An algorithm for a singly constrained class of quadratic programs subject to upper and lower bounds

By P. Pardalos, N. Kovoor. 1990

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Parseval Networks: Improving Robustness to Adversarial Examples (2017, background): “This is a well studied problem (Michelot, 1986; Pardalos & Kovoor, 1990; Duchi et al., 2008; Condat, 2016).”
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification (2016, background): “The Jacobian matrix of a transformation ρ , J ρ ( z ) := [ ∂ρ i ( z ) /∂z j ] i,j , is of key importance to train models with gradient-based optimization.”
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization (2011, methodology): “A randomized search with bookkeeping (Pardalos and Rosen, 1990) can be straightforwardly used to derive a linear time algorithm.”
Nonlinear optimization and support vector machines (2018, methodology): “In fact, a projection onto the feasible can be performed by efficient algorithms like those proposed in [14], [41], and [62].”
Sparse and Constrained Attention for Neural Machine Translation (2018, methodology): “α? can be computed in O(J) time with the algorithm of Pardalos and Kovoor (1990) (Alg.”