Nadav Hallak

19 papers A* 5Journal 14
YearRankTypeTitle / Venue / Authors
2026 J jnl
An Augmented Lagrangian Approach to Bi-Level Optimization via a Smooth Equilibrium Constrained Problem.
J. Optim. Theory Appl.
Nadav Hallak, Nitay Suissa
2025 A* conf
A Stochastic Approach to the Subset Selection Problem via Mirror Descent.
ICLR
Dan Greenstein, Elazar Gershuni, Ilan Ben-Bassat, Yaroslav Fyodorov, Ran Moshe, Fiana Raiber, Alex Shtoff, Oren Somekh, Nadav Hallak
2024 J jnl
A Path-Based Approach to Constrained Sparse Optimization.
SIAM J. Optim.
Nadav Hallak
2024 A* conf
A Study of First-Order Methods with a Deterministic Relative-Error Gradient Oracle.
ICML
Nadav Hallak, Kfir Yehuda Levy
2023 J jnl
An Adaptive Lagrangian-Based Scheme for Nonconvex Composite Optimization.
Math. Oper. Res.
Nadav Hallak, Marc Teboulle
2022 J jnl
A Dynamic Alternating Direction of Multipliers for Nonconvex Minimization with Nonlinear Functional Equality Constraints.
J. Optim. Theory Appl.
Eyal Cohen, Nadav Hallak, Marc Teboulle
2022 J jnl
The regularized feasible directions method for nonconvex optimization.
Oper. Res. Lett.
Amir Beck, Nadav Hallak
2021 A* conf
Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach.
ICML
Nadav Hallak, Panayotis Mertikopoulos, Volkan Cevher
2020 A* conf
Efficient Proximal Mapping of the 1-path-norm of Shallow Networks.
ICML
Fabian Latorre, Paul Rolland, Nadav Hallak, Volkan Cevher
2020 J jnl
Efficient Proximal Mapping of the 1-path-norm of Shallow Networks.
CoRR
Fabian Latorre, Paul Rolland, Nadav Hallak, Volkan Cevher
2020 J jnl
Finding Second-Order Stationary Points in Constrained Minimization: A Feasible Direction Approach.
J. Optim. Theory Appl.
Nadav Hallak, Marc Teboulle
2020 A* conf
On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems.
NeurIPS
Panayotis Mertikopoulos, Nadav Hallak, Ali Kavis, Volkan Cevher
2020 J jnl
On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems.
CoRR
Panayotis Mertikopoulos, Nadav Hallak, Ali Kavis, Volkan Cevher
2020 J jnl
On the Convergence to Stationary Points of Deterministic and Randomized Feasible Descent Directions Methods.
SIAM J. Optim.
Amir Beck, Nadav Hallak
2020 J jnl
Regret minimization in stochastic non-convex learning via a proximal-gradient approach.
CoRR
Nadav Hallak, Panayotis Mertikopoulos, Volkan Cevher
2019 J jnl
A non-Euclidean gradient descent method with sketching for unconstrained matrix minimization.
Oper. Res. Lett.
Nadav Hallak, Marc Teboulle
2019 J jnl
Optimization problems involving group sparsity terms.
Math. Program.
Amir Beck, Nadav Hallak
2018 J jnl
Proximal Mapping for Symmetric Penalty and Sparsity.
SIAM J. Optim.
Amir Beck, Nadav Hallak
2016 J jnl
On the Minimization Over Sparse Symmetric Sets: Projections, Optimality Conditions, and Algorithms.
Math. Oper. Res.
Amir Beck, Nadav Hallak