A simple and easy-to-use CLI application that can be used to solved optimization problems!

Currently supports the following optimization techniques

• Newton Raphson
• Secant Method
• Fibonacci Search
• Golden Section Search
• Cauchy Gradient
• Lagrange Interpolation
• Least Squares
• Downhill Simplex More to come

We like math! 🤓

• Clone this repository.
• Run pip install -r requirements.txt.
• Run the main with relevant options and arguments.

Arguments type The value that specifies the optimization problem type Accepted Types: SECANT 'sec' NEWTON 'newt' FIBONACCI 'fib' GOLDEN SECTION 'GS' DOWNHILL SIMPLEX 'DS' LAGRANGE INTERPOLATION 'lagin' LEAST SQUARE 'ls' CAUCHY GRADIENT 'CG' Options: -f, --func TEXT Function to be optimized (Enter the function in python style syntax). -x1, --first FLOAT Leftmost of an interval (or) initial guess -x2, --second FLOAT Rightmost of an interval -x3, --third FLOAT Third value of x (applies only for lagrange interpolation) -i, --iter INTEGER Number of iterations -e, --epsilon FLOAT Epsilon (by default 10^-3) --help Shows the help message 

$ python3 main.py newt -f "x**2 - 2" -x1 1 -i 10 >>> 1.414213562373095 

• It's not necessary for you to know the concepts to use these calculators, however it is recommended that you do.
• This calculator was tested with various questions and the answers were pretty accurate (a lot of anomolies are to be expected).
• Enter the function in python style syntax (eg: x**2 + 2*x + 3 instead of x^2 + 2x + 3).

• Re-check your input function (the syntax expected is slightly different -> updates).
• Check the input values you've fed.
• If you function string has spaces, try enclosing it in quotes.
• See if your answer was obtained a few iterations prior.

• Moved to click for better CLI experience.
• Revamped the whole structure entirely.

• Feel free to open issues/PR's for any bugs or improvements.
• If you have any suggestions (new methods) feel free to open an issue regarding that!