Regula Falsi Method for Solving Equations

Regula Falsi method, also known as the false position method, is a numerical method used to find the root of an equation within a given interval. It is one of the oldes and easily implmentable method.

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What is Regula-Falsi Method? #

The Regula Falsi method, also known as the false position method, is a numerical method used to find the root of an equation within a given interval. It is an iterative method that makes use of linear interpolation to approximate the root.

The method starts with two initial guesses, a and b, such that f(a) and f(b) have opposite signs, indicating that the root lies between them. The Regula Falsi method then computes a new estimate, c, for the root by finding the x-intercept of the line connecting the points (a, f(a)) and (b, f(b)).

The formula for computing c in the Regula Falsi method is:

c = (a * f(b) - b * f(a)) / (f(b) - f(a))

If f(c) is close enough to zero or the desired tolerance level, c is considered the root. Otherwise, the method updates the interval by replacing either a or b with c, depending on the sign of f(c). The process continues iteratively until the desired level of accuracy is achieved.

The Regula Falsi method is similar to the bisection method, but instead of taking the midpoint of the interval, it selects the x-intercept of the line connecting the function values at the endpoints. This can lead to faster convergence in some cases, especially when the function has asymmetrical behavior near the root.

History of Regula Falsi Method #

The origins of the Regula Falsi method can be traced back to the work of Greek mathematicians, particularly Hero of Alexandria (c. 10-70 CE). Hero described a method for approximating roots of equations in his work “Metrica,” which involved a geometric interpretation of the method. His approach was based on the idea of linear interpolation between two initial guesses to estimate the root.

One of the earliest Indian mathematicians who discussed the Regula Falsi method was the mathematician and astronomer Brahmagupta (598-668 CE). Brahmagupta described the method in his treatise “Brahmasphutasiddhanta” (Correctly Established Doctrine of Brahma), which included rules for solving quadratic equations. He used a variant of the Regula Falsi method to approximate roots of quadratic equations.

Another influential Indian mathematician, Mahāvīra (c. 9th century CE), also contributed to the development of the Regula Falsi method. He discussed the technique in his work “Ganitasarasangraha” (Compendium of the Essence of Mathematics), where he provided detailed explanations and examples of the method’s application to solve equations.

Indian mathematicians played a crucial role in the development and dissemination of mathematical knowledge during the classical period. Their contributions not only included the Regula Falsi method but also encompassed other mathematical topics such as arithmetic, algebra, trigonometry, and numerical analysis.

The Regula Falsi method gained further prominence during the medieval period and was used by Islamic mathematicians. One notable mathematician who contributed to the development and understanding of the method was Abu al-Hasan al-Uqlidisi (c. 952-1000 CE), an Arab mathematician from Baghdad. He wrote a treatise titled “Kitab al-Fusul fi al-Hisab al-Hindi” (Book of Chapters on Hindu Numeration), in which he discussed the Regula Falsi method as a technique for solving polynomial equations.

Throughout history, the Regula Falsi method underwent refinements and modifications as mathematicians made advancements in numerical analysis and calculus. The method was described by various mathematicians, including Persian mathematician Sharaf al-Dīn al-Tūsī (1135-1213) and Italian mathematician Leonardo Fibonacci (c. 1170-1250). It continued to be a fundamental technique for root finding until more sophisticated methods were developed later, such as Newton’s method in the 17th century.

Today, the Regula Falsi method holds historical significance as one of the earliest known numerical methods for finding roots of equations. While it may not be as commonly used as more advanced methods, its historical significance and fundamental principles have contributed to the development of numerical analysis as a field of study.

JavaScript Code for Regula Falsi Method #

Below an example of how you can implement the Regula Falsi method in JavaScript to approximate the root of an equation. You can experiment using the notebook on Scribbler: Regula Falsi Method

1. regulaFalsi Function #

function regulaFalsi(func, a, b, tolerance, maxIterations) {
  let c;
  let iterations = 0;

  while (Math.abs(b - a) > tolerance && iterations < maxIterations) {
    c = (a * func(b) - b * func(a)) / (func(b) - func(a));

    if (func(c) === 0) {
      break; // Found exact root
    } else if (func(a) * func(c) < 0) {
      b = c;
    } else {
      a = c;


