activity
20172022
most citedSecond-Order Methods with Cubic Regularization Under Inexact Information

20 citations · 33 across the 5 of their papers we have counts for

collaborators
Showing math.OCShow all

6 papers · 1 filter

math.OC20221 cited

Stochastic Search for a Parametric Cost Function Approximation: Energy storage with rolling forecasts

Saeed Ghadimi, Warren B. Powell

Rolling forecasts have been almost overlooked in the renewable energy storage literature. In this paper, we provide a new approach for handling uncertainty not just in the accuracy…

math.OC20205 cited

Reinforcement Learning via Parametric Cost Function Approximation for Multistage Stochastic Programming

Saeed Ghadimi, Raymond T. Perkins, Warren B. Powell

The most common approaches for solving stochastic resource allocation problems in the research literature is to either use value functions ("dynamic programming") or scenario trees…

math.OC2018

A Single Time-Scale Stochastic Approximation Method for Nested Stochastic Optimization

Saeed Ghadimi, Andrzej Ruszczyński, Mengdi Wang

We study constrained nested stochastic optimization problems in which the objective function is a composition of two smooth functions whose exact values and derivatives are not ava…

math.OC2018

Zeroth-order Nonconvex Stochastic Optimization: Handling Constraints, High-Dimensionality and Saddle-Points

Krishnakumar Balasubramanian, Saeed Ghadimi

In this paper, we propose and analyze zeroth-order stochastic approximation algorithms for nonconvex and convex optimization, with a focus on addressing constrained optimization, h…

math.OC2018

Approximation Methods for Bilevel Programming

Saeed Ghadimi, Mengdi Wang

In this paper, we study a class of bilevel programming problem where the inner objective function is strongly convex. More specifically, under some mile assumptions on the partial…

math.OC201720 cited

Second-Order Methods with Cubic Regularization Under Inexact Information

Saeed Ghadimi, Han Liu, Tong Zhang

In this paper, we generalize (accelerated) Newton's method with cubic regularization under inexact second-order information for (strongly) convex optimization problems. Under mild…