50 Years of Integer Programming 1958-2008 - From the Early Years to the State-of-the-Art

581 citations recorded by the source corpus.

How researchers see this paper

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

Matching for Balance (2019, methodology): “Matching for balance exemplifies how tools from modern optimization (e.g., Jünger et al. 2009 and Bixby 2012) can play a central role in the design of observational studies in general (e.g., Rosenbaum 2002 and Imbens and Rubin 2015) and in matching for aggregate covariate balance in particular…”
Algorithms and Software for Convex Mixed Integer Nonlinear Programs (2012, methodology): “If there is no feasible solution to (NLP(x̂I)), a feasibility cut can be obtained similarly by using the solution x to (NLPF(x̂I)) and corresponding multipliers λ: λ T [g(x) +∇Ig(x)T (xI − x̂I)] ≤ 0.”
Learning to Search with MCTSnets (2018, background): “Many success stories in artiﬁcial intelligence are based on the application of powerful tree search algorithms to challenging planning problems (Samuel, 1959; Knuth & Moore, 1975; J¨unger et al., 2009).”
Solving Mixed Integer Programs Using Neural Networks (2020, background): “O C ] 2 3 Ju l 2 02 1 of applications such as capacity planning, resource allocation, bin packing, etc. (Taha 2014, Jünger et al. 2009, Sierksma and Zwols 2015).”
The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization (2015, background): “Consequently, estimator β̂ P satisfies the oracle property in the sense of Fan and Li [20].”