Minimizing Quadratic Functions in Constant Time
Authors Kohei Hayashi, Yuichi Yoshida Abstract A sampling-based optimization method for quadratic functions is proposed. Our method approximately solves the following $n$-dimensional quadratic minimization problem in constant time, which is independent of $n$: $z^*=\min_{\bv \in \bbR^n}\bracket{\bv}{A \bv} + n\bracket{\bv}{\diag(\bd)\bv} + n\bracket{\bb}{\bv}$, where $A \in \bbR^{n \times n}$ is a
NIPS 2016 Proceedings
Advances in Neural Information Processing Systems 29 (NIPS 2016) The papers below appear in Advances in Neural Information Processing Systems 29 edited by D.D. Lee and M. Sugiyama and U.V. Luxburg and I. Guyon and R. Garnett. They are proceedings from the conference, "Neural Information Processing Systems 2016." Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much Bryan
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