Пікірлер

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  @kamalnathkadirvel269115 сағат бұрын

  14:05Multiple input elements

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  @user-nd7zh6gy6b20 сағат бұрын

  It's interesting for novice in the field, I thought you would present multiple objectives convex optimization with non standards form of objective functions.

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  @PaulIvanov314Күн бұрын

  54:15 is where you get your waffles!

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  @MahmoudMohamed-lo7oz6 күн бұрын

  😊

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  @thenerdguy998511 күн бұрын

  Great Talk

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  @caine702412 күн бұрын

  very useful tool. it's such a freaking pain to merge jupyter notebooks D:

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  @wolpumba409916 күн бұрын

  *Summary* *Overall Tutorial:* * *Focus (**0:03**):* Practical application and problem-solving for numerical optimization using Python libraries. * *Libraries:* `scipy.optimize`, `estimagic`, and `jaxopt`. * *Exercises (**0:36**):* Hands-on Jupyter notebooks with examples of common optimization issues and their solutions. * *Prerequisites:* Basic Python, NumPy, and function definition knowledge. *Library Breakdown:* * *`scipy.optimize` (**0:03**):* * Simple, mature, and reliable starting point. * Provides access to 14 local optimizers suitable for various optimization problems. * Parameters are 1D NumPy arrays. * Lacks features like parallelization, interactive feedback, and flexible parameter representation. * *`estimagic` (**0:31**):* * Built on top of `scipy.optimize` and other libraries (`nlopt`, `tao`, `pygmo`, etc.), providing a harmonized interface. * Offers a wider range of optimizers and advanced features, including: * Flexible parameter representation using dictionaries, Pandas Series/DataFrames, and nested structures. * Interactive dashboard, logging, and visualization tools. * Built-in scaling and constraint handling mechanisms. * Support for global optimization techniques. * Emphasizes informed algorithm choice and robust convergence assessment. * *`jaxopt` (**0:47**):* * Utilizes JAX for automatic differentiation, JIT compilation, and GPU acceleration. * Provides differentiable optimizers, enabling gradient-based approaches with high precision and speed. * Excels in solving many instances of similar optimization problems efficiently through vectorization. *Key Concepts and Exercise Highlights:* * *Criterion Functions (**3:28**):* Defining optimization targets as Python functions. * *Start Parameters (**5:26**):* Understanding their importance and setting them appropriately. * *Algorithm Selection (**30:11**):* Choosing the right algorithm based on function properties (differentiability, complexity, constraints, size). Exercises involve identifying and fixing optimization failures by switching algorithms. * *Scaling (**40:22**):* Recognizing the impact of poorly scaled problems and using `estimagic`'s scaling capabilities to improve performance. Visualizing scaling issues with slice plots. * *Benchmarking (**53:32**):* Comparing optimizer performance on a set of benchmark problems with known optima. Utilizing profile plots and convergence plots for analysis. * *Bounds and Constraints (**1:08:48**):* Using bounds, fixed parameters, and linear constraints to define the optimization problem. Exercises involve implementing these constraints in `estimagic`. * *Automatic Differentiation (**49:59**):* Employing JAX to calculate gradients efficiently and accurately. Implementing JAX gradients within `estimagic`. * *Global Optimization (**1:29:49**):* Briefly introducing techniques like genetic algorithms, Bayesian optimization, and multi-start optimization. * *Vectorization with JAX (**2:00:19**):* Utilizing `jaxopt` and the `vmap` function transformation to solve multiple optimization problems concurrently. *Exercise Breakdown with Timestamps:* *Exercise 1: First Optimization with `scipy.optimize` (**6:57**):* * *Goal:* Familiarize yourself with basic optimization in Python using `scipy.optimize.minimize`. * *Task:* * Translate a mathematical criterion function (a function of multiple variables to be minimized) into Python code. * Set up starting parameters for the optimization. * Use `scipy.optimize.minimize` to find the minimum of the function. * *Key Takeaway:* You learn the essential steps involved in setting up and solving a basic optimization problem in Python. *Exercise 2: Convert Previous Example to `estimagic` (**13:45**):* * *Goal:* Experience the advantages of `estimagic`'s interface and features. * *Task:* * Convert the criterion function and starting parameters from Exercise 1 to work with `estimagic.minimize`. * Use dictionaries instead of flat arrays to represent parameters, taking advantage of `estimagic`'s flexibility. * Plot the optimization history using `estimagic`'s built-in plotting functions (`criterion_plot` and `params_plot`) to visualize the optimization process. * *Key Takeaway:* You become comfortable with `estimagic`'s syntax, understand how to represent parameters flexibly, and learn to use visualization tools for analyzing optimization runs. *Exercise 3: Play