Name: newton’s method for finding roots
Uploaded: 2026-01-12T12:24:54.000Z
Duration: 1 min 30 s
Channel: JoVE

Metoda Newtona to technika iteracyjny służąca do znajdowania przybliżonych pierwiastków funkcji różniczkowalnych o wartościach rzeczywistych.Pomaga rozwiązywać nieliniowe równania, które są zbyt złożone dla standardowych metod algebraicznych.Na przykład metoda Newtona może oszacować stopę procentową na podstawie nieliniowego równania, które modeluje spłatę kredytu samochodowego. Równania te zapisuje się jako y równe f z x i często są przedstawiane graficznie, aby rozwinąć wzór.Proces zaczyna się od początkowego przypuszczenia, opartego na przybliżonym oszacowaniu korzenia.W zgadywanym punkcie rysuje się linię styczną na podstawie nachylenia funkcji. Przecięcie x tej prostej staje się nowym oszacowaniem, które wizualnie jest bliższe rzeczywistemu korzeniowi.To nowe oszacowanie pochodzi z liniowego przybliżenia. Jest równa początkowej estymatice minus wartość funkcji podzielonej przez jej pochodną przy tej estymacji.Proces powtarza się przy użyciu nowego oszacowania. Przy każdym powtórzeniu wartości często zbliżają się do rzeczywistego korzenia.To prowadzi do ogólnego wzoru: nowa estymacja jest równa poprzedniej estymatice minus wartość funkcji podzielona przez jej pochodną.Każdy krok udoskonala przybliżenie, czyniąc metodę Newtona skutecznym narzędziem iteracyjnym do rozwiązywania równań nieliniowych.

metoda newtona

Newton’s Method

Metoda Newtona to skuteczna technika iteracyjna s&#x142;u&#x17C;&#x105;ca do przybli&#x17C;ania pierwiastk&#xF3;w funkcji r&#xF3;&#x17C;niczkowalnych o warto&#x15B;ciach rzeczywistych, szczeg&#xF3;lnie gdy rozwi&#x105;zania analityczne s&#x105; niepraktyczne. Podej&#x15B;cie to jest szeroko stosowane w obliczeniach naukowych, in&#x17C;ynierii i finansach, gdzie r&#xF3;wnania mog&#x105; by&#x107; zbyt z&#x142;o&#x17C;one dla tradycyjnych metod algebraicznych. Metoda ta opiera si&#x119; na procesie iteracyjnym, umo&#x17C;liwiaj&#x105;cym stopniowe zbli&#x17C;anie si&#x119; do rzeczywistego rozwi&#x105;zania poprzez wykorzystanie pochodnej funkcji. Matematycznie rzecz bior&#x105;c, metoda ta opiera si&#x119; na wzorze rekurencyjnym:<figure class="image image-style-align-center image_resized" style="display:table;margin-left:auto;margin-right:auto;width:50%;"><img style="aspect-ratio:300/48;" src="https://cloudfront.jove.com/files/ftp_upload/19849/19849_PT_Equation_1_editor_19849.svg" alt="19849_PT_Equation_1.svg" /></figure>gdzie:x_n = aktualne przybli&#x17C;enie pierwiastkaf(x_n) = warto&#x15B;&#x107; funkcji w punkcie x_nf&#x2032;(x_n) = pochodna funkcji w punkcie x_nx_n+1 = kolejne przybli&#x17C;enie, obliczone na podstawie bie&#x17C;&#x105;cego oszacowania.Ka&#x17C;da iteracja zbli&#x17C;a przybli&#x17C;enie do rzeczywistego pierwiastka, o ile pocz&#x105;tkowe oszacowanie jest wystarczaj&#x105;co bliskie, a funkcja zachowuje si&#x119; odpowiednio.Jednym z praktycznych zastosowa&#x144; metody Newtona jest modelowanie finansowe, na przyk&#x142;ad szacowanie st&#xF3;p procentowych na podstawie nieliniowych r&#xF3;wna&#x144; sp&#x142;at. W takich kontekstach r&#xF3;wnania te mog&#x105; nie nadawa&#x107; si&#x119; do rozwi&#x105;zania jawnego, ale metoda Newtona pozwala na efektywn&#x105; zbie&#x17C;no&#x15B;&#x107; do pierwiastka przy minimalnej liczbie krok&#xF3;w obliczeniowych, pod warunkiem wybrania odpowiedniego przybli&#x17C;enia pocz&#x105;tkowego.Ze wzgl&#x119;du na swoj&#x105; efektywno&#x15B;&#x107; i szybk&#x105; zbie&#x17C;no&#x15B;&#x107;, metoda Newtona-Raphsona pozostaje jedn&#x105; z najskuteczniejszych technik znajdowania pierwiastk&#xF3;w i rozwi&#x105;zywania r&#xF3;wna&#x144; w matematyce stosowanej oraz naukach obliczeniowych.Pomimo swoich zalet, metoda Newtona nie gwarantuje zbie&#x17C;no&#x15B;ci we wszystkich przypadkach. Je&#x15B;li pochodna f&#x2032;(x_n) jest r&#xF3;wna zero lub bardzo bliska zeru, wz&#xF3;r aktualizacji mo&#x17C;e prowadzi&#x107; do dzielenia przez ma&#x142;&#x105; liczb&#x119;, co powoduje niestabilno&#x15B;&#x107; numeryczn&#x105;. Ponadto niew&#x142;a&#x15B;ciwe przybli&#x17C;enia pocz&#x105;tkowe mog&#x105; spowodowa&#x107;, &#x17C;e metoda stanie si&#x119; rozbie&#x17C;na lub wejdzie w cykl zamiast zbli&#x17C;a&#x107; si&#x119; do pierwiastka. W szczeg&#xF3;lno&#x15B;ci w przypadku funkcji z punktami przegi&#x119;cia, ekstremami lokalnymi lub nieci&#x105;g&#x142;o&#x15B;ciami pochodnej metoda mo&#x17C;e nie osi&#x105;gn&#x105;&#x107; pierwiastka lub zbiec do niezamierzonego rozwi&#x105;zania. Dlatego staranna analiza funkcji oraz odpowiednio dobrane oszacowanie pocz&#x105;tkowe s&#x105; kluczowe dla zapewnienia skutecznego zastosowania metody Newtona.

