlinear oscillator (7.6), with ε=1 and ω=100, and its approximation (circles) computed via Monte Carlo with 10000 simulations of the SLL1 scheme with h=1/2 and p=q=6.
Fig. 4, Top: Evolution of domains in the phase plane of the harmonic oscillator (7.6), with ε=0 and ω=σ=1. Images of the initial unit circle (green) are obtained at three time moments T by the exact solution (black), and by the schemes SLL1 (blue) and Implicit Euler (red) with h=0.05. Bottom: Expected value of the energy (solid line) along the solution of the nonlinear oscillator (7.6), with ε=1 and ω=100, and its approximation (circles) computed via Monte Carlo with 10000 simulations of the SLL1 scheme with h=1/2 and p=q=6.
Local linearization method: Fig. 5 Approximate mean of the SDE (8.2) computed via Monte Carlo with 100 simulations of various schemes with h=1/16 and p=q=6.
Fig. 5 Approximate mean of the SDE (8.2) computed via Monte Carlo with 100 simulations of various schemes with h=1/16 and p=q=6.

Worked examples

Example 1 — a first encounter with Local linearization method

Start with the simplest possible case. Write down what Local linearization method claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Local linearization method before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Local linearization method ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Local linearization method

In research
Local linearization method appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Local linearization method in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Local linearization method is common in secondary-school and first-year university syllabi. It links to neighbouring topics Numerical analysis, Numerical integration, so understanding it makes those chapters shorter.
In everyday life
Look for Local linearization method outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Local linearization method in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Local linearization method means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Local linearization method out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Local linearization method in simple terms?

In numerical analysis, the local linearization (LL) method is a general strategy for designing numerical integrators for differential equations based on a local (piecewise) linearization of the given equation on consecutive time intervals. The numerical integrators are then iteratively defined as t…

Why does Local linearization method matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Local linearization method?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Local linearization method.

Tags

  • Numerical analysis
  • Numerical integration

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