Combinatorial optimization:Algorithms and complexity

1984

Citations in the corpus

• Learning Combinatorial Optimization Algorithms over Graphs (2017, neutral): “These algorithms are specifically designed for each type of problem: • MVC: MVCApprox iteratively selects an uncovered edge and adds both of its endpoints [30].”
• Coordinated Multi-Agent Imitation Learning (2017, neutral): “Our inference technique rests on the well-known Linear Assignment Problem (Papadimitriou & Steiglitz, 1982), which is solved optimally via the Kuhn-Munkres algorithm.”
• A Taxi Order Dispatch Model based On Combinatorial Optimization (2017, unknown): “Œis is a constrained combinatorial optimization problem [18].”
• The impact of imperfect scheduling on cross-Layer congestion control in wireless networks (2006, unknown): “) An -complexity algorithm for MWM can be found in [30], where is the number of nodes.”
• Lyapunov-Guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks (2020, unknown): “Other search based methods, such as branch-and-bound and block coordinate descent [29], are also time-consuming when N is large.”
• Guided Local Search (2010, unknown): “It can be seen as a GA with GLS to bring it out of local optimum: if no progress has been made after a specific of iterations (this number is a parameter to GGA), GLS modifies the fitness function (which is the objective function) by means of penalties, using the criteria defined in equation (3).”
• The generalized minimum spanning tree problem (2002, unknown): “The following concepts are taken from [57].”
• On the rank of a tropical matrix (2003, unknown): “The key step is to find a permutation that achieves the minimum in the determinantal tropical sum, which is the assignment problem in combinatorial optimization [20].”
• A Multilevel Algorithm for Force-Directed Graph Drawing (2000, unknown): “As such we seek a fast and efficient (i.e. not necessarily optimal) algorithm that is G gradual – if too many vertices are clustered together in one step it may depreciate the benefits of the multilevel paradigm and in particular inhibit the force directed placement algorithm, as applied to”
• The impact of imperfect scheduling on cross-layer rate control in wireless networks (2005, unknown): “) An O(N(3))-complexity algorithm for MWM can be found in [19], where N is the number of nodes.”
• Quantized incremental algorithms for distributed optimization (2005, unknown): “In graph theoretic terms such a cycle is known as a Hamilton cycle, and the problem of determining whether a graph contains a Hamilton cycle is known to be NP-complete [10].”
• Shape Matching and Object Recognition Using Shape Contexts (2002, unknown): “This is an instance of the square assignment (or weighted bipartite matching) problem, which can be solved in O(N(3)) time using the Hungarian method [42].”
• Holographic quantum error-correcting codes: toy models for the bulk/boundary correspondence (2015, unknown): “The key to the argument, explained in appendix B, is using a strengthened version of the max-flow min-cut theorem (which is standard in graph theory [33]) to establish that the tensor network representations of P and Q can be interpreted as unitary quantum circuits.”
• Planning algorithms (2006, unknown): “The lattices developed in this section were introduced in [292] for analyzing the kinodynamic planning problem in the rigorous approximation algorithm framework for NP-hard problems [768].”