Integer-Programming Software Systems

By Alper Atamtürk, Martin W. P. Savelsbergh. 2005

232 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Industrial and Manufacturing Engineering, Management Science and Operations Research

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Citations in the corpus

Optimal Decision Trees for the Algorithm Selection Problem: Integer Programming Based Approaches (2019, methodology): “The LP solver is the main component in Integer Programming solvers (Atamtürk and Savelsbergh, 2005) and it is executed at every node of the search tree.”
JuMP: A Modeling Language for Mathematical Optimization (2015, background): “A number of algorithms including branch-and-bound [5], Benders decomposition [81], and cutting-plane methods derive their efficiency from the fact that when solving a sequence of linear programming problems, one can “hot-start” the simplex algorithm from the previous optimal solution when new constr”
Integer and Combinatorial Optimization (2013, background): “In the early years of integer optimization, considerable research activity was focused on identifying part (or all) of the list of facets for specific combinatorial optimization problems by exploiting the special structure of conv(S) (Balas and Padberg, 1972; Balas, 1975; Bauer et al., 2002; Hammer ”
Learning-based optimization of cache content in a small cell base station (2014, background): “Knapsack problems, which are known to be NP-hard, can be solved optimally using branching algorithms, such as branch and bound (B&B) [15].”
A Survey on Energy-Efficient Train Operation for Urban Rail Transit (2016, methodology): “For example, the train mass is assumed as a constant parameter; the energy conversion efficiency is considered as a deterministic parameter; and the calculation of energy consumption is simplified by using mechanical energy equations but not modeling the power network.”
Progress in presolving for mixed integer programming (2015, methodology): “Williams [29] pointed out a projection method for the elimination of integer variables and Savelsbergh [26] investigated preprocessing and probing techniques for mixed integer programming problems.”
Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers (2012, methodology): “Possible methods to improve scalability include the use of state of the art solver software, a topic discussed in-depth by Atamtürk and Savelsbergh [16].”
Optimal trajectory planning for trains under fixed and moving signaling systems using mixed integer linear programming (2014, background): “…integer linear programming (MILP) problem, which31 can be solved efficiently using existing commercial and free solvers (Linderoth & Ralphs, 2005;32 Atamtürk & Savelsbergh, 2005) that guarantee finding the global optimum of the MILP problem.33 However, the approaches mentioned above ignore the…”
Branch-and-bound algorithms: A survey of recent advances in searching, branching, and pruning (2016, unknown): “Reusing the optimal basis and its LU factorization when solving the child LP is known as hot starting, while reusing only the optimal basis is known as warm starting [34,35].”