1 citations · 1 across the 3 of their papers we have counts for
3 papers · 1 filter
Automatic Generation of Explicit Quadratic Programming Solvers
Maximilian Schaller, Daniel Arnström, Alberto Bemporad +1
We consider a family of convex quadratic programs in which the coefficients of the linear objective term and the righthand side of the constraints are affine functions of a paramet…
Learning Parametric Convex Functions
Maximilian Schaller, Alberto Bemporad, Stephen Boyd
A parametrized convex function depends on a variable and a parameter, and is convex in the variable for any valid value of the parameter. Such functions can be used to specify para…
Code generation for solving and differentiating through convex optimization problems
Maximilian Schaller, Stephen Boyd
We introduce custom code generation for parametrized convex optimization problems that supports evaluating the derivative of the solution with respect to the parameters, i.e., diff…