Find similar content

Source channel @githubtrending · Post #14731 · May 21

#cplusplus#high_performance#interior_point_method#linear_optimization#mixed_integer_programming#parallel#quadratic_programming#simplex HiGHS is a free, high-performance software that solves large and complex optimization problems like linear, quadratic, and mixed-integer programming. It works fast on many computers, including Linux, MacOS, and Windows, without needing extra software. You can use it through various programming languages like Python, C, C#, and Fortran, making it easy to integrate into your projects. HiGHS supports both serial and parallel computing, and it is advancing GPU acceleration for even faster solutions. This helps you efficiently find the best solutions for planning, scheduling, and decision-making problems in science, engineering, and business. Installation is straightforward, and detailed documentation is available to guide you[1][2][3][4]. https://github.com/ERGO-Code/HiGHS

Hashtags

#cplusplus #high_performance #interior_point_method #linear_optimization #mixed_integer_programming #parallel #quadratic_programming #simplex

Results

1 similar post found

Search: #sequences

当前筛选 #sequences清除筛选

djangoproject

@djangoproject · Post #153 · 09/03/2016, 08:20 PM

Find similar View

http://wla.berkeley.edu/~cs61a/fa11/lectures/streams.html In this chapter, we continue our discussion of real-world applications by developing new tools to process #sequential#data. In Chapter 2, we introduced a sequence interface, implemented in Python by built-in data types such as #tuple and #list. #Sequences supported two operations: querying their length and accessing an element by index. In Chapter 3, we developed a user-defined implementations of the sequence interface, the Rlist class for representing recursive lists. These sequence types proved effective for representing and accessing a wide variety of sequential #datasets.

Hashtags

#sequential #data #tuple #list #sequences #datasets