Streamlit app

[vktrbr]

Required input fields

• [x] Type of algorithm. (Gradient with constant step, fractional step, optimal step, nonlinear conjugate gradient method )
• [x] Field with function input 𝑓(x)
• [x] Field with the analytical gradient flag. If analytical gradient flag is True then request the gradient in analytical form. ∇𝑓(x)
• [x] Field with the start point (The default value is random numbers from a uniform distribution on (-1, 1))
• [x] Search precision 𝜀
• [x] Field with a history saving flag
• [x] Required parameters for each method

Output:

• [x] Found point
• [x] plots

[vktrbr]

analytical gradient cancelled history sving cancelled

[vktrbr]

branch: https://github.com/vktrbr/optimization_ml/tree/41-streamlit-app streamlit_app.py