Solving large sparse Ax=b with lower bound constraint
3 vues (au cours des 30 derniers jours)
Afficher commentaires plus anciens
I'm having some issues with some forward modelling I'm doing - im doing a forward model in a loop and currently solving an Ax=b system using a conjugate gradient least squares method (code from https://web.stanford.edu/group/SOL/software/cgls/).
My problem is that this solution is unconstrained, and so the solution converges close to the answer I expect, but has values outside permitted bounds still. If my constraint is say x >= c (c is a vector), is there a matlab inbuilt function that can do this?? I've tried lsqlin(A,b,[],[],[],[],c,[]) rather than the cgls function linked above, but the model no longer converges to a result that is even remotely correct.
0 commentaires
Réponse acceptée
Plus de réponses (0)
Voir également
Catégories
En savoir plus sur Linear Least Squares dans Help Center et File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!