Search Results for author:
Found 5 papers, 1 papers with code
Super-Universal Regularized Newton Method
We analyze the performance of a variant of Newton method with quadratic regularization for solving composite convex minimization problems.
Dynamic pricing under nested logit demand
Recently, there is growing interest and need for dynamic pricing algorithms, especially, in the field of online marketplaces by offering smart pricing options for big online stores.
Optimization and Control Theoretical Economics 90C25, 91B42
Stochastic Subspace Cubic Newton Method
In this paper, we propose a new randomized second-order optimization algorithm---Stochastic Subspace Cubic Newton (SSCN)---for minimizing a high dimensional convex function $f$.
Inexact Tensor Methods with Dynamic Accuracies
In this paper, we study inexact high-order Tensor Methods for solving convex optimization problems with composite objective.
Optimization and Control
Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods
In this paper, we consider a non-convex loss-minimization problem of learning Supervised PageRank models, which can account for features of nodes and edges.
