ompr | R package to model Mixed Integer Linear Programs | Compiler library

I am trying to make run this model. I am trying to maximize:x[4]

I am trying to learn to fit a linear integer programming optimization model in R using the ompr package that a colleague had previously fit using CPLEX/GAMS (specifically, the one described here: Haight et al. 2021). I am running my implementation on a Linux Supercomputing server at my University that has 248gb of memory, which I'd think would be sufficient for the job.

Here is my code and output from the failure report from the server:

I'm currently using the ompr and roi packages in R to solve a IP type problem. I've managed to solve a simple version, however, I'm now trying to extend the problem, and I only want to include an item in 1 knapsack (don't allow it to appear in a knapsack more than once). Below is an example dataset I am using (called KP):

My inital version of the knapsack I solved using the following code (and with a knapsack capacity of 240):

I have the following dockfile

I'm using the ompr package for creating and solving an integer programming problem. For simplicity's sake, I will use NFL football fantasy players as my example.

I want to maximize the number of points scored across the 2 games, while only playing 1 player at each position per game. (For simplicity's sake, here assume that any player can play any position.)

The part I'm having trouble with is that of the 25 possible players, I want to limit the total number of players chosen across the two games to 15. The i component of the added ompr variable represents the player indices, but I'm not sure how to add a constraint that limits the total unique i's chosen.

Any help would be greatly appreciated!

I am trying to add a constraint to a mixed integer programming model in R so that each person is only assigned to a single role.

I have a data frame that looks like this:

I am familiar with how to use ompr::MIPModel but am trying to learn how to use MILPModel to take advantage of the model build speed. A simplified version of my model is below. I have two decision variables, x and y, binary and of equal length. I have constraints on the sum of all the x decision variables, and the sum of all the y decision variables. So far so good with MILPModel, I can build the model and solve it fast.

The problem is when I try to use the next constraint. The LHS of this constraint multiplies the x binary decision variables by a numeric column in a dataframe of the same length, then multiplies that by a matrix where the rows are equal to the length of x. Similar story in the RHS with the y variable. I then iterate this constraint 20 times to represent all the columns of the matrix.

I've used constraints similar to this many times using MIPModel, but now when I try this I get an error message, non-numeric argument to binary operator. I assume this has something to do with the colwise function, but I am unfamiliar with how to approach this, even after reading up on the ompr github site. Thanks in advance for any help.