Sequential minimax search for a maximum
By J. Kiefer. 1953
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Yonina C. Eldar
- Francis R. Bach
- Felix Stahlberg
- Sébastien Bubeck
- E.J. Candes
- Yoshua Bengio
- Marco F. Duarte
- Dacheng Tao
Citations in the corpus
- Supervised Dimensionality Reduction via Distance Correlation Maximization (2016, methodology): “(2012); Kong et al. (2015); Berrendero José R and Torrecilla (2014) have used Distance Correlation as a criterion for feature selection in a regression setting.”
- Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012, positive): “The bandit strategy we analyze in this section is based on the golden section search due to Kiefer [1953], which is a general algorithm for finding the extremum of a unimodal function.”
- Recent advances in differential evolution: a survey and experimental analysis (2010, background): “Golden Section Search is a classical local search algorithm for non-differentiable fitness functions introduced in Kiefer (1953).”
- Focusing (2004, methodology): “The Fibonacci search technique [1] is the most efficient of any restricted search strategy [8, 9] which is why we claim it is the optimal search.”
- Reconstructing subject‐specific effect maps (2017, unknown): “Optimization given in Equation (10) is one dimensional and can be solved efficiently with golden section search algorithm (Kiefer, 1953).”
- Tractability of Multivariate Problems (2008, background): “Kiefer [106] studied the nonlinear problem of searching for the maximum of a unimodal function and proved that Fibonacci search is optimal.”
- Phase Retrieval via Matrix Completion (2011, methodology): “We implemented this strategy via the standard golden section search [41].”
- Application of Bayesian approach to numerical methods of global and stochastic optimization (1994, background): “For a family of one-dimensional unimodal functions Kiefer (1953) developed the optimal in a minimax sense method.”
- The University of Cambridge’s Machine Translation Systems for WMT18 (2018, result): “Both did not improve translation quality in our setting. gorithm similar to golden-section search (Kiefer, 1953).”