Non-convex optimization is ubiquitous in machine learning. The MajorizationMinimization (MM) procedure systematically optimizes non-convex functions through an iterative construction and optimization of upper bounds on the objective function. The bound at each iteration is required to touch the objective function at the optimizer of the previous bound. We show that this touching constraint is u...

Distribution grids constitute complex networks of lines oftentimes reconfigured to minimize losses, balance loads, alleviate faults, or for maintenance purposes. Topology monitoring becomes a critical task for optimal grid scheduling. While synchrophasor installations are limited in low-voltage grids, utilities have an abundance of smart meter data at their disposal. In this context, a statisti...

Let $Omega_X$ be a bounded, circular and strictly convex domain of a Banach space $X$ and $mathcal{H}(Omega_X)$ denote the space of all holomorphic functions defined on $Omega_X$. The growth space $mathcal{A}^omega(Omega_X)$ is the space of all $finmathcal{H}(Omega_X)$ for which $$|f(x)|leqslant C omega(r_{Omega_X}(x)),quad xin Omega_X,$$ for some constant $C>0$, whenever $r_{Omega_X}$ is the M...

In this paper we work in separated locally convex spaces where we give equivalent statements for the formulae of the conjugate function of the sum of a convex lower-semicontinuous function and the precomposition of another convex lower-semicontinuous function which is also K-increasing with a K-convex K-epi-closed function, where K is a non-empty closed convex cone. These statements prove to be...

This paper uses integrated Data Envelopment Analysis (DEA) models to rank all extreme and non-extreme efficient Decision Making Units (DMUs) and then applies integrated DEA ranking method as a criterion to modify Genetic Algorithm (GA) for finding Pareto optimal solutions of a Multi Objective Programming (MOP) problem. The researchers have used ranking method as a shortcut way to modify GA to d...

We investigate the extremal points of a functional ∫ f(∇u), for a convex or concave function f . The admissible functions u : Ω ⊂ RN → R are convex themselves and satisfy a condition u2 ≤ u ≤ u1. We show that the extremal points are exactly u1 and u2 if these functions are convex and coincide on the boundary ∂Ω. No explicit regularity condition is imposed on f , u1, or u2. Subsequently we discu...

Nonnegative matrix factorization (NMF) is a common method in data mining that have been used in different applications as a dimension reduction, classification or clustering method. Methods in alternating least square (ALS) approach usually used to solve this non-convex minimization problem.  At each step of ALS algorithms two convex least square problems should be solved, which causes high com...