Metoda Newtona

Metoda Newtona to skuteczna technika iteracyjna s&#x142;u&#x17C;&#x105;ca do przybli&#x17C;ania pierwiastk&#xF3;w funkcji r&#xF3;&#x17C;niczkowalnych o ...

Education

JoVE Core

Calculus

Rachunek różniczkowy i całkowy

Unit

Jednostka

Applications of Differentiation

Zastosowania różniczkowania

KEY TERMS AND CONCEPTS

KLUCZOWE TERMINY I POJĘCIA

newton’s method for finding roots

Newton’s Method is an iterative way to find the roots of real-valued, differentiable functions. A root is the value of x that makes a function equal to zero. This method is useful when an exact algebraic answer is hard to find.

The process starts with an initial guess. Newton’s Method then uses the function value and its derivative, which is the slope of the function at that point, to build a better estimate. The next approximation is found from the recursive formula x n+1 = x n - f(x n) / f'(x n). Each step can move the estimate closer to the true root when the first guess is reasonable and the function behaves well.

This method is widely used in scientific computing, engineering, and finance. One practical example is financial modeling, where it can help estimate interest rates from nonlinear repayment equations. These problems often do not have a simple explicit solution, so Newton’s Method can reach a root in only a few steps.

Newton’s Method does not always converge. If the derivative f'(x n) is zero or very close to zero, the formula can become unstable because it divides by a small number. A poor starting guess can also make the method diverge or cycle instead of approaching a root.

The method can also fail near inflection points, local extrema, or places where the derivative is discontinuous. In some cases, it may converge to the wrong solution. That is why a careful look at the function and a well-chosen starting point are important for successful root finding.

Newton’s Method for Finding Roots

absolute and local extreme values

Finding Max and Min on Function Graphs

Absolute and local extreme values show the highest and lowest points on a function graph. Absolute maximum and absolute minimum values describe the highest and lowest values across the entire domain. Local maximum and local minimum values describe the highest and lowest values within a smaller part of the graph. Periodic functions such as sine and cosine can have extreme values at many points. Their repeating pattern creates multiple local extrema. In these cases, the derivative becomes zero...

