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Related Concept Videos

Linearization and Approximation01:26

Linearization and Approximation

Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Linear Approximations01:23

Linear Approximations

For a differentiable function of two variables, linear approximation estimates values near a known point by replacing the curved surface with its tangent plane. Consider the function\begin{equation*}f(x,y)=x^2+3y^2\end{equation*}near the point (2, 1). The exact value at this point is f(2, 1) = 22 + 3(1)2 = 4 + 3 = 7.The linear approximation of f(x, y)) near (a, b) is\begin{equation*}L(x,y)=f(a,b)+f_x(a,b)(x-a)+f_y(a,b)(y-b)\end{equation*}First, compute the partial derivatives: fx(x, y) = 2x and...
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

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Related Experiment Video

Updated: Jun 12, 2026

Simulating the Mechanics of Lens Accommodation via a Manual Lens Stretcher
05:14

Simulating the Mechanics of Lens Accommodation via a Manual Lens Stretcher

Published on: February 23, 2018

Linearization of nonlinear automatic lens design problems.

K Tanaka

    Applied Optics
    |June 26, 2010
    PubMed
    Summary
    This summary is machine-generated.

    A new method simplifies nonlinear automatic lens design problems by using linearization. This approach is validated with two distinct computational solvers for optical engineering.

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    Area of Science:

    • Optical engineering
    • Computational optics
    • Mathematical modeling

    Background:

    • Automatic lens design involves complex, nonlinear optimization problems.
    • Traditional methods can be computationally intensive and may struggle with local optima.

    Purpose of the Study:

    • To present a straightforward method for formally linearizing nonlinear automatic lens design problems.
    • To demonstrate the applicability of the linearization technique using two different solvers.

    Main Methods:

    • Formal linearization of the nonlinear lens design equations.
    • Implementation and testing of the linearized model with two distinct numerical solvers.

    Main Results:

    • The proposed linearization method successfully transforms the nonlinear problem into a tractable form.
    • Validation of the method's effectiveness across two different solver implementations.

    Conclusions:

    • Linearization offers a simplified and potentially more efficient approach to automatic lens design.
    • The presented method provides a robust framework for solving complex optical design challenges.