The volume algorithm: producing primal solutions with a subgradient method

By F. Barahona, R. Anbil. Mathematical programming, 2000

339 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Networks and Communications, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/f5d8bc6143c3d64069335724458f7eb7ef96b8a6

How researchers see this paper

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

In Computational Theory and Mathematics

1. Asuman E. Ozdaglar
2. Andrea Roli
3. Jakob Puchinger
4. Günther R. Raidl
5. José Andrés Moreno-Pérez
6. Pierre Hansen
7. Christian Blum
8. Nikos Paragios

In Computer Networks and Communications

1. Angelia Nedić
2. Asuman E. Ozdaglar
3. Nikos Komodakis
4. Christian Blum
5. Alexander Olshevsky
6. Nikos Paragios
7. G. Tziritas
8. Günther R. Raidl

In Numerical Analysis

1. Nikos Komodakis
2. Asuman E. Ozdaglar
3. Angelia Nedić
4. Nikos Paragios
5. G. Tziritas
6. José Andrés Moreno-Pérez
7. Günther R. Raidl
8. Pierre Hansen

Citations in the corpus

• Structured Learning and Prediction in Computer Vision (2011, methodology): “In practise another popular method originally proposed by Barahona and Anbil [8] to obtain an approximate primal solution is to take the average of all primal iterates as a geometric series and thus obtaining a moving average ȳt as approximate primal solution, ȳ = γy(λ,μ) + (1 − γ)ȳt−1, (4.”
• Hybrid metaheuristics in combinatorial optimization: A survey (2011, methodology): “The volume algorithm is used for solving the Lagrangian dual [138].”
• Approximate Primal Solutions and Rate Analysis for Dual Subgradient Methods (2008, methodology): “A dual subgradient method producing primal solutions, the volume algorithm, for linear problems have been proposed by Barahona and Anbil [1].”
• Selected Topics in Column Generation (2002, background): “Assuming P is nonempty and bounded, the volume algorithm (Barahona and Anbil 2000) is an extension of subgradient algorithms, and rapidly produces primal as well as dual approximate solutions to the RMP.”
• The p-median problem: A survey of metaheuristic approaches (2005, methodology): “Senne & Lorena (2000), Barahona & Anbil (2000) , Beltran et al. (2004).”
• MRF Energy Minimization and Beyond via Dual Decomposition (2011, positive): “An example of a method on this topic is the Volume algorithm proposed by Barahona and Anbil [1], which can essentially be viewed as fast approximation to the Dantzig-Wolfe decomposition method, and produces primal solutions by computing the volume below the faces that are active at an optimal dual s”