critical numbers and the closed interval method

critical-numbers-and-the-closed-interval-method

Finding Absolute Extrema on a Closed Interval

Absolute extrema on a closed interval are found by checking critical numbers and the endpoints. A function’s maximum and minimum values help describe its overall behavior across its domain. These values are called extrema, and they can be local or absolute. Local extrema are peaks and valleys in a limited region. Absolute extrema are the highest or lowest points on the entire interval. Critical numbers are the domain values where the derivative is zero or does not exist. According to Fermat’s...

rolle’s theorem

Rolle’s Theorem and Zero Slope Points

Rolle’s Theorem explains when a differentiable function must have a flat tangent inside an interval. It applies to a real-valued function on a closed interval, which means the endpoints are included. The function must also be continuous on the whole interval and differentiable on the open interval, where the interior points lie. The theorem uses one more key condition. The function must have the same value at both endpoints. When that happens, Rolle’s Theorem guarantees at least one point...

the mean value theorem

Mean Change and Instantaneous Slope

The Mean Value Theorem links average change to instantaneous slope. It shows that when a function changes smoothly over an interval, the average change across the whole interval must match the change at one specific point inside it. The theorem is easy to see in a motion example. Imagine a vehicle traveling between two points on a road. Its velocity can change during the trip, beginning slowly, speeding up during acceleration, and slowing down near the end. Even when the speed is not...

first derivatives and the shape of a graph

first-derivatives-and-the-shape-of-a-graph

Using First Derivatives to Find Graph Changes

The first derivative shows how a function changes across its domain. In calculus, the derivative is written as f'(x). It tells us whether the graph is rising, falling, or turning at a given point. A function is increasing on an interval when its derivative is positive. That means the slope of the tangent line is positive. A function is decreasing on an interval when its derivative is negative. In that case, the tangent line slopes downward. These sign changes help describe the overall shape...

first derivative test: problem solving

Finding Turning Points with the First Derivative

The first derivative test is used to find turning points on a smooth curve. It shows where a function changes from increasing to decreasing, or from decreasing to increasing. In this example, the curve can model an asset price that falls to a low point, rebounds, and then gradually declines again. The process starts by finding the first derivative of the function. Because the function is a product of a polynomial term and an exponential term, the product rule is needed. After differentiating,...

second derivatives and the shape of a graph

second-derivatives-and-the-shape-of-a-graph

Using Second Derivatives to Read Curvature

The second derivative shows how a graph bends. It gives key information about curvature and how that curvature changes over an interval. These ideas help with graph analysis in math and in real-world settings such as road elevation, population growth, and economic trends. A function f(x) is concave upward on an interval when its graph lies above all of its tangent lines. In this case, the second derivative is positive. That means the slope of the tangent line is increasing, and the graph bends...

second derivative test: problem solving

Classifying Critical Points with the Second Derivative

The second derivative test helps classify critical points of a function. Critical points are places where the first derivative is zero or undefined. They are possible locations for local maxima and local minima, but they do not always become one or the other. The second derivative shows concavity, or whether a graph bends up or down. If f''(x) &gt; 0, the function is concave up, and the critical point is a local minimum. If f''(x) &lt; 0, the function is concave down, and the critical point is...

curve sketching and derivatives

Using Derivatives to Read a Graph

Derivatives help students read the graph of a function by showing where the graph rises, falls, and bends. The first derivative tells the slope of the tangent line at each point. The second derivative shows how the curve changes shape over an interval. Points where the first derivative is zero or undefined are critical points. These points are important because they may mark a local maximum, a local minimum, or no extremum at all. A local minimum appears when the first derivative changes from...

indeterminate forms and l’hôpital’s rule

Using Derivatives to Resolve Limit Ambiguity

Indeterminate forms appear when a limit gives an expression that cannot be read directly. Common examples are 0 divided by 0 and infinity divided by infinity. These results do not show the true behavior of the function near the point of interest, so more analysis is needed. L’Hôpital’s Rule is a useful method for handling these ambiguous limits. It applies when two differentiable functions approach 0 or infinity at the same time near the point being studied. Instead of finding the limit of the...

indeterminate products

Limits of Zero Times Infinity

Limits with a zero-times-infinity product are indeterminate. In this type of limit, one factor approaches zero while the other factor grows toward positive or negative infinity. The result is not obvious at first, because the product may approach zero, infinity, or a finite nonzero value. To evaluate these product limits, the expression is first rewritten as a quotient. This form makes it possible to use L'Hôpital's Rule, which compares the derivatives of the numerator and denominator. A...

