Numerical Techniques | 2. Numerical Solutions of Algebraic and Transcendental Equations by Pavan | Learn Smarter
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2. Numerical Solutions of Algebraic and Transcendental Equations

2. Numerical Solutions of Algebraic and Transcendental Equations

The chapter discusses several numerical methods for finding the roots of algebraic and transcendental equations, emphasizing the Bisection Method, Newton-Raphson Method, Secant Method, and Fixed-Point Iteration. Each method is described in terms of its workings, advantages, disadvantages, and practical examples. A comparison of the methods aids in understanding their respective performance in various scenarios.

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  1. 2
    Numerical Solutions Of Algebraic And Transcendental Equations

    This section explores numerical methods for solving algebraic and...

  2. 2.1
    Introduction To Numerical Methods For Solving Equations

    This section introduces numerical methods used for finding roots of...

  3. 2.2
    Bisection Method

    The Bisection Method is a reliable numerical technique for finding roots of...

  4. 2.2.1
    How The Bisection Method Works

    The Bisection Method is a reliable numerical technique for finding roots of...

  5. 2.2.2
    Advantages And Disadvantages

    This section outlines the advantages and disadvantages of the Bisection...

  6. 2.2.3
    Bisection Method Example

    The Bisection Method is illustrated through an example, demonstrating how it...

  7. 2.3
    Newton-Raphson Method

    The Newton-Raphson Method is an iterative technique for finding successively...

  8. 2.3.1
    How The Newton-Raphson Method Works

    The Newton-Raphson method is an iterative technique that utilizes tangent...

  9. 2.3.2
    Advantages And Disadvantages

    This section outlines the advantages and disadvantages of the Newton-Raphson...

  10. 2.3.3
    Newton-Raphson Method Example

    The Newton-Raphson method is an iterative technique used for finding...

  11. 2.4
    Secant Method

    The Secant Method is an iterative numerical technique for finding roots of...

  12. 2.4.1
    How The Secant Method Works

    The Secant Method is an iterative numerical approach for finding roots of...

  13. 2.4.2
    Advantages And Disadvantages

    This section outlines the advantages and disadvantages of the bisection...

  14. 2.4.3
    Secant Method Example

    The Secant Method is an iterative root-finding technique that approximates...

  15. 2.5
    Fixed-Point Iteration

    Fixed-point iteration is a numerical method for finding roots of equations...

  16. 2.5.1
    How Fixed-Point Iteration Works

    Fixed-point iteration is an iterative method for finding roots of equations...

  17. 2.5.2
    Advantages And Disadvantages

    This section outlines the advantages and disadvantages of several numerical...

  18. 2.5.3
    Fixed-Point Iteration Example

    This section illustrates the fixed-point iteration method by transforming...

  19. 2.6
    Comparison Of Methods

    This section compares different numerical methods for finding roots of...

  20. 2.7
    Summary Of Key Concepts

    This section summarizes the four primary numerical methods used to find...

What we have learnt

  • The Bisection Method guarantees convergence when bracketed correctly but has slow convergence.
  • The Newton-Raphson Method offers faster convergence but requires the derivative and may not work well with poor initial guesses.
  • The Secant Method avoids the derivative but requires two initial guesses and may also fail to converge.

Key Concepts

-- Bisection Method
A reliable root-finding technique requiring two initial points where the function changes sign, guaranteeing convergence.
-- NewtonRaphson Method
An iterative method that uses derivatives to find roots more quickly, converging quadratically if close to the root.
-- Secant Method
An alternative to Newton-Raphson that approximates derivatives using secant lines rather than requiring actual derivatives.
-- FixedPoint Iteration
An iterative method that rewrites an equation into a form suitable for successive approximations, although convergence is not guaranteed.

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