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Approximations and Errors using Derivatives: Definition and Examples

Approximations and Errors using Derivatives: Definition and Examples

Edited By Komal Miglani | Updated on Jul 02, 2025 07:50 PM IST

Approximation is an important concept in calculus. It is used to estimate the values of functions at a given point. An approximate model is used to make the calculations easier. The approximations can also be used if inadequate information prevents the use of exact demonstrations. These concepts of approximation have been broadly applied in branches of mathematics, physics, engineering, economics, and biology.

This Story also Contains
  1. Approximation
  2. Approximations and Errors Using Derivatives
  3. ERROR
Approximations and Errors using Derivatives: Definition and Examples
Approximations and Errors using Derivatives: Definition and Examples

In this article, we will cover the concept of the approximation. This topic falls under the broader category of Calculus, which is a crucial chapter in Class 11 Mathematics. This is very important not only for board exams but also for competitive exams, which even include the Joint Entrance Examination Main and other entrance exams: SRM Joint Engineering Entrance, BITSAT, WBJEE, and BCECE. A total of one question has been asked on this topic in JEE Main from 2013 to 2023 in 2013.

Approximation

Where $f x$ is the negative value of $f(x)$. It may be positive or negative.
It gives the approximate value of any $\mathrm{f}(\mathrm{x})$ at $x=x_{\circ}$. We break $x_{\circ}$ to $x+\delta x$ Such that $(x+\delta x)=f(x)+f^{\prime}(x) \cdot \delta x$
ex $: \sqrt{25.5}$ we take $f(x)=\sqrt{x}, \quad x=25$ and $\delta x=0.5$
we will use differentials to approximate values of certain quantities.
Let $f: D \rightarrow R, D \subset R$, be a given function, and let $y=f(x)$. Let $\Delta x$ denote a small increment in x . Recall that the increment in y corresponding to the increment in $x$, denoted by $\Delta y$, is given by $\Delta y=f(x$ $+\Delta x)-f(x)$. We define the following
(i) The differential of x , denoted by dx , is defined by $\mathrm{dx}=\Delta \mathrm{x}$.
(ii) The differential of $y$, denoted by $d y$, is defined by $d y=f^{\prime}(x) d x$ or

$
d y=\left(\frac{d y}{d x}\right) \Delta x
$

Approximations and Errors Using Derivatives

Let the function, $y=f(x)$, be a function of $x$
As we have derived derivatives earlier,

$
\frac{d y}{d x}=\lim\limits_{\Delta x \rightarrow 0} \frac{\Delta y}{\Delta x}=\lim\limits_{\Delta x \rightarrow 0} \frac{f(x+\Delta x)-f(x)}{\Delta x}
$

$\Delta x$ is a small change in $x$ and the corresponding change in $y$ is $\Delta y$
As in the figure, point $Q$ moves closer to point $P$ on the curve, then dy is a good approximation of $\Delta y$.

$
\begin{aligned}
& \frac{d y}{d x}=\frac{\Delta y}{\Delta x}=\frac{f(x+\Delta x)-f(x)}{\Delta x} \\
\therefore \quad & \mathbf{f}(\mathbf{x}+\Delta \mathbf{x})=\mathbf{f}(\mathbf{x})+\Delta \mathbf{x} \cdot \frac{\mathbf{d y}}{\mathbf{d x}}
\end{aligned}
$

ERROR

Absolute Error

$\Delta \mathrm{x}$ or $d x$ is called absolute error in $x$.

Relative Error

$\frac{\Delta \mathrm{x}}{\mathrm{x}}$ or $\frac{d x}{\mathrm{x}}$ is called the relative error in $x$

Percentage Error

$\frac{\Delta \mathrm{x}}{\mathrm{x}} \cdot 100$ or $\frac{d x}{\mathrm{x}} \cdot 100$ is called the percentage error in $x$.

