A constraint is a hard limit placed on the value of a variable which prevents us from going forever in certain directions. Page 4. Constrained Optimization.
Stochastic constrained optimization with first-order oracles (SCO) is critical in machine learning. Indeed the scalability of classical machine learning
Knowledge Distillation via Route Constrained Optimization. Xiao Jin1? Baoyun Peng2?
2015. 3. 3. Constrained blackbox optimization is a difficult problem with most approaches coming from the mathematical programming literature.
In this paper we extend some popular optimization algorithm to the Riemannian (constrained) setting. We substantiate our proposed extensions with a range of
Learning: A Constrained Optimization-based Approach. Haichuan Yang1 Shupeng Gui1
Preference-based Constrained Optimization with. CP-nets. Craig Boutilier. Department of Computer Science. University of Toronto. Toronto ON
(2017) proposed the Constrained Policy Optimization. (CPO) algorithm. However policy updates for the CPO algorithm involve solving an optimization problem
Then we transfer the constrained problem to its dual unconstrained optimization problem using an Augmented Lagrangian method (ALM) [2]. We optimize the
The task of optimizing these metrics can naturally be written as a constrained optimization problem wherein one seeks to optimize a quantity such as the