  return c;
  • Parameters:
    • func: This is a function parameter representing the function whose root we want to find.
    • a, b: These are the initial guesses or initial brackets of the root. The function assumes that func(a) and func(b) have opposite signs, ensuring that the root lies between a and b.
    • tolerance: This parameter specifies the acceptable error tolerance for the root. The iteration stops when the absolute difference between b and a becomes smaller than this tolerance.
    • maxIterations: This parameter sets the maximum number of iterations allowed. If the root is not found within these iterations, the function returns the best approximation found so far.
  • Initialization:
    • c: This variable holds the current approximation of the root.
    • iterations: This counter tracks the number of iterations performed.
  • Iteration Loop:
    • The while loop continues iterating until one of the stopping conditions is met: either the difference between b and a is less than tolerance, or the maximum number of iterations (maxIterations) is reached.
  • Calculating c:
    • In each iteration, c is calculated using the formula derived from the secant line between points (a, func(a)) and (b, func(b)). This formula ensures that c lies on the secant line and updates iteratively towards the root of func.
  • Root Condition:
    • If func(c) equals 0, the loop breaks early because c is an exact root of func.
  • Updating Brackets:
    • Depending on the sign of func(a) * func(c), the new bracket [a, b] for the root is adjusted. If func(a) * func(c) is less than zero, the root is between a and c, so b is updated to c. Otherwise, the root is between c and b, so a is updated to c.
  • Iterations Count:
    • The iterations counter increments after each iteration to keep track of the number of iterations performed.
  • Return:
    • Once the loop exits, either due to achieving the desired tolerance or reaching the maximum iterations, c is returned as the approximate root found by the Regula Falsi method.

2. Example Usage #

// Example usage
function f(x) {
  return x * x - 4; // Solve the equation x^2 - 4 = 0

const root = regulaFalsi(f, 1, 3, 0.0001, 100);
console.log("Approximate root:", root);
  • f(x) Function:
    • This is an example function f(x) defined to solve the equation x^2 - 4 = 0. The function returns the value of the equation at any given x.
  • Calling regulaFalsi:
    • regulaFalsi(f, 1, 3, 0.0001, 100) calls the regulaFalsi function with parameters:
      • f: The function f(x) defined above.
      • 1 and 3: Initial guesses a and b.
      • 0.0001: Tolerance (maximum acceptable error).
      • 100: Maximum number of iterations allowed.
  • Output:
    • The function calculates and returns the approximate root of the equation x^2 - 4 = 0 using the Regula Falsi method. The result is then logged to the console.

Applications of the Regula Falsi Method in Numerical Analysis #

The Regula Falsi method has found various applications across different fields due to its simplicity and effectiveness in solving nonlinear equations. In this article, we will explore some key applications of the Regula Falsi method in numerical analysis and engineering.

1. Solving Nonlinear Equations #

The primary application of the Regula Falsi method is in finding roots of nonlinear equations of the form ( f(x) = 0 ). Unlike linear equations, where solutions can be straightforwardly calculated, nonlinear equations often require iterative numerical methods. The Regula Falsi method provides a reliable approach to approximate these roots within a specified tolerance.

2. Circuit Analysis #

In electrical engineering, the Regula Falsi method can be applied to analyze electrical circuits involving nonlinear components such as diodes and transistors. By formulating circuit equations and applying the method, engineers can determine operating points, voltages, currents, and other critical parameters.

3. Chemical Engineering #

In chemical engineering, processes often involve nonlinear relationships between variables such as temperature, pressure, and concentrations. The Regula Falsi method can be used to solve equations derived from mass and energy balances, helping engineers optimize processes and predict behavior under different conditions.

4. Economics and Finance #

Economic models and financial calculations frequently involve nonlinear equations that describe relationships between variables such as supply and demand, interest rates, and investment returns. The Regula Falsi method can be employed to find equilibrium points, analyze economic trends, and optimize investment strategies.

5. Optimization Problems #

The Regula Falsi method is sometimes used as a component in optimization algorithms, especially when the objective function is continuous but nonlinear. By iteratively refining estimates of roots or critical points, this method can assist in solving optimization problems where finding a global or local minimum/maximum is required.

6. Biomedical Engineering #

In biomedical engineering, various physiological processes and biochemical reactions can be modeled using nonlinear equations. The Regula Falsi method aids in analyzing these models, predicting outcomes, and optimizing treatment strategies.

7. Heat Transfer and Fluid Dynamics #

Numerical simulations in heat transfer and fluid dynamics often involve nonlinear equations governing the behavior of temperature, pressure, velocity, and other variables. The Regula Falsi method can be applied to solve these equations, aiding in the design and analysis of heat exchangers, fluid flow systems, and thermal management solutions.

8. Environmental Modeling #

Environmental scientists and engineers use mathematical models to simulate complex interactions in ecosystems, pollution dispersion, and climate change. The Regula Falsi method contributes to solving equations derived from these models, helping researchers make informed decisions and predictions.

9. Academic Research and Education #

The Regula Falsi method plays a crucial role in academic research and education, serving as an introductory numerical method for studying root-finding techniques and iterative algorithms. It helps students and researchers grasp fundamental concepts in numerical analysis and apply them to practical problems across various disciplines.

Shortcomings of the Regula Falsi Method and Alternatives in Numerical Analysis #

The Regula Falsi method, while effective for solving certain types of equations, comes with limitations that may affect its applicability and efficiency in numerical analysis. In this article, we will explore the shortcomings of the Regula Falsi method and discuss alternative methods that address these limitations.

Shortcomings of the Regula Falsi Method #

  1. Slow Convergence Rate: The Regula Falsi method can converge slowly, especially when the initial guesses do not sufficiently bracket the root or when the function has regions of high curvature. This slow convergence can lead to inefficiencies, requiring more iterations to achieve the desired accuracy compared to other methods.

