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20122021
most citedPractical Integer-to-Binary Mapping for Quantum Annealers

32 citations · 65 across the 6 of their papers we have counts for

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7 papers

math.OC2021

Nonlinear conjugate gradient for smooth convex functions

Sahar Karimi, Stephen Vavasis

The method of nonlinear conjugate gradients (NCG) is widely used in practice for unconstrained optimization, but it satisfies weak complexity bounds at best when applied to smooth…

math.OC2017★ 9 cited

A single potential governing convergence of conjugate gradient, accelerated gradient and geometric descent

Sahar Karimi, Stephen Vavasis

Nesterov's accelerated gradient (AG) method for minimizing a smooth strongly convex function is known to reduce by a factor of after $k=…

quant-ph2017★ 32 cited

Practical Integer-to-Binary Mapping for Quantum Annealers

Sahar Karimi, Pooya Ronagh

Recent advancements in quantum annealing hardware and numerous studies in this area suggests that quantum annealers have the potential to be effective in solving unconstrained bina…

math.OC2016

A Subgradient Approach for Constrained Binary Optimization via Quantum Adiabatic Evolution

Sahar Karimi, Pooya Ronagh

An earlier work [18] proposes a method for solving the Lagrangian dual of a constrained binary quadratic programming problem via quantum adiabatic evolution using an outer approxim…

math.OC2016★ 5 cited

A unified convergence bound for conjugate gradient and accelerated gradient

Sahar Karimi, Stephen A. Vavasis

Nesterov's accelerated gradient method for minimizing a smooth strongly convex function is known to reduce $f(\x_k)-f(\x^*)$ by a factor of $\eps\in(0,1)$ after $k\ge O(\sqrt{L…

math.OC2014★ 18 cited

IMRO: a proximal quasi-Newton method for solving -regularized least square problem

Sahar Karimi, Stephen Vavasis

We present a proximal quasi-Newton method in which the approximation of the Hessian has the special format of "identity minus rank one" (IMRO) in each iteration. The proposed struc…

math.OC2012★ 1 cited

Detecting and correcting the loss of independence in nonlinear conjugate gradient

Sahar Karimi, Stephen Vavasis

It is well known that search directions in nonlinear conjugate gradient (CG) can sometimes become nearly dependent, causing a dramatic slow-down in the convergence rate. We provide…