Relaxed gradient projection algorithm for constrained node-based shape optimization

A Gradient Projection Algorithm for Side-constrained Traffic Assignment

A Linearly Convergent Dual-Based Gradient Projection Algorithm for Quadratically Constrained Convex Minimization

This paper presents a new dual formulation for quadratically constrained convex programs (QCCP). The special structure of the derived dual problem allows to apply the gradient projection algorithm to produce a simple explicit method involving only elementary vector-matrix operations, that is proven to converge at a linear rate.

A gradient-based optimization algorithm for LASSO

LASSO is a useful method for achieving both shrinkage and variable selection simultaneously. The main idea of LASSO is to use the L1 constraint in the regularization step which has been applied to various models such as wavelets, kernel machines, smoothing splines, and multiclass logistic models. We call such models with the L1 constraint generalized LASSO models. In this paper, we propose a ne...

A new hybrid conjugate gradient algorithm for unconstrained optimization

In this paper, a new hybrid conjugate gradient algorithm is proposed for solving unconstrained optimization problems. This new method can generate sufficient descent directions unrelated to any line search. Moreover, the global convergence of the proposed method is proved under the Wolfe line search. Numerical experiments are also presented to show the efficiency of the proposed algorithm, espe...

An Efficient Conjugate Gradient Algorithm for Unconstrained Optimization Problems

In this paper, an efficient conjugate gradient method for unconstrained optimization is introduced. Parameters of the method are obtained by solving an optimization problem, and using a variant of the modified secant condition. The new conjugate gradient parameter benefits from function information as well as gradient information in each iteration. The proposed method has global convergence und...