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License: MIT License
Optimization in ML
License: MIT License
References: multidimensionaloptimization.pdf
Conceptual model rst file ConceptualModel.rst. Describe the situation in which we work, e.g. stock exange or healthcare
Mathematical model rst file MathematicalModel.rst. Describe losses and motivation or l1 and l2.
(L2 - https://en.wikipedia.org/wiki/Tikhonov_regularization,
L1 - https://en.wikipedia.org/wiki/Lasso_(statistics))
Requirements rst file SolutionRequirements.rst. A very short part. About half a sheet
Sphinx deploy sphinx-step-by-step.txt
Formatting all files for latex. And create documentation in pdf format
Some features, such as gradient, are common to different projects.
Refactoring the project for a clear import.
We have three points for each iteration. Therefore, we can build a parabolic equation and draw a parabola for each iteration
Input data format: a table is given, where the column headers are passed in the first row. Indexes are missing
Conceptual model rst file. Describe the situation in which we work
A fully mathematical analysis with latex/rst records
Algorithms devs
Other files:
Input
visualiztion
Formatting all files for latex. And create documentation in pdf format
For each file below need to add comments.
make requirements.txt files
Comments must be written by pep and be fully understandable
Is subtask of Streamlit. 1d algorithms
Making the page
Problem:
f(x) -> *extremum* on [a, b]
a
, b
, e
(e - error/accuracy)x1 = b - (b - a) / Ф
,x2 = a + (b - a) / Ф
f(x1) > f(x2)
(for min
) | if f(x1) > f(x2)
(for max
) then a = x1
b = x2
|a - b| > e
Ф = (1 + 5 ** 0.5) / 2
For one dimensional unputs make
reference: https://scikit-learn.org/stable/modules/linear_model.html#ordinary-least-squares
Let's make THETA = [THETA0, *THETA1] is m dimensional vector-column.
And try:
Made on GO
https://www.cs.cmu.edu/~pradeepr/convexopt/Lecture_Slides/primal-dual.pdf (Primal-Dual Interior-Point Methods)
https://www.stat.cmu.edu/~ryantibs/convexopt-S15/scribes/15-barr-method-scribed.pdf ( Log Barrier Method)
https://www.cs.cmu.edu/~ggordon/10725-F12/scribes/10725_Lecture12.pdf (Newton’s method under equality constrains)
2022-05-0
5 10:26:21.678 Uncaught app exception
Traceback (most recent call last):
File "/home/appuser/venv/lib/python3.9/site-packages/streamlit/scriptrunner/script_runner.py", line 443, in _run_script
exec(code, module.__dict__)
File "/app/optimization_ml/Inner_point/streamlit_app.py", line 101, in
solution, history = bound_constrained_lagrangian_method(function.call, x0, constraints, max_iter=100)
File "/app/optimization_ml/Inner_point/algorithms.py", line 87, in bound_constrained_lagrangian_method
constraints[i](x0)
File "/app/optimization_ml/StreamlitSupport/functions.py", line 39, in function_callable
return function_callable_initial(*x)
TypeError: _lambdifygenerated() takes 2 positional arguments but 4 were given
Using documentation multidimensionaloptimization.pdf page 6, algorithm 2.4.1 make new method.
Using sympy https://docs.sympy.org/latest/modules/parsing.html create a raw data parser from a string.
This list will be updated
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