# Program For Bisection Method In Fortran Code

Time complexity :- Time complexity of this method depends on the assumed values and the function. What are pros and cons? Advantage of the bisection method is that it is guaranteed to be converged. Disadvantage of bisection method is that it cannot detect multiple roots.In general, Bisection method is used to get an initial rough approximation of solution. Then faster converging methods are used to find the solution. We will soon be discussing other methods to solve algebraic and transcendental equationsReferences: Introductory Methods of Numerical Analysis by S.S. Sastry _methodThis article is contributed by Abhiraj Smit. Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above

## Program For Bisection Method In Fortran Code

The development of FORTRAN paralleled the early evolution of compiler technology indeed many advances in the theory and design of compilers were specifically motivated by the need to generate efficient code for FORTRAN programs. For these reasons, FORTRAN is considered to be the first widely used programming language supported across a variety of computer architectures. Significantly, the increasing popularity of FORTRAN spurred competing computer manufacturers to provide FORTRAN compilers for their machines, so that by 1963 over 40 FORTRAN compilers existed. The inclusion of a complex number data type in the language made Fortran especially suited to technical applications such as electrical engineering.Ä«y 1960, versions of FORTRAN were available for the IBM 709, 650, 1620, and 7090 computers. The language was widely adopted by scientists for writing numerically intensive programs, which encouraged compiler writers to produce compilers that could generate faster and more efficient code. I didn't like writing programs, and so, when I was working on the IBM 701 (an early computer), writing programs for computing missile trajectories, I started work on a programming system to make it easier to write programs." ] Said creator John Backus during a 1979 interview with Think, the IBM employee magazine, "Much of my work has come from being lazy. While the community was skeptical that this new method could possibly out-perform hand-coding, it reduced the amount of programming statements necessary to operate a machine by a factor of 20, and quickly gained acceptance. This was an optimizing compiler, because customers were reluctant to use a high-level programming language unless its compiler could generate code whose performance was comparable to that of hand-coded assembly language.

Bisection method is simple, reliable & convergence guaranteed method for finding roots. This article covers pseudocode for bisection method for finding real root of non-linear equations.

In the following table, each line/entry contains the program file nameand a brief description. Click on the program name to display the source code,which can be downloaded. Chapter 1: Introduction first.f90 First programming experiment pi.f90 Simple code to illustrate double precision Chapter 2: Number Representation and Errors oct.f90 Print in octal format hex.f90 Print in hexadecimal format numbers.f90 Print internal machine representation of various numbers xsinx.f90 Example of carefully programming f(x) = x - sinx Chapter 3: Locating Roots of Equations bisection.f90 Bisection method rec_bisection.f90 Recursive version of bisection method newton.f90 Sample Newton method secant.f90 Secant method Chapter 4: Interpolation and Numerical Differentiation coef.f90 Newton interpolation polynomial at equidistant pts deriv.f90 Derivative by center differences/Richardson extrapolation Chapter 5: Numerical Integration sums.f90 Upper/lower sums experiment for an integral trapezoid.f90 Trapezoid rule experiment for an integral romberg.f90 Romberg arrays for three separate functions Chapter 6: More on Numerical Integration rec_simpson.f90 Adaptive scheme for Simpson's rule Chapter 7: Systems of Linear Equations ngauss.f90 Naive Gaussian elimination to solve linear systems gauss.f90 Gaussian elimination with scaled partial pivoting tri.f90 Solves tridiagonal systems penta.f90 Solves pentadiagonal linear systems Chapter 8: More on Systems of Linear Equations Chapter 9: Approximation by Spline Functions spline1.f90 Interpolates table using a first-degree spline function spline3.f90 Natural cubic spline function at equidistant points bspline2.f90 Interpolates table using a quadratic B-spline function schoenberg.f90 Interpolates table using Schoenberg's process Chapter 10: Ordinary Differential Equations euler.f90 Euler's method for solving an ODE taylor.f90 Taylor series method (order 4) for solving an ODE rk4.f90 Runge-Kutta method (order 4) for solving an IVP rk45.f90 Runge-Kutta-Fehlberg method for solving an IVP rk45ad.f90 Adaptive Runge-Kutta-Fehlberg method Chapter 11: Systems of Ordinary Differential Equations taylorsys1.f90 Taylor series method (order 4) for systems of ODEs taylorsys2.f90 Taylor series method (order 4) for systems of ODEs rk4sys.f90 Runge-Kutta method (order 4) for systems of ODEs amrk.f90 Adams-Moulton method for systems of ODEs amrkad.f90 Adaptive Adams-Moulton method for systems of ODEs Chapter 12: Smoothing of Data and the Method of Least Squares Chapter 13: Monte Carlo Methods and Simulation test_random.f90 Example to compute, store, and print random numbers coarse_check.f90 Coarse check on the random-number generator double_integral.f90 Volume of a complicated 3D region by Monte Carlo volume_region.f90 Numerical value of integral over a 2D disk by Monte Carlo cone.f90 Ice cream cone example loaded_die.f90 Loaded die problem simulation birthday.f90 Birthday problem simulation needle.f90 Buffon's needle problem simulation two_die.f90 Two dice problem simulation shielding.f90 Neutron shielding problem simulation Chapter 14: Boundary Value Problems for Ordinary Differential Equations bvp1.f90 Boundary value problem solved by discretization technique bvp2.f90 Boundary value problem solved by shooting method Chapter 15: Partial Differential Equations parabolic1.f90 Parabolic partial differential equation problem parabolic2.f90 Parabolic PDE problem solved by Crank-Nicolson method hyperbolic.f90 Hyperbolic PDE problem solved by discretization seidel.f90 Elliptic PDE solved by discretization/ Gauss-Seidel method Chapter 16: Minimization of Functions Chapter 17: Linear Programming Addditional programs can be found at the textbook's anonymous ftp site:

In the following table, each line/entry contains the program file name, the page number where it can be found in the textbook, and a brief description. Click on the program name to display the source code,which can be downloaded. Chapter 1: Introduction first.f90 6-7 First programming experiment pi.f90 8 Simple code to illustrate double precision Chapter 2: Number Representation and Errors oct.f90 49 Print in octal format hex.f90 50 Print in hexadecimal format numbers.f90 60-61 Print internal machine representation of various numbers xsinx.f90 77-79 Example of carefully programming f(x) = x - sinx Chapter 3: Locating Roots of Equations bisection.f90 94-95 Bisection method rec_bisection.f90 95-96 Recursive version of bisection method newton.f90 106-107 Sample Newton method secant.f90 127-128 Secant method Chapter 4: Interpolation and Numerical Differentiation coef.f90 152-155 Newton interpolation polynomial at equidistant pts deriv.f90 185-186 Derivative by center differences/Richardson extrapolation Chapter 5: Numerical Integration sums.f90 200 Upper/lower sums experiment for an integral trapezoid.f90 207 Trapezoid rule experiment for an integral romberg.f90 223-224 Romberg arrays for three separate functions Chapter 6: More on Numerical Integration rec_simpson.f90 241 Adaptive scheme for Simpson's rule Chapter 7: Systems of Linear Equations ngauss.f90 270-271 Naive Gaussian elimination to solve linear systems gauss.f90 285-287 Gaussian elimination with scaled partial pivoting tri.f90 301-302 Solves tridiagonal systems penta.f90 304 Solves pentadiagonal linear systems Chapter 8: More on Systems of Linear Equations Chapter 9: Approximation by Spline Functions spline1.f90 385 Interpolates table using a first-degree spline function spline3.f90 404-406 Natural cubic spline function at equidistant points bspline2.f90 427-428 Interpolates table using a quadratic B-spline function schoenberg.f90 430-431 Interpolates table using Schoenberg's process Chapter 10: Ordinary Differential Equations euler.f90 448-449 Euler's method for solving an ODE taylor.f90 451 Taylor series method (order 4) for solving an ODE rk4.f90 462-463 Runge-Kutta method (order 4) for solving an IVP rk45.f90 472-473 Runge-Kutta-Fehlberg method for solving an IVP rk45ad.f90 474 Adaptive Runge-Kutta-Fehlberg method Chapter 11: Systems of Ordinary Differential Equations taylorsys1.f90 489-490 Taylor series method (order 4) for systems of ODEs taylorsys2.f90 491 Taylor series method (order 4) for systems of ODEs rk4sys.f90 491-493,496 Runge-Kutta method (order 4) for systems of ODEs amrk.f90 510-513 Adams-Moulton method for systems of ODEs amrkad.f90 513 Adaptive Adams-Moulton method for systems of ODEs Chapter 12: Smoothing of Data and the Method of Least Squares Chapter 13: Mont