with Algorithm and `algo_options` (**30:26**):* * *Goal:* Develop an intuition for choosing appropriate algorithms and understanding their impact on optimization success. * *Task:* * You receive code snippets for two optimization problems, each with a pre-selected algorithm that *appears* to succeed but produces incorrect results. * Analyze the criterion functions to understand why the initial algorithm choice fails. * Choose a different algorithm (and potentially fine-tune `algo_options`) that successfully finds the true minimum. * *Key Takeaway:* You gain a deeper understanding of the strengths and weaknesses of different optimization algorithms and learn how to diagnose and address optimization failures caused by inappropriate algorithm choices. *Exercise 4: Benchmarking Optimizers (**54:53**):* * *Goal:* Learn to systematically compare optimizers and understand their relative performance on different types of problems. * *Task:* * Use `estimagic`'s benchmarking tools to run a set of benchmark problems with various optimizers. * Visualize the results using profile plots (showing the share of problems solved over the number of function evaluations) and convergence plots (detailing the convergence paths for individual problems). * Compare different implementations of the Nelder-Mead algorithm to see how implementation details can affect performance. * *Key Takeaway:* You gain experience with benchmarking optimizers, understand how to interpret benchmark results, and learn to appreciate the importance of choosing well-implemented algorithms. *Exercise 5: Constrained Optimization (**1:24:31**):* * *Goal:* Implement bounds and constraints to define a more realistic optimization problem. * *Task:* * Use `estimagic`'s constraint handling features to: * Set upper and lower bounds on specific parameters. * Fix certain parameters at their starting values. * Implement a linear constraint on the average of a subset of parameters. * Solve the constrained optimization problem and compare the results to the unconstrained case. * *Key Takeaway:* You learn to define and solve constrained optimization problems in `estimagic` and understand the impact of constraints on the solution. *Exercise 6: Scaling of Optimization Problems (timestamp not available):* * *Goal:* Visualize and address the challenges posed by poorly scaled optimization problems. * *Task:* * Work with a badly scaled benchmark problem. * Use `estimagic`'s `slice_plot` function to visualize the sensitivity of the criterion function to changes in each parameter. * Run the optimization with and without scaling (`scaling=True` in `estimagic.minimize`) and compare the results using a criterion plot. * *Key Takeaway:* You understand the concept of scaling in optimization, learn to recognize scaling issues through visualization, and experience how `estimagic`'s scaling feature can significantly improve optimizer performance. *Exercise 7: Using JAX Derivatives in `estimagic` (**1:53:58**):* * *Goal:* Integrate JAX's automatic differentiation capabilities into `estimagic` for faster and more precise gradients. * *Task:* * Translate a criterion function to use JAX arrays (`jnp`). * Compute the gradient of the function using `jax.grad` and optionally JIT-compile it for further speedup. * Solve the optimization problem using `estimagic.minimize`, passing the JAX gradient as the `derivative` argument. * *Key Takeaway:* You learn to combine the strengths of `estimagic` and JAX, demonstrating how automatic differentiation can be seamlessly integrated to enhance optimization performance. *Exercise 8: Vectorized Optimization in `jaxopt` (**2:00:19**) (Optional):* * *Goal:* Explore `jaxopt`'s capabilities for solving multiple instances of the same optimization problem concurrently. * *Task:* * Define a wrapper function that encapsulates the `jaxopt` optimization process for a single problem instance. * Use JAX's `vmap` function transformation to vectorize the wrapper function, enabling it to handle batches of problems. * Solve a set of problems with varying parameters efficiently using the vectorized solver. * *Key Takeaway:* You gain exposure to `jaxopt` and understand how to leverage JAX's vectorization features for situations where you need to solve many similar optimization problems. These exercises offer a comprehensive, hands-on approach to learning practical numerical optimization in Python, covering a wide range of topics from basic problem setup to advanced techniques using JAX and `jaxopt`. They are designed to build your intuition, problem-solving skills, and confidence in tackling real-world optimization challenges. i used gemini 1.5 pro to summarize the transcript

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  @salimtlemcani412219 күн бұрын

  Awesome presentation! Please do you provide the solution of the exercises?

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  @user-tg2gm1ih9g22 күн бұрын

  the mere fact that everyone and his brother is working on a way to speed up python makes it *crystal* clear that python is unusable except for throw-away prototypes or maybe a bignums calculator.

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  @kisho267924 күн бұрын

  how can an external LaTex file be called/included/embedded in a JupyterLab cell?

