Projected gradient methods for linearly constrained problems
Mathematical Programming: Series A and B
On the convergence of projected gradient processes to singular critical points
Journal of Optimization Theory and Applications
On the identification of active constraints
SIAM Journal on Numerical Analysis
On the linear convergence of descent methods for convex essentially smooth minimization
SIAM Journal on Control and Optimization
On the convergence rate of dual ascent methods for linearly constrained convex minimization
Mathematics of Operations Research
Error bounds in mathematical programming
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
Convergence of the Gradient Projection Method for Generalized Convex Minimization
Computational Optimization and Applications
Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
The Ordered Subsets Mirror Descent Optimization Method with Applications to Tomography
SIAM Journal on Optimization
Interior Gradient and Epsilon-Subgradient Descent Methods for Constrained Convex Minimization
Mathematics of Operations Research
Mirror descent and nonlinear projected subgradient methods for convex optimization
Operations Research Letters
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This paper presents a new dual formulation for quadratically constrained convex programs. The special structure of the derived dual problem allows us to apply the gradient projection algorithm to produce a simple explicit method involving only elementary vector-matrix operations, proven to converge at a linear rate.