Recommended Video Based on Condition of Approximation

Solved Examples Based on Condition of Approximation:

Example 1: Let $f(1)=-2$ and $f^{\prime}(x) \geqslant 4.2$ for $1 \leq x \leq 6$ The possible value of $f(6)$ lies in the interval:
1) $[1,2\}$
2) $[13, \infty)$
3) $[14, \infty)$
4) $[19, \infty)$

Solution:

Given $f(1)=-2$ and $f^{\prime}(x) \geqslant 4.2$ for $1 \leq x \leq 6$
Consider $f^{\prime}(x)=\frac{f(x+h)-f(x)}{h}$

$
\Rightarrow f(x+h)-f(x)=f^{\prime}(x) \cdot h \geq(4.2) h
$
So, $f(x+h) \geq f(x)+(4.2) h$
put $x=1$ and $h=5$,
we get

$
\begin{aligned}
& f(6) \geq f(1)+5(4.2) \\
& \Rightarrow f(6) \geq 19
\end{aligned}
$
Hence $f(6)$ lies in $[19, \infty)$
Hence, the answer is option 4.

Example 2: Approximate value of $\sqrt{400.1}$ will be
1) 20.0025
2) 20.01
3) 20.001
4) 20.0035

Solution

Let $y=f(x)=\sqrt{x}$
With a small change $\Delta x$ in x , there will be a small change in y i.e., $\Delta y$

Now,

$
\begin{aligned}
& y=\sqrt{x} \Rightarrow \frac{d y}{d x}=\frac{1}{2 \sqrt{x}} \Rightarrow d y=\frac{d x}{2 \sqrt{x}} \\
& \Rightarrow \Delta y=\frac{\Delta x}{2 \sqrt{x}}\qquad . . . (i)
\end{aligned}
$
For a given question, let $\mathrm{x}=400, x+\Delta x=400.1 \Rightarrow \Delta x=0.1$
Also $\mathrm{y}=\sqrt{400}=20$

$
\begin{aligned}
& \therefore \Delta y=\frac{0.1}{2 \sqrt{400}}=0.1 / 40=0.0025 (Using (i))\\
& \therefore y+\Delta y=20+0.0025=20.0025
\end{aligned}
$

Hence, the answer is the option 1.

Example 3: Approximate value of $(999)^{\frac{1}{3}}$ equals
1) 9.96
2) 9.99
3) 10
4) 9.997

Solution

Let $\mathrm{y}=\mathrm{f}(\mathrm{x})=x^{1 / 3}$
With a small change $\Delta x$ in x , there will be a small change $\Delta y$ in y
Now,

$
\begin{aligned}
& y=x^{1 / 3} \Rightarrow \frac{d y}{d x}=\frac{1}{3 x^{2 / 3}} \\
& \frac{d y}{d x}=\frac{1}{3 x^{2 / 3}} \Rightarrow \Delta y=\frac{\Delta x}{3 x^{2 / 3}}
\end{aligned}
$
For the given question, $\mathrm{x}=1000, x+\Delta x=999 \Rightarrow \Delta x=-1$
Also $y=(1000)^{\frac{1}{3}}=10$
Now using (i)

$
\begin{aligned}
& \therefore \Delta y=\frac{-1}{3 *(1000)^{\frac{2}{3}}}=-0.00333 \\
& \therefore y+\Delta y=10-0.0033=9.99667 \approx 9.997
\end{aligned}
$
Hence, the answer is the option 4.

Frequently Asked Questions (FAQs)

1. What is approximation?

Approximation involves estimating the values of a function $y=f(x)$ based on small changes in $x$.

2. What is absolute error?

$\Delta \mathrm{x}$ or $d x$ is called absolute error in $x$.

3. What is relative error?

 $\frac{\Delta \mathrm{x}}{\mathrm{x}}$ or $\frac{d x}{\mathrm{x}}$ is called the relative error in $x$.

4. What is percentage error?

$\frac{\Delta \mathrm{x}}{\mathrm{x}} \cdot 100$ or $\frac{d x}{\mathrm{x}} \cdot 100$ is called the percentage error in $x$.

5. Why do we use the approximation in Mathematics?

 An approximate model can also be used to make the calculations easier.

