A Smooth Perceptron Algorithm. - Negar Soheili; Javier Peña 001 (SIAM J. Optim., 2012)

A condition-based algorithm for solving polyhedral feasibility problems. - Negar Soheili; Javier Peña 001 (J. Complex., 2014)

Some preconditioners for systems of linear inequalities. - Javier Peña 001; Vera Roshchina; Negar Soheili (Optim. Lett., 2014)

A deterministic rescaled perceptron algorithm. - Javier Peña 001; Negar Soheili (Math. Program., 2016)

On the von Neumann and Frank-Wolfe Algorithms with Away Steps. - Javier Peña 001; Daniel Rodríguez; Negar Soheili (SIAM J. Optim., 2016)

Solving Conic Systems via Projection and Rescaling. - Javier Peña 001; Negar Soheili (Math. Program., 2017)

A Level-Set Method for Convex Optimization with a Feasible Solution Path. - Qihang Lin; Selvaprabu Nadarajah; Negar Soheili (SIAM J. Optim., 2018)

Self-guided Approximate Linear Programs. - Parshan Pakiman; Selvaprabu Nadarajah; Negar Soheili; Qihang Lin (CoRR, 2020)

A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints. - Qihang Lin; Selvaprabu Nadarajah; Negar Soheili; Tianbao Yang (J. Mach. Learn. Res., 2020)

Revisiting Approximate Linear Programming: Constraint-Violation Learning with Applications to Inventory Control and Energy Storage. - Qihang Lin; Selvaprabu Nadarajah; Negar Soheili (Manag. Sci., 2020)

Computational performance of a projection and rescaling algorithm. - Javier Peña 001; Negar Soheili (Optim. Methods Softw., 2021)

Projection and Rescaling Algorithm for Finding Maximum Support Solutions to Polyhedral Conic Systems. - Javier Peña 001; Negar Soheili (Math. Oper. Res., 2022)

Implementation of a projection and rescaling algorithm for second-order conic feasibility problems. - Javier Peña 001; Negar Soheili (Optim. Methods Softw., 2023)

Self-Guided Approximate Linear Programs: Randomized Multi-Shot Approximation of Discounted Cost Markov Decision Processes. - Parshan Pakiman; Selvaprabu Nadarajah; Negar Soheili; Qihang Lin (Manag. Sci., 2025)