5 papers
A family of spectral conjugate gradient algorithms derived by least-squares approximations based on a modified quasi--Newton update with application to a revised robust binary classification model
Saman Babaie-Kafaki, Maryam Khoshsimaye-Bargard, Ahmad Mousavi
We develop a spectral three-term modification of the classic Hestenes--Stiefel conjugate gradient algorithm, preserving its anti-jamming characteristic and, simultaneously, taking…
From a Scalar to a Matrix Setting for the Dai--Liao Parameter
Saman Babaie--Kafaki, Morteza Kimiaei, Zohre Aminifard
As is well known, both the numerical performance and the theoretical properties of the Dai--Liao conjugate gradient algorithm are highly dependent on the adjustment of its key para…
An Approximate Conjugate Subgradient Algorithm with Matrix Parameter for Derivative-Free Nonsmooth Optimization Problems
Morteza Kimiaei, Saman Babaie-Kafaki, Zohre Aminifard
We propose a derivative-free matrix conjugate-subgradient method for unconstrained nonsmooth optimization of locally Lipschitz functions. The method constructs discrete gradients u…
Diagonal Hessian Approximation Based on Conjugacy Condition for Noisy Derivative-Free Optimization Problems in High Dimensions
Morteza Kimiaei, Saman Babaie--Kafaki
We consider large-scale noisy derivative-free optimization (DFO) problems in which only function values are available and gradient or subgradient information cannot be reliably est…
An efficient penalty decomposition algorithm for minimization over sparse symmetric sets
Ahmad Mousavi, Morteza Kimiaei, Saman Babaie-Kafaki +1
This paper proposes an improved quasi-Newton penalty decomposition algorithm for the minimization of continuously differentiable functions, possibly nonconvex, over sparse symmetri…