I basically have to obtain a certain probability distribution by sampling from a sample of 5000 matrices. So I just need to count how many times element X occurs in the position (i,j) of these 5000 matrices. Then, I save these [values and counts] in a dictionary. That said, I thought it could be a good idea to parallelize my
Tag: optimization
Insert matrix inside another matrix using numpy without overwriting some original values
I need to insert a matrix inside another one using numpy The matrix i need to insert is like this one: While the other matrix is like this: The code i’m actually using this one: The problem matrix that i’m getting is this one: while the expected result is: The error is that, since the matrix contains 0, when i
Creating Pyomo’s ConstraintList with a list of constraints rather than adding them individually?
I’m currently storing my Pyomo variables in a Pandas dataframe, and have been using that to generate arrays of constraints. Is there a way I can add them to my model (e.g. by initialising a ConstraintList with it) rather than having to loop through them all and add them individually? I don’t think I’m able to use a rule to
score calculation takes too long: avoid for loops – python
I am new to python and I need your kindly help. I have three matrices, in particular: Matrix M (class of the matrix: scipy.sparse.csc.csc_matrix), dimensions: N x C; Matrix G (class of the matrix: numpy.ndarray), dimensions: C x T; Matrix L (class of the matrix: numpy.ndarray), dimensions: T x N. Where: N = 10000, C = 1000, T = 20.
using ModuleList, still getting ValueError: optimizer got an empty parameter list
With Pytorch I am attempting to use ModuleList to ensure model parameters are detected, and can be optimized. When calling the SGD optimizer I get the following error: ValueError: optimizer got an empty parameter list Can you please review the code below and advise? Answer This seems to be a copy-paste issue: your __init__ has 3 underscores instead of 2,
Best parameters of an Optuna multi-objective optimization
When performing a single-objective optimization with Optuna, the best parameters of the study are accessible using: If I want to perform a multi-objective optimization, this would be become for example : This works, but the command study.best_params fails with RuntimeError: The best trial of a ‘study’ is only supported for single-objective optimization. How can I get the best parameters for
Filter 2d list by another 2d list
I have a list A: And list B: I would like to leave only these rows in list A, that all values of which are contained in at least one row from list B. So my expected output is: Because values ‘512’ and ‘102’ are in second row of list B. I know how to achieve that by iterating or
How to include a variable in the summation bound of a objective function with pyomo?
I’m using pyomo to find an approach for solving an energy optimization problem. I’m trying to find the optimal time slot during a day to shift the start of a smart dishwasher to, so that the electricity cost is minimized. I’m using an example from a paper but unfortunately I can’t embed pictures yet, so here are the links to
Absurd solution using gurobi python in regression
So I am new to gurobi and I decided to start working with it on a well known problem as regression. I found this official notebook, where an L0 penalized regression model was solved and I took just the part of the regression model out of it. However, when I solve this problem in gurobi, I get a really strange
Python 3: time.perf_counter() output does not match the programme processing time on Coursera
I have been working on a Coursera assignment, it required my run time to be less than 5.00 . The code is below, but my question is basically about the first and last line, where I was testing the processing time Using the last line of code, my processing time was 3.3251947 However, when I submitted this code onto Coursera,