6. What is the main purpose of using derivatives for approximations?
The main purpose of using derivatives for approximations is to estimate the value of a function near a known point without directly calculating the function at that point. This is particularly useful when the exact calculation is difficult or time-consuming. Derivatives provide a linear approximation of the function's behavior in a small neighborhood around the known point.
7. How does the concept of tangent lines relate to approximations using derivatives?
Tangent lines are closely related to approximations using derivatives because the derivative at a point gives the slope of the tangent line at that point. The tangent line serves as a linear approximation of the function near the point of tangency. This relationship forms the basis for using derivatives to approximate function values.
8. What is the difference between linear approximation and linearization?
Linear approximation and linearization are essentially the same concept. Both refer to the process of approximating a function near a point using a linear function (usually the tangent line). The term "linearization" is sometimes used to emphasize the process of making a non-linear function behave linearly in a small region.
9. How accurate are approximations using derivatives?
The accuracy of approximations using derivatives depends on how close you are to the point of approximation and the behavior of the function. Generally, the approximation is more accurate for points closer to the known point and for functions that are well-behaved (smooth and not rapidly changing). The error in the approximation typically increases as you move farther from the known point.
10. What is the formula for linear approximation using derivatives?
The formula for linear approximation using derivatives is:
11. Can approximations using derivatives be applied to any function?
Approximations using derivatives can be applied to any differentiable function. However, the accuracy and usefulness of the approximation depend on the function's behavior. Functions that are continuous and smooth tend to yield better approximations. Functions with sharp turns, discontinuities, or rapid oscillations may have less accurate approximations, especially away from the point of approximation.
12. What is the difference between absolute error and relative error in approximations?
Absolute error is the magnitude of the difference between the true value and the approximated value. Relative error is the absolute error divided by the true value, often expressed as a percentage. Relative error gives a sense of the error's significance in relation to the true value, while absolute error provides the actual numerical difference.
13. How does the order of the derivative affect the accuracy of the approximation?
Higher-order derivatives can be used to create more accurate approximations. While a first-order derivative gives a linear approximation, using second-order derivatives (and higher) can provide polynomial approximations that more closely match the function's curvature. However, these higher-order approximations are more complex to calculate and interpret.
14. What is Taylor's theorem and how does it relate to approximations using derivatives?
Taylor's theorem is a generalization of linear approximation that uses higher-order derivatives to create more accurate polynomial approximations of a function. It states that any sufficiently smooth function can be approximated by a polynomial whose coefficients are determined by the function's derivatives at a single point. This theorem provides a theoretical foundation for understanding and improving approximations using derivatives.
15. How do you determine if an approximation using derivatives is "good enough" for a specific application?
Determining if an approximation is "good enough" depends on the specific requirements of the application. Factors to consider include:
16. What is the relationship between the Mean Value Theorem and approximations using derivatives?
The Mean Value Theorem (MVT) provides a theoretical foundation for approximations using derivatives. It states that for a function continuous on [a,b] and differentiable on (a,b), there exists a point c in (a,b) where the derivative equals the average rate of change over [a,b]. This theorem justifies the use of derivatives for approximations by ensuring that the derivative accurately represents the function's behavior over small intervals.
17. How do approximations using derivatives relate to the concept of local linearity?
Approximations using derivatives are directly based on the concept of local linearity. Local linearity states that a differentiable function behaves like a straight line when viewed up close (in a small neighborhood around a point). The derivative at a point gives the slope of this locally linear approximation, which is why we can use the tangent line (a linear function) to approximate the function near that point.
18. What are some real-world applications of approximations using derivatives?
Approximations using derivatives have numerous real-world applications, including:
19. How does the concept of differentials relate to approximations using derivatives?
Differentials are closely related to approximations using derivatives. A differential (dy) represents the approximate change in a function's value (y) corresponding to a small change in the input (dx). The relationship is given by dy = f'(x)dx, where f'(x) is the derivative. This concept is essentially the same as linear approximation, but expressed in terms of small changes rather than absolute values.
20. What is the difference between forward, backward, and central differences in numerical approximations of derivatives?
Forward, backward, and central differences are numerical methods to approximate derivatives:
21. How do approximations using derivatives relate to the concept of sensitivity analysis?
