A global MINLP approach to symbolic regression

DOI10.1007/s10107-018-1289-xzbMath1402.90092OpenAlexW2803008420MaRDI QIDQ1650769

Alison Cozad, Nikolaos V. Sahinidis

Publication date: 13 July 2018

Published in: Mathematical Programming. Series A. Series B (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10107-018-1289-x

zbMATH Keywords

global optimizationmachine learningsymbolic regressioninteger nonlinear optimization

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) General nonlinear regression (62J02) Integer programming (90C10) Nonconvex programming, global optimization (90C26) Learning and adaptive systems in artificial intelligence (68T05) Theory of computing (68Q99)

Related Items (1)

Optimality-based domain reduction for inequality-constrained NLP and MINLP problems

Uses Software

• KNITRO
• SCIP
• LINDO
• Bonmin
• AlphaECP
• LINDOGlobal
• ANTIGONE
• DataModeler
• Eureqa

Cites Work

• An algorithmic framework for convex mixed integer nonlinear programs
• A polyhedral branch-and-cut approach to global optimization
• Review of nonlinear mixed-integer and disjunctive programming techniques
• Solving pseudo-convex mixed integer optimization problems by cutting plane techniques
• ANTIGONE: algorithms for coNTinuous/Integer global optimization of nonlinear equations
• Branching and bounds tighteningtechniques for non-convex MINLP
• The global solver in the LINDO API
• Introduction to Derivative-Free Optimization
• An outer-approximation algorithm for a class of mixed-integer nonlinear programs
• A new look at the statistical model identification
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