Titlebook: Numerical Python; A Practical Techniqu Robert Johansson Book 20151st edition Robert Johansson 2015 Python.numerical.NumPy.SciPy.computation

Jargon

Grandstand

微塵

Optimization,aximum of the function, depending on the application and the specific problem. In this chapter we are concerned with optimization of real-valued functions of one or several variables, which optionally can be subject to a set of constraints that restricts the domain of the function.

玩忽職守

Excitotoxin

Sparse Matrices and Graphs,ons are matrices where most of the elements are zeros. Such matrices are known as ., and they occur in many applications, for example, in connection networks (such as circuits) and in large algebraic equation systems that arise, for example, when solving partial differential equations (see . for examples).

Hallmark

Bayesian Statistics,hile it is generally true that statistical problems can in principle be solved using either frequentist or Bayesian statistics, there are practical differences that make these two approaches to statistics suitable for different types of problems

防御

Accrue

Data Processing and Analysis,is direction, we look at the data analysis library .. This library provides convenient data structures for representing series and tables of data, and makes it easy to transform, split, merge, and convert data. These are important steps in the process.

無(wú)思維能力

Ordinary Differential Equations,for many special types of ODEs there are analytical solutions, and in those cases there is a chance that we can find solutions using symbolic methods. If that fails, we must, as usual, resort to numerical techniques.

熟練

Data Input and Output,omputational practitioner, it is important to be able to handle data efficiently and seamlessly, regardless of which format it comes in. This motivates why this entire chapter is devoted to this topic.