Output-sensitive Complexity of Multiobjective Combinatorial Optimization
October 23, 2016 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Fritz BΓΆkler, Matthias Ehrgott, Christopher Morris, Petra Mutzel
arXiv ID
1610.07204
Category
math.OC: Optimization & Control
Cross-listed
cs.CC,
cs.DS
Citations
33
Venue
arXiv.org
Last Checked
2 months ago
Abstract
We study output-sensitive algorithms and complexity for multiobjective combinatorial optimization problems. In this computational complexity framework, an algorithm for a general enumeration problem is regarded efficient if it is output-sensitive, i.e., its running time is bounded by a polynomial in the input and the output size. We provide both practical examples of MOCO problems for which such an efficient algorithm exists as well as problems for which no efficient algorithm exists under mild complexity theoretic assumptions.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Optimization & Control
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Local SGD Converges Fast and Communicates Little
R.I.P.
π»
Ghosted
On Lazy Training in Differentiable Programming
R.I.P.
π»
Ghosted
A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications
R.I.P.
π»
Ghosted
Learned Primal-dual Reconstruction
R.I.P.
π»
Ghosted
On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Language Models are Few-Shot Learners
R.I.P.
π»
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
π»
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
π»
Ghosted