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  @ravindarmadishetty73628 күн бұрын

  Such a nice presentation on plotly. In one area while visual to code i am unable to see any response where fig.show('json') at 12.53.....root, data.....etc. Please help me if it is required to avail anything.

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  @mohammadgaeini450028 күн бұрын

  It was very helpful. Thank you.

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  @JJayaraj-fs1diАй бұрын

  Hi. Im having a normal python dictionary which has values a list of numbers. Is there a way to convert this dictionary into a numba typed dictionary?

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  @JoseCostas-nd8npАй бұрын

  please remove this NOISE; no way to hear such poor quality.

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  @kcvinuАй бұрын

  I tested cython with python version 3.11. I just created a GUI library with cython and it seems a nice language and nice bridge between C and Python. But surprisingly "ctypes" ran faster than cython. Yes! I wrote the same GUI lib in Odin & C3. Then called the functions from python with ctypes. It was 2.5 times faster than cython. Both Odin & C3 are newer languages with manual memory management. Both are aiming to be alternatives to C. Due to this performance diff, I checked my cython code again and again. I realized that type conversion takes more time in Cython. But ctypes module in CPython 311 is marvelous.

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  @mmorpeАй бұрын

  Thanks, this was fast. Little difficult to for newcomers, but was great.

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  @rithanyabalamurali69362 ай бұрын

  how to install mayavi? ,it always shows file not responding!!!

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  @maximecros40902 ай бұрын

  Can you implement your own manifold with geomstats ? then customize your metric and connexion or is it not possible ?

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  @xmurisfurderx2 ай бұрын

  why isn't there some kind of goddamn ISO standard already

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  @zapy4222 ай бұрын

  how are the dependencies managed ?

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  @user-mv3im2fi4f2 ай бұрын

  Comecei sem entender nada, terminei sem entender nada e mais um pouco

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  @Rohull-123452 ай бұрын

  Very good❤❤❤🎉🎉🎉🎉

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  @andresfelipehiguera7852 ай бұрын

  Easy. Desinstall Matlab, and use Python.

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  @AdityaBhoite-vj1lb3 ай бұрын

  can you guys provide the materials for this tutorial, the link in the description is not working

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  @MrMate123453 ай бұрын

  Didn't expected to understand the (i know that very very) basics of gene expression from a NumPy tutorial. Thank you.

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  @code2compass3 ай бұрын

  Ahhh such a polite teacher and the way she talk abd explain. OMG she and people like her are really a gift to our society. Stay safe, keep teaching and keep smiling. thank you

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  @convexset3 ай бұрын

  Useful.

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  @sandipdas72063 ай бұрын

  Is nobody gonna talk about how swiftly he switched from Mac to Windows

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  @diodin85874 ай бұрын

  22:06 pybind11

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  @codejunkes46074 ай бұрын

  40 degrees C, where are you?

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  @mmmhorsesteaks4 ай бұрын

  "the posterior is what we're interested in" - brother knows what's up!

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  @AkashKumar-lr6hc4 ай бұрын

  thanks for the presentation

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  @YesSirPi3144 ай бұрын

  Government using python notebooks

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  @AndrewDArcyEvans4 ай бұрын

  Potentially such an interesting talk. Do you have a link without the masks and with better sound?

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  @bephrem5 ай бұрын

  great talk

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  @spinj33075 ай бұрын

  How is this library installed i've really never successfully installed it in all platforms that i have used

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  @pyajudeme92455 ай бұрын

  Awesome!

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  @mattpopovich5 ай бұрын

  This is a very impressive demonstration. I wish it was recorded at a higher resolution. Thank you for sharing.

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  @MaskedPixel5 ай бұрын

  Yup. Using this one tomorrow.

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  @alerdoballabani83225 ай бұрын

  Very good explanation, Unfortunately I can not access the study material.

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  @piotr7805 ай бұрын

  Tfx is useless junk😂

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  @pyajudeme92455 ай бұрын

  Best project ever! Keep on!

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  @flowy-moe5 ай бұрын

  Would someone be able to share the Jupyter Notebooks? The link in the description is not working for me ...

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  @stevehageman67855 ай бұрын

  well done talk, thanks! :-)

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  @affable.pebble6 ай бұрын

  thank you for this concise explanation!

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  @djangoworldwide79256 ай бұрын

  Coming from R I was like dang

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  @forheuristiclifeksh78366 ай бұрын

  2:17:20

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  @forheuristiclifeksh78366 ай бұрын

  2:01:17

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  @forheuristiclifeksh78366 ай бұрын

  52:27

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  @forheuristiclifeksh78366 ай бұрын

  1:03