Approximations using derivatives are fundamental to sensitivity analysis. In sensitivity analysis, we study how changes in input variables affect the output of a system or model. The derivative of a function with respect to a variable gives the rate of change of the output with respect to that variable, providing a measure of sensitivity. This allows us to approximate how small changes in inputs will affect the output without recalculating the entire model.
22. What is the role of higher-order derivatives in improving approximations?
Higher-order derivatives can improve approximations by capturing more information about the function's behavior. While first-order derivatives provide linear approximations, second-order derivatives capture curvature, third-order derivatives capture change in curvature, and so on. Including these higher-order terms in Taylor series expansions allows for more accurate polynomial approximations of the function, especially over larger intervals.
23. How do approximations using derivatives relate to the concept of linearization in multivariable calculus?
In multivariable calculus, linearization extends the concept of linear approximation to functions of several variables. Instead of using a single derivative, we use partial derivatives to create a linear approximation of the function near a point. The approximation takes the form of a tangent plane (for two variables) or a hyperplane (for more than two variables). The process is similar to single-variable approximation but involves the gradient vector instead of a single derivative.
24. What is the connection between approximations using derivatives and Newton's method for finding roots?
Newton's method for finding roots of equations is a direct application of approximations using derivatives. The method uses the tangent line approximation at a point to estimate where the function crosses the x-axis (i.e., where f(x) = 0). By iteratively applying this process, we can converge on a root of the equation. The formula for Newton's method, x_(n+1) = x_n - f(x_n)/f'(x_n), is derived from the linear approximation formula.
25. How do approximations using derivatives help in understanding the behavior of complex functions?
Approximations using derivatives help in understanding complex functions by:
26. What is the relationship between approximations using derivatives and the concept of error bounds?
Approximations using derivatives are closely related to error bounds. The error in a linear approximation can be bounded using the second derivative of the function (if it exists and is continuous). This bound, known as the remainder term in Taylor's theorem, provides an estimate of the maximum possible error in the approximation. Understanding these error bounds is crucial for determining the reliability and applicability of the approximation in various contexts.
27. How do approximations using derivatives relate to the concept of numerical integration?
Approximations using derivatives play a role in numerical integration methods, particularly in error analysis and in developing more advanced integration techniques. For example:
28. What is the significance of the Lipschitz condition in relation to approximations using derivatives?
The Lipschitz condition is important in the context of approximations using derivatives because it ensures that the function doesn't change too rapidly. A function f(x) satisfies a Lipschitz condition if there exists a constant K such that |f(x) - f(y)| ≤ K|x - y| for all x and y in the domain. This condition guarantees that the function is continuous and has a bounded derivative (if it exists), which in turn ensures that linear approximations using derivatives will have bounded errors.
29. How do approximations using derivatives relate to the concept of differential equations?
Approximations using derivatives are fundamental to the study and solution of differential equations:
30. What is the role of complex derivatives in approximations of complex functions?
Complex derivatives extend the concept of approximations using derivatives to functions in the complex plane. The key principles remain similar:
31. How do approximations using derivatives relate to the concept of asymptotic analysis?
Approximations using derivatives are closely related to asymptotic analysis:
32. What is the connection between approximations using derivatives and the concept of sensitivity in control systems?
In control systems, sensitivity refers to how system outputs change in response to variations in inputs or parameters. Approximations using derivatives are fundamental to sensitivity analysis:
33. How do approximations using derivatives relate to the concept of gradient descent in optimization?
Gradient descent, a fundamental optimization algorithm, is directly based on approximations using derivatives:
34. What is the role of approximations using derivatives in understanding the stability of dynamical systems?
Approximations using derivatives are crucial in analyzing the stability of dynamical systems:
35. How do approximations using derivatives relate to the concept of curvature in differential geometry?
Approximations using derivatives are fundamental to understanding curvature in differential geometry:
36. What is the relationship between approximations using derivatives and the concept of convexity in optimization?
Approximations using derivatives play a crucial role in understanding and utilizing convexity in optimization:
37. How do approximations using derivatives relate to the study of singularities in complex analysis?
In complex analysis, approximations using derivatives are essential for understanding singularities:
38. What is the significance of approximations using derivatives in the study of manifolds?
Approximations using derivatives are fundamental to the study of manifolds in differential geometry:

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