guidelines for sketching a curve

Using Derivatives to Sketch Function Graphs

Sketching a function graph uses key features of the function to show its overall shape. A function plots as a curve on the coordinate plane, with the input on the horizontal axis and the output on the vertical axis. The first step is to find the domain, which is the set of input values where the function is defined. The domain shows how far the graph can extend left and right. Intercepts are also important reference points. The x-intercept is where the graph crosses the horizontal axis, and...

slant asymptotes

Rational Functions with Slant Asymptotes

Rational functions can follow a slant asymptote when the numerator grows faster than the denominator. In this case, a cubic numerator is divided by a squared denominator. That degree difference creates a linear trend that guides the graph at large input values. The function is only defined where the denominator is not zero. Those excluded x-values split the domain into separate regions and avoid singular points. The graph can still cross the x-axis where the numerator equals zero, so the real...

optimization problems

Pipe Length Around a Corner

Pipe length around a corner is a classic constrained optimization problem. It shows how calculus can find a maximum or minimum value when space is limited by fixed walls and angles. The example used here asks for the longest horizontal pipe that can move around a right-angled turn. One hallway is 3 meters wide and the other is 2 meters wide. The pipe is shown as a straight line that touches the inner corner and reaches the opposite walls of both hallways. The key idea is that the pipe’s...

application of differentiation to business

application-of-differentiation-to-business

Maximizing Bakery Revenue with Calculus

Calculus helps businesses make pricing decisions that can improve revenue. In this example, a bakery uses a cupcake model to find the best price and daily sales volume. The goal is to see how changes in price affect demand and total revenue. The demand function shows the link between price and how many cupcakes are sold each day. In this model, p(x) gives the price per cupcake when x cupcakes are sold. Lower prices lead to higher demand, so the function captures how sales change with price. <br...

newton’s method

Newton’s Method is an iterative way to find the roots of real-valued, differentiable functions. A root is the value of x that makes a function equal to zero. This method is useful when an exact algebraic answer is hard to find. The process starts with an initial guess. Newton’s Method then uses the function value and its derivative, which is the slope of the function at that point, to build a better estimate. The next approximation is found from the recursive formula x n+1 = x n - f(x n) /...

the antiderivative of a function

Finding Original Functions with Antiderivatives

Antiderivatives help reconstruct an original function from its derivative. In calculus, an antiderivative of a function f(x) is a function F(x) whose derivative gives back f(x). This idea works like reversing differentiation to recover the starting form. Antiderivatives are not unique because differentiation removes constant terms. For that reason, the general form includes an arbitrary constant C. That constant represents unknown initial conditions when a function is rebuilt from its...

graphing antiderivatives

Antiderivative Graph Shape and Slope

Antiderivative graphs show how a function accumulates over time. In calculus, an antiderivative tracks that buildup, much like the changing position of a rolling ball as its velocity changes. The graph of the antiderivative reflects the total effect of the original function's values. The sign of the original function helps determine the antiderivative's direction. When the function is positive, the antiderivative rises because the accumulation grows. When the function is negative, the...

application of antiderivatives: linear motion

application-of-antiderivatives-linear-motion

Finding Car Stopping Distance with Integration

Integration connects acceleration, velocity, and displacement in linear motion. It works as the reverse of a derivative, which tells how one quantity changes with another. In physics, this makes it possible to move from a rate of change back to the motion itself. A car provides a clear example. It is moving at a steady 20 meters per second when an obstacle appears 800 meters ahead. To stop in time, the car slows with a constant acceleration. That acceleration tells how the car’s velocity...

key terms and concepts

Learn how Newton’s Method uses derivatives and repeated guesses to find roots of equations, plus why a good starting point matters for convergence.

Newton’s Method for Finding Roots | JoVE K12

The JoVE Core Calculus collection provides a comprehensive visual guide to the entire calculus sequence, seamlessly connecting mathematical theory to real-world applications in physics and economics. Through dynamic animated lessons, the course transitions smoothly from foundational single-variable concepts to advanced multivariable topics. Students will explore everything from standard differentiation and integration to spatial vectors, multi-dimensional derivatives, and complex vector fields. By breaking down abstract coordinate systems, infinite series, and spatial motion, this structured approach builds the robust analytical skills and advanced mastery essential for STEM success.

calculus

Newton’s Method for Finding Roots

Description

Transcript

Tags