How to improve your diet and save money with Python


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How to improve your diet and save money with Python
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Bauer, Zuria
López, Daniel Domene
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Zuria Bauer/Daniel Domene López - How to improve your diet and save money with Python Optimization in Python (also known as mathematical programming) can be performed by minimization (or maximization) of an objective function within a model that can include discrete variables subject to a set of constrains. At this talk, chemical engineering students of the University of Alicante will introduce the audience to the possibilities of optimization, presenting Pyomo and showing real world examples such as how to improve your diet and save money at fast food restaurants. ----- Process optimization in industry has become essential in order to maximize the resources available and reduce energy consumption. Optimization problems become interesting when dealing with restrictions (linear or nonlinear) and integer variables (modeling the discrete decisions). Python ecosystem presents different libraries to solve optimization problems, some of them are CVXOpt, CVXPy, PulP, OpenOpt, or Pyomo. Among them, Pyomo results interesting because: - It can be used for Mathematical modeling in Python similarly to AMPL (and GAMS) - It communicates with the main solvers used in this field such as GLPK, Gurobi, CPLEX, CBC and PICO - It's free and open source Python library (BSD license), being developed by Sandia National Laboratories, USA. - It supports Python 3 and it is easy to install. The talk will be divided in three parts: 1. Introduction to Mathematical Programming/Optimization (15 min): visual introduction to optimization concepts including restrictions and non linearties (linear Programming, Nonlinear Programming, ILP, MIP, MINLP). 2. Introduction to the Pyomo sintax and a quick note for the installation (20 min): showing how to improve their diet and save money when ordering food in fast food restaurants. 3. Optimization problems in engineering (10 min): showing more advanced optimization examples that include decision variables.
EuroPython 2016
EP 2016
EuroPython Conference
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know what are the methods for optimization that using said you more or any of the tools that you calling from it like you see the data that we I think that people were and this is the Newtonian at that come the conjugate gradient what kind of method of precision is don't put to the minimum it's normally it's an iterative way saying he he tries every thing and wants to change how the solution will fit everything would normally just seems very some problems what the before washing the you have I 129 equations you have 2 of because the solutions of it's every of them so sometimes it will be a good solution would you get that sometimes not say to play living with we normally use guns and how you will see the characters on there actually that they give very similar to the same solution which is a problem because of resource that stuff like that but we don't know the want to and yet the article it of the 1 thank you very much for the talk with the optimization of calories fall 5 euros did you have any constraints as in non toxic level of salt effect so you can use life those calories so it's just happened to have no constraints on highways looking at that in I've seen an Internet that you should lead to problems of books sold at which is the river of I think it's a lot more than it was looking at it and if you I think if you need that much of calories of sold today when you go to the hospital in the next so it will be I think you can be blocked much for only 1 then you would like to do for you at the end of the month 1 and the all the the 2 thousand 600 calories yet the problem goods from have the look of salt and sugar and that's to what should have been you but the you will have problems later maybe and know maybe not you and you should try we should try to hit the life of me please send me a message tried to be hi thank you for the talk i in the question why can't you drink this was is with was basically drink and drive you know why did that there is a was the waste thundering . 4 if you want to play around buy found that's all that's in this and that's the case that have to study of boat distillation you have a lot of types of different outcomes I think in this case and you have to see that there are some types you can get it from it say not what extracting and not in the water so maybe you yeah In the knowledge that you would use and only and everything we got in the stomach and bearing production you don't should lead difficulties that well I didn't implemented it could so does that mean if you waste water with risk you could you know that at so that OK actually relations this lecture questions and it is it it is it possible to have the 2 objectives of the implemented now the patrician constraints as constraints that but can they be 2 objectives which are somehow weighted that you can say well that you can compromise between the nutritional value and and money spent so it's it's fine if the nutrition value is is not quite as high if if you were saved to euro on the 1 1 and if you have a final deal the if there uh exists knowledge using this technique for sample the fuel and that's the mean of me say no costs and they may even 10 that's what quantities that a different so you have to use their and I did on all take using these is that but yes you could do do it by using only the the this is on the floor when I did this question this is 1 liter would come to the colonists point into what we will be using when you want do it this is that you can on transform 1 function objective and rates this point have to function you only have to to do what I'm dealing with you know what do which normally we don't the distinction 45 minutes was living costs and the reason that she's so so we can have our review and


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