Is there any Boosting algorithm with closed-form solutions?

My goal is to formulate the learning and generalization phases of any Boosting algorithm as a matrix-vector operation.

Through Google, I found a good pdf (*) describing several boosting algorithms but the author Marco Cusumano-Towner only hints and doesn't explain how to recast the boosting algorithms (GradientBoost, Adaboost, QuadBoost) using matrix operations.

My question is:

Can I formulate a boosting algorithm as a regression, a game or a linear programming and arrive at closed-form solution which uses only matrix operations?

• This seems pretty broad, and I don't know of any reason to expect there to be a simple universal solution. Boosting is a generic method that can be applied to many different kinds of weak learners. I suspect everything will depend on the specifics of the weak learners you use with boosting, and I don't know of any reason to expect that the solution will necessarily be matrix-vector operations or to expect the same answer for all weak learners. – D.W. Feb 26 at 0:46
• Also, AdaBoost as normally described doesn't involve computation of any gradients (see, e.g., en.wikipedia.org/wiki/…), so I'm not sure I understand what you mean by "to avoid iterative computing of gradients", and I'm not sure why expressing it in matrix-vector operations would be any better than the standard way it is expressed. – D.W. Feb 26 at 0:48
• Since you mention gradients, are you familiar with gradient boosting and xgboost? Xgboost deals with the special case where the weak learner is a decision tree algorithm, and might be related to what you're hoping for. Apologies if I'm telling you things you already know and are familiar with. – D.W. Feb 26 at 0:50
• nice comments D.W.. I simplified and edited my question, thanks – Ricardo S. Feb 26 at 1:10