pulp | A python Linear Programming API | Performance Testing library
kandi X-RAY | pulp Summary
kandi X-RAY | pulp Summary
A python Linear Programming API
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Top functions reviewed by kandi - BETA
- Read a PMPS file .
- Gets the cplexStyleArrays for each variable .
- Solve a CBC .
- Initialize a configuration file .
- Create a list of dictionaries .
- Solve the objective function .
- Solve the dual objective function .
- Read a table from data .
- Read the SCIP solver .
- Creates the slave problem problem
pulp Key Features
pulp Examples and Code Snippets
Lines of Code : 20License : Strong Copyleft (CC BY-SA 4.0)
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FROM openjdk:8
RUN apt-get update && apt-get install -y python3 python3-pip
RUN apt-get -y install python3-pydot python3-pydot-ng graphviz
RUN apt-get -y install python3-tk
RUN apt-get -y install zip unzip
RUN apt-get -y installSets:
- workers = ["a", "b", "c"]
- tasks = [1, 2, ..., 12]
Variables:
- x[w,t] = 1 iff worker w is assigned to task t, 0 otherwise
- min_val
- max_val
Model:
min max_val - min_val
s.t.
# each worker is assigned to exactly n_tasfrom pulp import *
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
load = np.array([0.580416667,0.539066667,0.390116667,0.232033333,
0.204533333,0.194716667,0.194633333,0.209233333,
0.247266668,0.407916668,0.53from scipy.optimize import dual_annealing
def objective(ab):
a, b = ab
mask = (df.x >= a) & (df.y <= b)
if (mask == False).all():
return -1000.0
else:
vals = df[mask].result.values
return min sum(gain[c, v]*b[c,v] for c in customers for v in visits[c])
s.t.
# visit each customer at most once
sum(b[c,v] for v in visits[c]) <= 1 for c in customers
# total number of visits
sum(b[c,v]*v for all v in visits, for all c in # battery modes
import pulp
# some data
num_periods = 3
rate_limits = { 'energy' : 10,
'freq' : 20}
price = 2 # this could be a table or double-indexed table of [t, m] or ....
# SETS
M = rate_limits.keys() # mimport numpy as np
from pulp import LpMaximize, LpMinimize, LpProblem, lpSum, LpVariable
rng = np.random.default_rng(seed=0)
numbers = rng.integers(1, 10**6, size=10**4)
target = int(numbers.mean() * rng.normal(loc=1, scale=0.1))
indicefor i in DEMANDPOINT:
prob += lpSum(z[i][j] for j in FACILITY5) == 1
# !pip install pulp
import pulp
import numpy as np
rows=[17,21,18,52,16]
cols=[4,8,9,9,15]
w=[4,3,1,5,2]
row_list = list(range(len(rows)))
col_list = list(range(len(cols)))
problem = pulp.LpProblem("FillArr")
fill = pulp.LpVariable.dimport pyspark.sql.functions as F
df1 = df.groupBy("bin").count().groupBy().pivot("bin").agg(F.first("count"))
df1 = df1.toDF(*[f"count_bin{c}" for c in df1.columns])
df1.show()
#+----------+----------+----------+
#|count_bin1|count_biCommunity Discussions
Trending Discussions on pulp
QUESTION
I've a spring boot application which run sql on H2 to create database table during startup. The project is in github here.
I've Entity called Movie.java
The sql that I'm running is below and on github here -
...ANSWER
Answered 2022-Apr-03 at 09:39The main problem is probably "movie" as table-name incl the "
So this should work
schema.sql:
QUESTION
I come from the C-Like language world and Python never ceases to amaze me. I realised I have no idea how += operator works in Python. In C-like if I do += on an integer variable, the result is integer:
...ANSWER
Answered 2022-Mar-09 at 06:03Nevermind, I found the answer here:
https://realpython.com/linear-programming-python/
All the operators are overloaded.
QUESTION
I am trying to solve the following problem:
I have a pandas dataframe df with multiple columns. I want to find values a and b to maximise the sum of column 'result' divided by the number of selected rows from the dataframe where a and b are used to select rows of the dataframe using the following constraints:
...ANSWER
Answered 2022-Mar-05 at 07:13You can only use pulp variables to build the objective expression. However, because your objective expression can't be expressed by pulp variables, you need to reformulate the problem and solve it as a mixed-integer program.
Alternatively, you can keep the objective (which doesn't seem to be differentiable) and use a black-box optimization by means of scipy.optimize.dual_annealing. But please be aware that this approach doesn't guarantee a local maximum:
QUESTION
I have a dataframe of title and bin:
ANSWER
Answered 2022-Jan-15 at 13:23Group by bin and count then pivot the column bin and rename the columns of resulting dataframe if you want:
QUESTION
I was wondering if there is a way to handle decoding JSON into a struct, when the API sending the JSON is potentially inconsistent with it's typing. In this case, it sometimes sends a property as an array, and other times as a string. I am not sure how to handle that, or if there is a nice way with Decodable. Example below. My Struct:
...ANSWER
Answered 2021-Nov-10 at 09:28struct Movie: Codable {
let title: String
let cast: [String]
let director: Director
}
enum Director: Codable {
case string(String)
case stringArray([String])
init(from decoder: Decoder) throws {
let container = try decoder.singleValueContainer()
if let x = try? container.decode([String].self) {
self = .stringArray(x)
return
}
if let x = try? container.decode(String.self) {
self = .string(x)
return
}
throw DecodingError.typeMismatch(Director.self, DecodingError.Context(codingPath: decoder.codingPath, debugDescription: "Wrong type for Director"))
}
func encode(to encoder: Encoder) throws {
var container = encoder.singleValueContainer()
switch self {
case .string(let x):
try container.encode(x)
case .stringArray(let x):
try container.encode(x)
}
}
}
typealias Movies = [Movie]
QUESTION
I would like to create a "switch using a decision variable (if syntax)" using Python's pulp. Here, "switch using decision variables (if syntax)" means, for example, "when x (x is an integer greater than or equal to 0) and y (y is a binary variable of 0, 1) are decision variables, if x is an integer greater than or equal to 1, y outputs 1, and if x is 0, y outputs 0. The following is a simple example. The formula is shown in Problem Formulation 3 (image) attached below.
