Optimisasi

2022 :

2021 : Catatan Kuliah MA3071


2021 Workshop Pengenalan Optimisasi Jaringan dengan Python

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  1. Kale Co JakimSeptember 7, 2026 at 8:42 PM

    nice

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  2. Kale Co JakimSeptember 7, 2026 at 8:55 PM

    This collection presents an interesting range of computational optimization topics, from steepest descent and Newton methods to linear programming, network optimization, and scheduling. The combination of mathematical concepts with practical programming examples makes these subjects much easier to connect with real computational problems.

    The focus on vector and matrix operations is particularly useful because they form the foundation for many numerical algorithms. Working with arrays, mathematical expressions, and efficient numerical computations is an important part of applying these concepts in Python, making Numpy Course highly relevant to this material.

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  3. Kale Co JakimSeptember 7, 2026 at 8:55 PM

    The optimization algorithms discussed here also provide a strong introduction to techniques used in machine learning. Methods such as steepest descent, Newton's method, and conjugate gradient demonstrate how mathematical optimization can be applied to find better solutions to computational problems, which connects naturally with a Machine Learning Course.

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  4. Kale Co JakimSeptember 7, 2026 at 8:55 PM

    Overall, the examples covering linear programming, network flow, assignment, and scheduling show how optimization concepts can be translated into practical solutions using Python and R. These ideas provide useful foundations for students developing Machine Learning Projects for Final Year, particularly when numerical computation and optimization are important components of the project.

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