Interested in optimization for scientific computing, where to start?

In summary, the conversation discusses the use of different programming languages and how they are translated into machine code. It also mentions the benefits of learning a low-level language, specifically C, to better understand the underlying processes and improve performance in scientific computing. Other recommended languages for scientific computing include C++, Fortran, and Julia.
  • #1
rem1618
14
0
I've just started programming with Python this summer, and I'm taking a course in computational physics this semester. I've been really enjoying it and programming in general, but I don't have much knowledge in computer science save for intro stuff (string/list methods, functions/classes, precision digits, a bit of dynamic programming), and I'm interested in learning more. Basically, I don't even know what I don't know. For example, what should I look into in order to understand something like this?

(top answer)
http://stackoverflow.com/questions/...-slower-than-builtin-in-arithmetic-operations
 
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  • #2
You should know that there are different programming languages (bummer!).

A computer doesn't directly understand those strings of commands you're giving him, it's not smart enough, and for that you need another program(s) that translates those strings into something the computer understands, what we call machine code. For some languages you only need one program or two to get from human code to machine code, we call these languages "low-level languages", because they're closer to the string of 1's and 0's. For others you need several jumps, and we call them "high-level languages". As a result of those jumps, high-level languages tend to produce code that's easier to understand/write while being slower overall, while low-level languages tend to be the opposite.

Where does Python come? Python is a high-level language, and most of the under-the-hood stuff is hidden from the coder unless you use specific libraries. Those stuff are taken care-of for you by a translating program that's called an "interpreter", and you won't need to understand all those "blocks" and "bytes" stuff to code in Python at all!

However, while many Python coders don't even try to understand those, I think everyone should, as sometimes the interpreter (which gives you the errors) gives you weird low-level unintelligible errors, and you need to understand them to use the Python scientific libraries (numpy,scipy...etc) to their fullest.Which is why I recommend you learn a low-level language, and I recommend you learn C specifically. (fun fact: Python is made using C! And that stackoverflow question is about CPython, a Python library for calling pseudo-C code into Python) You shouldn't really "learn" it, you're not supposed to write everything you do in Python in C, just learn how things work. Learning C is by far the best way to learn how low-level stuff works, and it will enable you to write faster code in Numpy once you understand how computer memory works.

Also you might want to properly learn a low-level language for scientific computing, since you've probably noticed that Python is slow, while C is great, it's not at all user-friendly and it lacks scientific libraries as it's intended for use for CS specialists. And here I can recommend C++ (C with object oriented stuff), Fortran (I hate it, but it's incredible as it's intended for scientists) or Julia ( a "high-level" language with low-level performance, a bit biased here since I develop libraries in it!).
 

1. What is optimization in scientific computing?

Optimization in scientific computing is the process of finding the best possible solution to a problem by minimizing or maximizing a certain objective function, subject to a set of constraints. It involves using mathematical and computational methods to efficiently and accurately solve complex problems in various fields of science, such as physics, engineering, and biology.

2. Why is optimization important in scientific computing?

Optimization is important in scientific computing because it allows us to find optimal solutions to complex problems that would be difficult or impossible to solve using traditional methods. It also helps us to make more accurate predictions and decisions in various scientific fields, leading to advancements in technology, medicine, and other areas of research.

3. Where can I learn about optimization for scientific computing?

There are many resources available for learning about optimization in scientific computing. Some options include online courses, textbooks, and academic journals. You can also attend conferences and workshops on the topic, or seek guidance from experts in the field.

4. What are some common techniques used in optimization for scientific computing?

Some common techniques used in optimization for scientific computing include linear programming, gradient descent, genetic algorithms, and simulated annealing. Other methods such as convex optimization, nonlinear programming, and stochastic optimization are also commonly used, depending on the specific problem being solved.

5. How can I apply optimization in my own scientific research?

To apply optimization in your own scientific research, it is important to first identify the problem you want to solve and the objectives and constraints involved. Then, you can select the appropriate optimization technique and use programming languages and software tools to implement and solve the problem. It is also helpful to consult with experts and keep up-to-date with the latest developments in the field for optimal results.

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