cv.glmnet does not converge for certain lamda values - r

Question

I am training a multinomial regression model using cv.glmnet, and the number of features and number of classes I have been using has been increasing. On previous versions of my training set, where I had fewer features and classes, my model converged for all lambdas after increasing the value of maxit.
However, with the training data I am using now, I get the following errors even when I increase maxit = 10^7.
Warning messages:
1: from glmnet C++ code (error code -13); Convergence for 13th lambda
value not reached after maxit=100000 iterations; solutions for larger
lambdas returned
2: from glmnet C++ code (error code -14); Convergence for 14th lambda
value not reached after maxit=100000 iterations; solutions for larger
lambdas returned
3: from glmnet C++ code (error code -13);
Convergence for 13th lambda value not reached after maxit=100000
iterations; solutions for larger lambdas returned
.
.
.
Here is code that recreates these warnings:
load(url("https://github.com/DylanDijk/RepoA/blob/main/reprod_features.rda?raw=true"))
load(url("https://github.com/DylanDijk/RepoA/blob/main/reprod_response.rda?raw=true"))
# Training the model:
model_multinom_cv = glmnet::cv.glmnet(x = reprod_features, y = reprod_response,
family = "multinomial", alpha = 1)
I was wondering if anyone had any advise on trying to get a model to converge for all lambda values in the path.
Some options I have been thinking that I will try:
Would an option be to change some of the internal parameters as
listed in the glmnet
vignette
Or to select a lambda sequence myself and then increase maxit
further. I have tried maxit = 10^8 without defining a lambda
sequence but this did not train after multiple hours.
Choose a subset of the features, I have trained the model with a small subset of the features and it the model converged for more lambda values.
But I would rather use all of the features so want to explore whether there are other options first.
Lambda path returned
Below is the lambda path returned after training my model:
> model_multinom_cv$glmnet.fit
Call: glmnet(x = train_sparse, y = train_res, trace.it = 1,
family = "multinomial", alpha = 1)
Df %Dev Lambda
1 0 0.00 0.17730
2 1 1.10 0.16150
3 2 1.88 0.14720
4 5 4.72 0.13410
5 8 8.52 0.12220
6 14 13.49 0.11130
7 21 19.90 0.10150
8 27 25.83 0.09244
9 31 30.63 0.08423
10 36 34.56 0.07674
11 41 38.61 0.06993
12 45 41.89 0.06371