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20122021
most citedBest practices for comparing optimization algorithms

194 citations · 285 across the 9 of their papers we have counts for

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8 papers · 1 filter

math.OC2020

Hessian approximations

Warren Hare, Gabriel Jarry-Bolduc, Chayne Planiden

This work introduces the nested-set Hessian approximation, a second-order approximation method that can be used in any derivative-free optimization routine that requires such infor…

math.OC2020

A deterministic algorithm to compute the cosine measure of a finite positive spanning set

Warren Hare, Gabriel Jarry-Bolduc

Originally developed in 1954, positive bases and positive spanning sets have been found to be a valuable concept in derivative-free optimization (DFO). The quality of a positive ba…

math.OC2019

The chain rule for VU-decompositions of nonsmooth functions

Warren Hare, Chayne Planiden, Claudia Sagastizábal

In Variational Analysis, VU-theory provides a set of tools that is helpful for understanding and exploiting the structure of nonsmooth functions. The theory takes advantage of the…

math.OC20192 cited

A derivative-free -algorithm for convex finite-max problems

Warren Hare, Chayne Planiden, Claudia Sagastizábal

The -algorithm is a superlinearly convergent method for minimizing nonsmooth, convex functions. At each iteration, the algorithm works with a certain -sp…

math.OC2017194 cited

Best practices for comparing optimization algorithms

Vahid Beiranvand, Warren Hare, Yves Lucet

Comparing, or benchmarking, of optimization algorithms is a complicated task that involves many subtle considerations to yield a fair and unbiased evaluation. In this paper, we sys…

math.OC20174 cited

Visualization of the ε-Subdifferential of Piecewise Linear-Quadratic Functions

Anuj Bajaj, Warren Hare, Yves Lucet

Computing explicitly the ε-subdifferential of a proper function amounts to computing the level set of a convex function namely the conjugate minus a linear function. The resulting…