Anna Nicanorova: Optimizing Life Everyday Problems Solved with Linear Programing in Python

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Anna Nicanorova: Optimizing Life Everyday Problems Solved with Linear Programing in Python

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Күн бұрын

PyData NYC 2015
Linear Optimization can be a very powerful tool to enable mathematical decision-making under constrains. This tutorial is designed on how to build a linear program optimizer in python. To make the format more entertaining, the tutorial problems are designed to tackle relevant day-to-day problems on how to optimize your vacation, see all art around museum and create optimal reading lists.
Linear Optimization is a very established area in operations research famous for solving investing and transportation problems. Linear Programing and Integer programing can describe a problem where decisions are constrained by problems and the solution requires decision where one seeks to maximize/minimize objectives (basically everyday life). So it always surprised me why more people don’t use LP for solving their real life problems. Also LP/IP can replace sometimes very complex algorithms where one seeks to optimize under constrains.
This is a tutorial how to use LP modeling framework in Python (using Pulp and Scipy) by giving relevant example of optimizing everyday life. It is amazing, that by properly translating the problem with algebraic expressions, we can find solutions to such relevant everyday problems as how many/which bestsellers to read in a year, which vacations to take, while keeping costs minimal and how to cover all museums in NYC.
Slides available here: github.com/AnnaNican/optimizers 00:00 Welcome!
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Пікірлер: 10

@marwinsolomon9511 2 жыл бұрын

Great video with excellent explanation and implementation of python for solving real-world problems which can be modelled linearly👍👏

@vocabularybytesbypriyankgo1558 Жыл бұрын

Great Video, taught me how to allocate quantities for a retail store, given quantities in the warehouse and with constraints

@sunnylee6238 8 жыл бұрын

Thanks! This is what I'm looking for.

@vishnujatav6329 Жыл бұрын

Excellent

@amirsadrpour3456 8 жыл бұрын

great video, she mentioned solving large scale linear programming problems in her company, do you use PULP ? does it scale well with size of the data ?

@JohnForbes 8 жыл бұрын

Wouldn't a more constrained problem be computed faster as there would be less possibilities that need to be considered? @13:15

@gregoryfenn1462 8 жыл бұрын

+John Forbes Not usually. The program doesn't literally go through each possibility to identify the best: it looks at all the extreme vertices of the space defined by the constraints. As such, the more constraints there are, the more vertices need to be checked. E.g. when we have 2 decision variables. x and y, consider these three constraints - 2x + y >= 1 - x >= 0 - y >= 0 and we want to minimise f(x, y) = 3x + y^2 then we imagine the 3D-Space with the curved surface "z = 3x + y^2" drawn, and the three flat planes "2x + y = 1", "x >= 0", "y >= 0" drawn too. Then there is a space where the z-curve exists within the three constraints. Looking at the extremums of it (min, max, x infinity, y infinity) let's us find the solution quickly.

@JohnForbes 8 жыл бұрын

Thanks for the explanation.

@LuisFernandoValenzuela 7 жыл бұрын

it depends more on the topology of the feasible set (and the complexity of the constrains)