We consider the classical problem of estimating a density on $[0,1]$ via some maximum entropy criterion. For solving this convex optimization problem with algorithms using first-order or second-order methods, at each iteration one has to compute (or at least approximate) moments of some measure with a density on $[0,1]$, to obtain gradient and Hessian data. We propose a numerical scheme based on semidefinite programming that avoids computing quadrature formula for this gradient and Hessian computation.

## Citation

To appear in Proceedings of the 46th IEEE CDC Conference, New Orleans, December 2007.

## Article

View Semidefinite Programming for Gradient and Hessian Computation in Maximum Entropy Estimation