  2. Potential for Oscillations: In some cases, the Regula Falsi method may exhibit oscillatory behavior, where the iterates alternate on either side of the root without converging quickly. This oscillation can prolong the convergence process and make it less predictable.

  3. Bracketing Requirement: The method requires initial guesses that bracket the root, meaning the function must change sign between the initial guesses. If suitable initial guesses are not known or are difficult to determine, it can be challenging to apply the Regula Falsi method effectively.

  4. Vulnerability to Flat Regions: If the function has flat or nearly flat regions near the root, the Regula Falsi method may struggle to make significant progress towards convergence. It may get stuck in these regions, leading to slow or stalled convergence.

  5. No Guarantee of Convergence: Unlike some other root-finding methods like Newton-Raphson, the Regula Falsi method does not always guarantee convergence to a root. Depending on the function and initial guesses, it may fail to converge or converge to a wrong root.

Alternatives to the Regula Falsi Method #

While the Regula Falsi method has its limitations, several alternative methods offer different approaches to solving nonlinear equations more efficiently and reliably:

  1. Newton-Raphson Method: The Newton-Raphson method uses the derivative (slope) of the function to iteratively refine the root approximation. It generally converges faster than the Regula Falsi method but requires the derivative to be known or computable.

  2. Secant Method: The Secant method is similar to the Newton-Raphson method but approximates the derivative using finite differences. It does not require the derivative to be known explicitly, making it more versatile in some cases.

  3. Bisection Method: The Bisection method is a simple and robust method that relies on bracketing the root and narrowing down the interval iteratively. While slower than the Regula Falsi method, it guarantees convergence and is straightforward to implement.

  4. Modified Regula Falsi Method: Variants of the Regula Falsi method, such as the Illinois method or the Pegasus method, aim to address some of its shortcomings by adjusting the update rule or the criteria for selecting new bounds.

  5. Brent’s Method: Brent’s method combines the bisection method, the secant method, and inverse quadratic interpolation. It is known for its robustness and typically performs well across a wide range of functions, often outperforming other methods in terms of convergence speed and reliability.

The Regula Falsi method and the Newton-Raphson method are both iterative methods for finding roots of functions, but they have different characteristics in terms of convergence speed and behavior. Here are the key reasons why the Regula Falsi method is generally slower than the Newton-Raphson method:

Regula Falsi vs Newton-Raphson #

Regula Falsi Method #

  • Linear Convergence: The Regula Falsi method has linear convergence, meaning that the number of correct digits in the approximation increases linearly with the number of iterations. This can be relatively slow, especially if the function near the root is not very steep.
  • Dependence on Function Values: Regula Falsi uses linear interpolation between two points on the function to estimate the root. This method relies solely on function values without using derivative information, which can limit the rate at which the method converges to the root.

Newton-Raphson Method #

  • Quadratic Convergence: The Newton-Raphson method has quadratic convergence, meaning that the number of correct digits approximately doubles with each iteration when the initial guess is close to the root. This leads to much faster convergence compared to linear methods.
  • Use of Derivatives: The Newton-Raphson method uses both the function value and its derivative to estimate the next approximation. This use of derivative information provides more accurate and faster updates, especially near the root.
  • Step Calculation: In each iteration, the Newton-Raphson method calculates the next approximation using the formula ( x_{n+1} = x_n - \frac{f(x_n)}{f’(x_n)} ). This formula tends to converge more rapidly because it accounts for the slope of the function, effectively guiding the iteration process more directly towards the root.

Comparison #

  1. Convergence Rate:
    • Regula Falsi: Linear convergence.
    • Newton-Raphson: Quadratic convergence.
  2. Use of Information:
    • Regula Falsi: Uses only function values.
    • Newton-Raphson: Uses both function values and their derivatives.
  3. Efficiency:
    • Regula Falsi: Slower, particularly when the function is flat near the root.
    • Newton-Raphson: Faster, especially when the derivative information leads to better approximations.
  4. Initial Guess Sensitivity:
    • Regula Falsi: Less sensitive to initial guesses but may converge slowly.
    • Newton-Raphson: Requires a good initial guess close to the root to ensure rapid convergence.

Summary #

The primary reason the Regula Falsi method is slower than the Newton-Raphson method is due to their convergence rates. The Newton-Raphson method benefits from quadratic convergence and the use of derivative information, making it significantly faster and more efficient, particularly when the initial guess is reasonably close to the root. In contrast, the Regula Falsi method’s linear convergence and reliance solely on function values result in slower progress towards the root.

Choosing the Right Method #

The choice of root-finding method depends on several factors including the nature of the function, the availability of derivatives, the desired accuracy, and computational efficiency. While the Regula Falsi method remains a valuable tool in numerical analysis, especially for functions with well-behaved characteristics and known initial brackets, understanding its limitations and considering alternative methods can lead to more effective problem-solving in practical applications.