! BISECTION METHOD
WRITE(*,*)'VALUES OF X1,X2'
READ (*,*)X1,X2
WRITE(*,*)'ENTER ERROR YOU WANT'
READ(*,*)ER
FX1=F(X1)
WRITE(*,10)FX1
FX2=F(X2)
WRITE(*,10)FX2
10 FORMAT(F8.3)
FP=FX1*FX2
IF (FP.GT.0) GO TO 100
15 X3=(X1+X2)/2
FX3=F(X3)
IF (ABS(FX3).LE.ER) THEN
WRITE (*,20)X3
20 FORMAT(5X,'THE ROOT IS='F8.4)
WRITE(*,30)FX3
30 FORMAT(5X,'THE VALUE OF FUNCTION AT ROOT',F8.4)
GO TO 200
ELSE
FP=FX1*FX3
WRITE(*,*)FP
IF (FP.LT.0)THEN
X2=X3
FX2=FX3
GO TO 15
ELSE
X1=X3
FX1=FX3
GO TO 15
ENDIF
ENDIF
GO TO 200
100 WRITE(*,*)'THE ROOT DOES NOT LIE IN THE INTERVAL'
200 STOP
END
FUNCTION F(X)
F=X**3-5*X+3
RETURN
END

The bisection method is a regular way of ﬁnding numbers aand bas close together as you want (within the limits of machine accuracy) bracketing the solution of a given equation f(x)=0.Onceyouhaveaand byou can say that.

Bisection Method Code In Fortran

This fortran 90 program implements Bisection method to find the root - bisectionwithoutdoloop.f90.
FORTRAN 77 Code. Old Mathematica Code (c). May 20, 2003 - xsinx.f, 77-79, Example of programming f(x) = x - sinx carefully. Chapter 3: Locating Roots of Equations. Bisect1.f, 94-95, Bisection method. Simple C Program to implement the bisection method to find roots in C language with stepwise explanation and solution.
The Bisection Method, also called the interval halving method, the binary search method, or the dichotomy method. Is based on the Bolzano’s theorem for continuous functions. Theorem (Bolzano): If a function f(x) is continuous on an interval a, b and f(a)f(b).

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. Fortran

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: Monte Carlo Methods and Simulation
test_random.f90	562-563	Example to compute, store, and print random numbers
coarse_check.f90	564	Coarse check on the random-number generator
double_integral.f90	574-575	Volume of a complicated 3D region by Monte Carlo
volume_region.f90	575-576	Numerical value of integral over a 2D disk by Monte Carlo
cone.f90	576-577	Ice cream cone example
loaded_die.f90	581	Loaded die problem simulation
birthday.f90	583	Birthday problem simulation
needle.f90	584	Buffon's needle problem simulation
two_die.f90	585	Two dice problem simulation
shielding.f90	586-587	Neutron shielding problem simulation
Chapter 14: Boundary Value Problems for Ordinary Differential Equations
bvp1.f90	602-603	Boundary value problem solved by discretization technique
bvp2.f90	605-606	Boundary value problem solved by shooting method
Chapter 15: Partial Differential Equations
parabolic1.f90	618-619	Parabolic partial differential equation problem
parabolic2.f90	620-621	Parabolic PDE problem solved by Crank-Nicolson method
hyperbolic.f90	633-634	Hyperbolic PDE problem solved by discretization
seidel.f90	642-645	Elliptic PDE solved by discretization/ Gauss-Seidel method
Chapter 16:

Bisection Method Fortran