The following is a simple example of how we tried to create a "switch with decision variables (if syntax)" and the results we achieved.
The examples were created by referring to the examples in "Introduction to Operations Research" (Tokai University Press) .
An ice cream shop is planning to produce two kinds of ice cream: espresso ice cream and raspberry ice cream. However, he cannot produce as much as he wants because he is limited to producing 8000 cc of milk and working for 360 minutes. With this amount of milk and time required for each ice cream, what is the plan to increase production to maximize profits? Today, however, the quantity of raspberries (the ingredients) for one serving of raspberry ice cream will expire. Therefore, you need to produce at least one raspberry ice cream so that it does not go to waste.
Product name Quantity of milk needed Working time Profit Espresso ice cream 100cc 7 minutes 50 yen Raspberry ice cream 150cc 5 minutes 10 yenThe above problem setup can be formulated as follows
As a Python program, it can be expressed as follows
...ANSWER
Answered 2021-Nov-08 at 08:37Try:
QUESTION
I obtained the max profit of 6442143.99530000 and the quota for each product by using pulp. However, I would like to output the next suboptimal solutions other than the optimal solution in rank order. Is it possible? In other words, I want to get all combinations assigned to each product.
I need the help of several experts here. Thanks in advance for your help.
...ANSWER
Answered 2021-Oct-29 at 08:10AFAIK, there are two ways to achieve this:
Use a commercial solver like Gurobi and its solution pool to collect the
Nbest solutions.Solve your model, add an integer cut to exclude the found solution and solve the model again. Repeat until you have enough solutions. One way to model such an integer cut is explained here.
After cleaning up your code a bit, here's a working example to find the next 5 best solutions:
QUESTION
I have the following DataFrame
...ANSWER
Answered 2021-Nov-04 at 12:36General solution if Codice are or not unique use DataFrame.explode with crosstab and test if not 0:
QUESTION
I am trying to do an Optimization problem with PuLP but I am having an issue with writing my objective function.
I've simplified my real-life example to a simpler one using cereal. So let's say I have a list of products and a number of Aisles I can put them into (for this example 2). Each product has a number we normally sell each week (ex: we sell 20 boxes of Fruit Loops and 6 boxes of Cheerios each week). Each item also needs a certain amount of shelves (ex: Frosted Flakes needs 1 shelf, but Corn Flakes needs 2).
Product Sales Shelves Assigned Aisle Fruit Loops 20 2 Frosted Flakes 15 1 Cocoa Pebbles 8 1 Fruitty Pebbles 9 1 Corn Flakes 12 2 Cheerios 6 1Each aisle only has 4 shelves. So in theory I could put Fruit Loops and Corn Flakes together in one Aisle (2 shelves + 2 shelves). If I put those in an Aisle the weekly sales from that Aisle would be 20 + 12 = 32. If I put the other 4 items (1 shelf + 1 + 1 + 1) in an Aisle then that Aisles sales would be 15 + 8 + 9 + 6 = 38. The average sales in an Aisle should be 35. The goal of my optimization problem is for each Aisle to be as close to that Average number as possible. I want to minimize the total absolute difference between each Aisles Total Weekly Sales and the Average number. In this example my deviation would be ABS(38-35) + ABS(32-35) = 6. And this is the number I want to minimize.
I cannot figure out the way to write that so PuLP accepts my objective. I can't find an example online with that level of complexity where it's comparing each value to an average and taking the cumulative absolute deviation. And when I write out code that technically would calculate that PuLP doesn't seem to accept it.
Here's some example code:
...ANSWER
Answered 2021-Oct-19 at 16:15Your main issue here is that the ABS function is non-linear. (So is whatever sorting thing you were trying to do... ;) ). So you have to reformulate. The typical way to do this is to introduce a helper variable and consider the "positive" and "negative" deviations separately from the ABS function as both of those are linear. There are several examples of this on the site, including this one that I answered a while back:
How to make integer optimization with absolute values in python?
That introduces the need bring the aisle selection into the index, because you will need to have an index for the aisle sums or diffs. That is not too hard.
Then you have to (as I show below) put in constraints to constrain the new aisle_diff variable to be larger than both the positive or negative deviation from the ABS.
So, I think the below works fine. Note that I introduced a 3rd aisle to make it more interesting/testable. And I left a few comments on your now dead code.
QUESTION
Is there a way for an item in Material-UI's autocomplete to always show, regardless of whether or not has a match? For example under Shawshank Redemption below I've added an alwaysShow key pair. If I start typing "Pulp Fiction" I also want Shawshank Redemption to show.
ANSWER
Answered 2021-Sep-14 at 11:02You can use filterOptions prop in Autocomplete component. It gives you 2 parameter. First one is the options you've given to it and second one is the state of the input component. So you can customize it with your own filterize:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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No vulnerabilities reported
Install pulp
You can use pulp like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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