Cocalc 01 Introduction Ipynb

📚 The CoCalc Library - books, templates and other resources Overview 01: Linear Programming and Pyomo formulate a linear program for a given problem scenario. determine if the four assumptions of linear program are satisfied. identify corner point feasible solutions, optimal solutions, the feasible region, and feasible solutions for linear programs with either bounded or unbounded feasible regions. solve a small linear program graphically.

In this notebook we will deal mainly with syntax and semantics in Python we explore some basic expressions in Python we introduce some of the most important control flow statements This is a Jupyter notebook. It is an interface allowing us to combine code (in this case Python) and formatted text in a unified way. The basic unit in a notebook is a cell.

You are right now reading the content of a "Markdown" cell, designed to input formatted text. There are also 'Code' cells, designed to input executable code. This tutorial should take at most 3-4 hours to fully work through. You can read it in HTML or PDF versions, or from the Sage notebook click Help, then click Tutorial to interactively work through the tutorial from within Sage. Though much of Sage is implemented using Python, no Python background is needed to read this tutorial. You will want to learn Python (a very fun language!) at some point, and there are many excellent free resources for doing so: the Python Beginner’s Guide lists many options.

If you just want to quickly try out Sage, this tutorial is the place to start. For example: intermediate level Python starting from a basic programming background basic knowledge in at least one object oriented programming language must know: variables, types, functions, basic I/O handling good to know: objects, classes, attributes, function arguments

Official Github repository: https://github.com/tuw-python/tuw-python-2022WS This notebook comes from A Whirlwind Tour of Python by Jake VanderPlas (OReilly Media, 2016). This content is licensed CC0. The full notebook listing is available at https://github.com/jakevdp/WhirlwindTourOfPython. Conceived in the late 1980s as a teaching and scripting language, Python has since become an essential tool for many programmers, engineers, researchers, and data scientists across academia and industry. As an astronomer focused on building and promoting the free open tools for data-intensive science, I've found Python to be a near-perfect fit for the types of problems I face day to day, whether...

The appeal of Python is in its simplicity and beauty, as well as the convenience of the large ecosystem of domain-specific tools that have been built on top of it. For example, most of the Python code in scientific computing and data science is built around a group of mature and useful packages: NumPy provides efficient storage and computation for multi-dimensional data arrays. SciPy contains a wide array of numerical tools such as numerical integration and interpolation. Python code is usually stored in text files with the file ending ".py": Every line in a Python program file is assumed to be a Python statement, or part thereof.

The only exception is comment lines, which start with the character # (optionally preceded by an arbitrary number of white-space characters, i.e., tabs or spaces). Comment lines are usually ignored by the Python interpreter. To run our Python program from the command line we use: On UNIX systems it is common to define the path to the interpreter on the first line of the program (note that this is a comment line as far as the Python interpreter is... Jupyter notebook Handouts/Handout-01/Handout-01.ipynb Welcome to PHYS 48T!

This is a practical course on data science (DS) and machine learning (ML). We will learn about the ideas underlying the computational tools of these fields, and spend a lot of time applying those tools to various examples. The course will be programming-intensive. There are various programming languages and tools for DS and ML; the most popular are Comes out of the field of statistics; a bit of a quirky language, but very widely used in the DS/ML community, especially among academic statisticians. It is open source, and research ideas are quickly implemented into R packages by volunteers/authors.

We won't use R in this course, but I recommend that you take a quick look at it when you get a chance. A general-purpose programming language that is both highly readable and suitable for large-scale projects. Has a very wide range of packages, not just for DS and ML, but for all sorts of purposes. We will be using Python throughout this course. The study of elliptic curves is an ancient discipline in algebraic geometry. Elliptic curves also play a significant role in number theory, where they constitute a major area of current research.

Many old and new number theoretical problems have been solved with the help of the theory of elliptic curves. For example, Andrew Wiles' proof from 1995 of the famous assertion called Fermat's last theorem, which had remained unproven for over three centuries, makes extensive use of the theory of elliptic curves. Moreover, elliptic curves find applications in integer factorization and cryptography. Elliptic curve cryptography (ECC) was discovered in 1985 by Neal Koblitz and Victor Miller. ECC algorithms are public-key mechanisms that offer the same functionality as RSA schemes. While they provide a level of security similar to that of RSA, they use much shorter keys, allowing faster computations and hence a more efficient use of power, bandwidth and storage.

This is especially useful in applications where these resources are limited, such as smart cards, and in high security environments. [LenstraVerheul2001] shows that in the future, when computation power grows, RSA key lengths will increase at a much faster rate than ECC key lengths. For this reason, most experts (NIST, NSA, BSI) see the future of cryptography in elliptic curves and are recommending the replacement of RSA systems by ECC schemes in high security scenarios. The US National Security Agency (NSA) writes "The best assured group of new public key techniques is built on the arithmetic of elliptic curves. [...] Elliptic curves begin to offer dramatic savings over the old, first generation techniques." They clearly make "a case for moving to elliptic curves as a foundation for future Internet security." (see [NSA ECC])

The goal of this document is to provide a high-level overview as well as a mathematical understanding of elliptic curve cryptography. It covers the basics of elliptic curves relevant for cryptography and the most important mathematical prerequisites, assuming that the reader is familiar with the topics covered in the German Mathe-Abitur. Many references to more detailed explanations of the mathematical prerequisites are provided. It also shows how ECC builds on conventional public key techniques and points out the major advantages, explaining why many see the future of cryptography in ECC. Finally, it provides an overview of ECC protocols, how they may be employed in a safe manner, and some examples of where they are already in use today. The document is meant for anyone interested in how elliptic curve cryptography works and why it is good, and willing to dig into the mathematical and exciting world of ECC.

Understanding part of the content, especially in Chapter 3, requires some effort by the reader; we can't relieve the reader of this task since elliptic curves are naturally a highly mathematical topic. Nevertheless the effort will surely pay off when connections become clear and the understanding of the topic grows. That's the beauty of mathematics: Everything connects back to something else and as we learn more and more, the picture starts to become more complete and we understand things that previously seemed to belong...

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