1 citations · 1 across the 4 of their papers we have counts for
5 papers
Quantum Interior Point Methods: A Review of Developments and An Optimally Scaling Framework
Mohammadhossein Mohammadisiahroudi, Zeguan Wu, Pouya Sampourmahani +2
The growing demand for solving large-scale, data-intensive linear and conic optimization problems, particularly in applications such as artificial intelligence and machine learning…
Optimal Scaling Quantum Interior Point Method for Linear Optimization
Mohammadhossein Mohammadisiahroudi, Zeguan Wu, Pouya Sampourmahani +2
The emergence of huge-scale, data-intensive linear optimization (LO) problems in applications such as machine learning has driven the need for more computationally efficient interi…
A quantum dual logarithmic barrier method for linear optimization
Zeguan Wu, Pouya Sampourmahani, Mohammadhossein Mohammadisiahroudi +1
Quantum computing has the potential to speed up some optimization methods. One can use quantum computers to solve linear systems via Quantum Linear System Algorithms (QLSAs). QLSAs…
Quantum Computing Inspired Iterative Refinement for Semidefinite Optimization
Mohammadhossein Mohammadisiahroudi, Brandon Augustino, Pouya Sampourmahani +1
Iterative Refinement (IR) is a classical computing technique for obtaining highly precise solutions to linear systems of equations, as well as linear optimization problems. In this…
On Semidefinite Representations of Second-order Conic Optimization Problems
Pouya Sampourmahani, Mohammadhossein Mohammadisiahroudi, Tamás Terlaky
Second-order conic optimization (SOCO) can be considered as a special case of semidefinite optimization (SDO). In the literature it has been advised that a SOCO problem can be embe…