i have considered your OSQP solver to speed up my QP-work. I think the solution is very smart! I am working on supportive vector regressions in R and not sure how to transform the A matrix for QSQP accordingly.
Maybe you could please give me a hint how to solve this issue?
I have added an example with the ipop solver from kernlab, i want to get the same solutions from OSQP. Any help is highly appreciated!
Thank you & Kind regards
K=kernels.gen(data=x, train.samples = 1:150, kernels = c(‘radial’,‘radial’),
sigma = c(0.25,4))
#solve with kernlabs ipop
kk=Reduce(’+’,mapply("", k, gamma,SIMPLIFY = FALSE))
#eps = 1e-11
#solve with rosqp
M <- Matrix(Dmat, sparse = TRUE)
Amat<- Matrix(A, sparse = TRUE) #thinking this is incorrect and shoudl be somehow transformed
res = solve_osqp(Dmat, dvec,Amat) #osqp(P=NULL, q=NULL, A=NULL, l=NULL, u=NULL, pars=osqpSettings())
#Error in osqp(P, q, A, l, u, pars) : dim(A) == c(m, n) are not all TRUE
Maybe a simple example for SVR in R would be excellent to add, too.