Primal Heuristics in SCIP - ZIB

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1 Introduction Basics Integration Into SCIP 2 Available Heuristics Rounding Heuristics (Objective) Diving LNS & Others 3 Remarks & Results Outline 26 / 34
LNS heuristics Idea: Create subMIP by fixing variables or adding constraints Approaches ⊲ Rins: fix variables equal in LP optimum and incumbent ⊲ Crossover: fix variables equal in different feasible solutions ⊲ Mutation: fix variables randomly ⊲ Local Branching: add distance constraint wrt. incumbent 27 / 34
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DFG Research Center MATHEON Mathema
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1 Introduction Basics Integration I
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Exact methods ⊲ Branch-And-Bound
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Why use heuristics inside an exact
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1 Introduction Basics Integration I
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Domain Propagation Solve LP Constra
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Primal Heuristics Domain Propagatio
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Two main categories ⊲ Start heuri
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Five main approaches ⊲ Rounding a
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Statistics & points ⊲ Variables
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Page 35 and 36: Statistics & points ⊲ Variables
Page 49 and 50: 1 Introduction Basics Integration I
Page 51 and 52: Guideline: Stay feasible! Features
Page 63 and 64: Algorithm 1. ¯x ← LP optimum; 2.
Page 69 and 70: Observations Rens: An LNS Rounding
Page 71 and 72: Idea ⊲ Alternately solve the LP a
Page 73 and 74: Algorithm 1. Solve LP; The Feasibil
Page 83 and 84: Objective Feasibility Pump (Achterb
Page 85: Other objective divers ⊲ Objectiv
Page 89 and 90: Combinatorial heuristics ⊲ 1-Opt
Page 95 and 96: Some tips and tricks ⊲ Use limits
Page 97 and 98: Some tips and tricks ⊲ Use limits
Page 99 and 100: Single heuristics Remarks & Conclus
Page 101 and 102: Single heuristics Remarks & Conclus
Page 103: DFG Research Center MATHEON Mathema
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