| aws/aws-eks-best-practices |
1,738 |
|
0 |
0 |
about 2 years ago |
0 |
|
87 |
other |
Python |
| A best practices guide for day 2 operations, including operational excellence, security, reliability, performance efficiency, and cost optimization. |
| argmin-rs/argmin |
820 |
|
0 |
14 |
about 2 years ago |
44 |
February 20, 2023 |
26 |
apache-2.0 |
Rust |
| Numerical optimization in pure Rust |
| helderpinto/AzureOptimizationEngine |
317 |
|
0 |
0 |
about 2 years ago |
0 |
|
4 |
mit |
PowerShell |
| The Azure Optimization Engine is an extensible solution designed to generate optimization recommendations for your Azure environment. See it like a fully customizable Azure Advisor. |
| joshlk/k-means-constrained |
164 |
|
6 |
12 |
over 2 years ago |
17 |
November 26, 2025 |
3 |
bsd-3-clause |
Jupyter Notebook |
| K-Means clustering - constrained with minimum and maximum cluster size. Documentation: https://joshlk.github.io/k-means-constrained |
| sethjuarez/DeepLearningWithPyTorch |
76 |
|
0 |
0 |
almost 6 years ago |
0 |
|
0 |
mit |
Jupyter Notebook |
| ekhoda/optimization-tutorial |
34 |
|
0 |
0 |
almost 5 years ago |
0 |
|
0 |
mit |
Python |
| Simple framework for modeling optimization problems in Python |
| Gnimuc/Hungarian.jl |
32 |
|
0 |
0 |
about 3 years ago |
0 |
|
1 |
unlicense |
Julia |
| The Hungarian(Kuhn-Munkres) algorithm for Julia |
| VicentePerezSoloviev/EDAspy |
30 |
|
0 |
0 |
over 2 years ago |
11 |
September 29, 2023 |
0 |
mit |
Jupyter Notebook |
| Estimation of Distribution algorithms Python package |
| Bihaqo/tf-memonger |
27 |
|
0 |
0 |
almost 10 years ago |
0 |
|
0 |
|
Jupyter Notebook |
| Sublinear memory optimization for deep learning, reduce GPU memory cost to train deeper nets |
| sduprey/optimal_transaction_execution |
24 |
|
0 |
0 |
about 12 years ago |
0 |
|
0 |
|
Matlab |
| This entry contains two topics The first item is entirely based on the following paper: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2011-056.pdf It contains 2 MATLAB demonstrating script : DATA_preprocessing.m & VAR_modeling_script.m DATA_preprocessing.m uses the LOBSTER framework (https://lobster.wiwi.hu-berlin.de/) to preprocess high frequency data from the NASDAQ Total View ITCH (csv files) allowing us to reconstruct exactly at each time the order book up to ten depths. Just look at the published script ! VAR_modeling_script.m contains the modeling of the whole order book as VEC/VAR process. It uses the great VAR/VEC Joahnsen cointegration framework. After calibrating your VAR model, you then assess the impact of an order using shock scenario (sensitivity analysis) to the VAR process. We deal with 3 scenarii : normal limit order, aggressive limit order & normal market order). Play section by section the script (to open up figures which contain a lot of graphs). It contains a power point to help you present this complex topic. The second item is entirely based on the following paper : http://www.courant.nyu.edu/~almgren/papers/optliq.pdf It contains a mupad document : symbolic_demo.mn I did struggle to get something nice with the symbolic toolbox. I was not able to drive a continuous workflow and had to recode some equations myself. I nevertheless managed to get a closed form solution for the simplified linear cost model. It contains a MATLAB demonstrating script : working_script.m For more sophisticated cost model, there is no more closed form and we there highlighted MATLAB numerical optimization abilities (fmincon). It contains an Optimization Apps you can install. Just launch the optimization with the default parameters. And then switch the slider between volatility risk and liquidation costs to see the trading strategies evolve on the efficient frontier. It contains a power point to help you present this complex topic. |
