Network optimization : continuous and discrete models

By Dimitri P. Bertsekas. 1998

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Citations in the corpus

Distributed Statistical Machine Learning in Adversarial Settings (2017, background): “In practice, there are standard guidances in choosing the initial point [29].”
Product Manifold Filter: Non-rigid Shape Correspondence via Kernel Density Estimation in the Product Space (2017, unknown): “Despite the common wisdom, we demonstrate that the problem is efficiently solvable for relatively densely sampled shapes by means of the well-established auction algorithm [9] and a simple multi-scale approach.”
Distributed Constrained Optimization by Consensus-Based Primal-Dual Perturbation Method (2013, background): “Formulation (2) also encompasses the network flow control problems [34]; see [35] for an example which considered maximizing the network lifetime.”
Refined Second Law of Thermodynamics for Fast Random Processes (2012, methodology): “The quadratic cost function corresponding to the optimal Lagrangian map xi → xf (xi) 1 kBT Kmin ≈ 1.996 s (7.11) (the Auction Algorithm produced a value lower by 0.07 %, giving an idea about the accuracy of our calculations).”
Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning (2019, background): “tween arbitrary distributions. Computing the Wasserstein distance between two discrete distributions amounts to solving a tractable linear program that is susceptible to the network simplex algorithm [7] as well as dual ascent methods [9] or specialized auction algorithms [5,6], etc. The set of feas”
Simultaneous routing and resource allocation via dual decomposition (2004, background): “Such models are widely used in the literature of network routing and optimization (see, e.g., [1], [2], [6]).”
Phase Unwrapping via Graph Cuts (2005, background): “The worst case complexity for augmenting path algorithms is O(n(2)m) [58], where n and m are the number of vertices and edges, respectively.”
Structured Prediction, Dual Extragradient and Bregman Projections (2006, background): “In case of alignment, Z i is the convex hull of the bipartite matching polytope and the projections onto Z i reduce to the much-studied minimum cost quadratic flow problem (Bertsekas, 1998).”
An algebraic approach to network coding (2001, methodology): “The Ford–Fulkerson labeling algorithm [16] gives a way for finding a solution for point-to-point connections provided a network problem is solvable.”
Optimization approach to real power tracing: an application to transmission fixed cost allocation (2006, methodology): “We develop a multicommodity network flow model [20] that can model the complete space of tracing solutions.”