In this paper we provide a unified theoretical analysis of multi-attribute graph learning using a penalized log-likelihood objective function. We consider both convex (sparse-group lasso) and ...

Human computation or crowdsourcing involves joint inference of the ground-truth-answers and the worker-abilities by optimizing an objective function, for instance, by maximizing the data likelihood ...

with some vector q ∈ ℝ n and a psd matrix Q ∈ ℝ n×n having eigenvalues λ(Q) ∈ [L, U].As can be verified easily, the gradient and Hessian of a quadratic function of the form (8) are obtained as ∇f(x) = ...

Matrix inequalities and convex functions constitute a central theme in modern mathematical analysis, with far‐reaching implications across numerical analysis, optimisation, quantum information ...

Optimization problems concern exploration of the best possible solutions to a given problem. The feasible solutions are termed good or bad based on the respective values of the objective function. For ...