Integer Programming¶
Wolsey, L. A. (1998). Integer Programming. Wiley.
Cited by¶
3 citations across 3 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Branch and Bound
- The sophistication of the bounding procedure directly controls pruning effectiveness — stronger bounds prune more (as demonstrated in foundational theory by Wolsey (1998)
This sourcestandard graduate text developing branch-and-bound, cutting planes, and strong valid inequalities, including bound-strengthening through valid inequalities.
- The sophistication of the bounding procedure directly controls pruning effectiveness — stronger bounds prune more (as demonstrated in foundational theory by Wolsey (1998)
- Integer Linear Programming (ILP)
- In machine learning and statistics, ILP and its relaxations support feature selection, clustering, classification, and various other problems with discrete structure — Wolsey's (1998) Integer Programming text remains the standard graduate-level entry point to these tools.
This sourceStandard graduate textbook on integer programming; develops branch-and-bound, cutting planes, and branch-and-cut as the core algorithmic toolkit and discusses bound-strengthening through valid inequalities.
- In machine learning and statistics, ILP and its relaxations support feature selection, clustering, classification, and various other problems with discrete structure — Wolsey's (1998) Integer Programming text remains the standard graduate-level entry point to these tools.
- Linear Programming (LP)
- The LP framework has been extended to handle many practical complications: stochastic crude-price uncertainty (through stochastic programming or scenario-based LP); integer unit-on/off decisions (through the mixed-integer extensions catalogued by Wolsey (1998)); nonlinear blending rules (through nonlinear solver integration or MINLP); and multi-period optimization (through multi-period LPs or dynamic programming) — all building on polynomial-time solvability results due to Khachiyan (1979) and Karmarkar (1984).
This sourceStandard graduate textbook on integer programming; develops branch-and-bound, cutting planes, and branch-and-cut as the core algorithmic toolkit and discusses bound-strengthening through valid inequalities.
- The LP framework has been extended to handle many practical complications: stochastic crude-price uncertainty (through stochastic programming or scenario-based LP); integer unit-on/off decisions (through the mixed-integer extensions catalogued by Wolsey (1998)); nonlinear blending rules (through nonlinear solver integration or MINLP); and multi-period optimization (through multi-period LPs or dynamic programming) — all building on polynomial-time solvability results due to Khachiyan (1979) and Karmarkar (1984).
Verification¶
Does it exist? Not checked yet. This entry carries no identifier to resolve. It was extracted from the citation as written in the article, normalized, and deduplicated against the rest of the registry.
Does it back the claim? Not recorded. Neither this nor any other of the 3 citations of this work carries a recorded support check.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Registry ID ref:29759d486c44 · see in the full table