pip install -r requirements.txt
to install dependencies.
- RootFinding
- Bracketing (simple)
- Bisection Method
- False Position Method
- Open (faster, but may diverge or cycle)
- Fixed Point Iteration
- Newton-Raphson Method (needs f and f') $ x_i+1 = x_i + f(x_i)/f'(x_i) $
- Important Points: Newton’s method is useful in cases of large values of f’(x) i.e. when the graph of f(x) while crossing the x-axis is nearly vertical. May converge slowly.
- It is not preferred when the graph of f(x) is nearly horizontal where it crosses the x-axis as the values of f’(x) have negative values in this case.
- It is sensitive to starting value.
- Convergence fails if the starting point is nor near the root.
- The formula converges provided the initial approximation x0 is chosen sufficiently close to the root.
- It is generally used to improve the result obtained by the other methods.
- It has quadratic convergence i.e. order of convergence is 2. The subsequent error at each step is proportional to the square of the error at the previous step.
- Modified Newton-Raphson Method $ x_i+1 = x_i + f(x_i)f'(x_i) / ( [f'(x_i)]^2 -f(x_i)f''(x_i) ) $
- Secant Method (needs 2 points) $ x_i+1 = x_i + u(x_i)/u(x_i) $
- Modified Secant Method $ x_i+1 = x_i - u(x_i)(x_i-1 + x_i)/(u(x_i-1) - u(x_i)) $
- Inverse Quadratic Interpolation (needs 3 points)
- Graphical (for debug)
- Bracketing (simple)
- LinearAlgebra
- CurveFitting
- Differentiation
- Integration
- Optimization
- Convex
- Nonlinear
- more
- ODE
- PDE
- Modeling Error
- Formulation Error
- Data Uncertainty
- *Blunders
- Numerical Error
- Truncation Error
- Taylor Series
- Roundoff Error
- Quantization Error
- Numerical Manipulations
- Adding large and small numbers
- Subtractive cancellation
- Truncation Error