breeze.optimize.linear
Solve argmin (a dot x + .5 * x dot (B * x) + .5 * normSquaredPenalty * (x dot x)) for x subject to norm(x) <= maxNormValue
Based on the code from "Trust Region Newton Method for Large-Scale Logistic Regression" * @author dlwh
Returns the vector x and the vector r.
Returns the vector x and the vector r. x is the minimizer, while r is the residual error (which may not be near zero because of the norm constraint.)
Solve argmin (a dot x + .5 * x dot (B * x) + .5 * normSquaredPenalty * (x dot x)) for x subject to norm(x) <= maxNormValue
Based on the code from "Trust Region Newton Method for Large-Scale Logistic Regression" * @author dlwh