optimization

问题 My host has a really, really low number of max connections for a database user. This is the error my users are getting: User 'username_here' has exceeded the 'max_user_connections' resource (current value: 15). I don't think it's in my power to raise that value, unless I upgrade to a much more expensive plan, so I'm looking for a way to use these 15 connections effectively. My way of handling connections is the following: I connect to the database in a script I load at the start of every page

问题 An obligatory assurance that I have read the many posts on the topic before posting this. I'm aware that multiprocessing entails a fixed cost, but to the best of my knowledge this doesn't seem to be the problem here. I basically have a number of separate optimisation problems, and want to solve them in parallel. The following code is a simple example: import psutil import multiprocessing as mp import time from scipy.optimize import minimize import numpy as np pset = np.random.uniform(-10,10

问题 An obligatory assurance that I have read the many posts on the topic before posting this. I'm aware that multiprocessing entails a fixed cost, but to the best of my knowledge this doesn't seem to be the problem here. I basically have a number of separate optimisation problems, and want to solve them in parallel. The following code is a simple example: import psutil import multiprocessing as mp import time from scipy.optimize import minimize import numpy as np pset = np.random.uniform(-10,10

问题 Here is the code below (ans to a CP qs) The time limit for execution is 6 seconds but my code is slower than. How can I make it more memory and time efficient ? Input: the input begins with the number t of test cases in a single line ( t <= 10 ). In each of the next t lines there are two numbers m and n ( 1 <= m <= n <= 1000000000 , n-m <= 100000 ) separated by a space. Output : for every test case print all prime numbers p such that m <= p <= n , one number per line, test cases separated by

问题 This example from PYMOTW gives an example of using multiprocessing.Pool() where the processes argument (number of worker processes) passed is twice the number of cores on the machine. pool_size = multiprocessing.cpu_count() * 2 (The class will otherwise default to just cpu_count() .) Is there any validity to this? What is the effect of creating more workers than there are cores? Is there ever a case to be made for doing this, or will it perhaps impose additional overhead in the wrong

问题 This example from PYMOTW gives an example of using multiprocessing.Pool() where the processes argument (number of worker processes) passed is twice the number of cores on the machine. pool_size = multiprocessing.cpu_count() * 2 (The class will otherwise default to just cpu_count() .) Is there any validity to this? What is the effect of creating more workers than there are cores? Is there ever a case to be made for doing this, or will it perhaps impose additional overhead in the wrong

问题 Easy function that works in JS without ES6: var evenOrOdd = function(n){ if(n % 2 == 1){ return "Odd"; } else { return "Even"; } } console.log(evenOrOdd(3)); //returns odd My attempt at restructuring this using ES6: const evenOrOdd = (n) => {(n % 2 == 1) ? "Odd" : "Even"}; console.log(evenOrOdd(3)); //returns undefined I'm following these examples here: 2ality and stoimen. Why is this arrow function returning undefined ? 回答1: You have to remove the {} . const evenOrOdd = n => (n % 2 === 1 ?

问题 Easy function that works in JS without ES6: var evenOrOdd = function(n){ if(n % 2 == 1){ return "Odd"; } else { return "Even"; } } console.log(evenOrOdd(3)); //returns odd My attempt at restructuring this using ES6: const evenOrOdd = (n) => {(n % 2 == 1) ? "Odd" : "Even"}; console.log(evenOrOdd(3)); //returns undefined I'm following these examples here: 2ality and stoimen. Why is this arrow function returning undefined ? 回答1: You have to remove the {} . const evenOrOdd = n => (n % 2 === 1 ?

问题 I have a Google Sheet where rows are products and columns are information about these products (Material, Color, Price, ...). These information are retrieved using the function IMPORTXML with the corresponding xpath query. In the following example, the column D is the URL of this product. These functions retrieve information (Glass, Color and Type) for the product on row 3: =IMPORTXML(D3,"//table[@class='info-table']/tr[th/text()[contains(.,'Glass')]]/td") =IMPORTXML(D3,"//table[@class='info

来源： https://stackoverflow.com/questions/64542594/how-could-constraints-be-dynamically-constructed-in-gekko