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       <li>[[Facility location problem]]</li>
       <li>[[Facility location problem]]</li>
       <li>[[Traveling salesman problem]]</li>
       <li>[[Traveling salesman problem]]</li>
      <li>[[Portfolio optimization]]</li>
       <li>[[Set covering problem]]</li>
       <li>[[Set covering problem]]</li>
      <li>[[Unit commitment problem]]</li>
       <li>[[Quadratic assignment problem]]</li>
       <li>[[Quadratic assignment problem]]</li>
      <li>[[Job shop scheduling]]</li>
       <li>[[Newsvendor problem]]</li>  
       <li>[[Newsvendor problem]]</li>  
      <li>[[Unit commitment problem]]</li>
      <li>[[Portfolio optimization]]</li>
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       <li>[[Heuristic algorithms]]</li>
       <li>[[Heuristic algorithms]]</li>
       <li>[[Branch and cut]]</li>
       <li>[[Branch and cut]]</li>
       <li>[[Local branching]]</li>
       <li>[[Local branching]]</li></div>
      <li>[[Feasibility pump]]</li>
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       <li>[[Signomial problems]]</li>
       <li>[[Signomial problems]]</li>
       <li>[[Mixed-integer linear fractional programming (MILFP)]]</li>
       <li>[[Mixed-integer linear fractional programming (MILFP)]]</li>
       <li>[[Convex Generalized disjunctive programming (GDP)]]</li>
       <li>[[Convex generalized disjunctive programming (GDP)]]</li>
       <li>[[Nonconvex Generalized disjunctive programming (GDP)]]</li>
       <li>[[Nonconvex generalized disjunctive programming (GDP)]]</li>
       <li>[[Branch and bound (BB) for MINLP]]</li>
       <li>[[Branch and bound (BB) for MINLP]]</li>
       <li>[[Branch and cut for MINLP]]</li>
       <li>[[Branch and cut for MINLP]]</li>
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       <li>[[Fuzzy programming]]</li>
       <li>[[Fuzzy programming]]</li>
       <li>[[Classical robust optimization]]</li>
       <li>[[Classical robust optimization]]</li>
      <li>[[Distributionally robust optimization]]</li>
       <li>[[Adaptive robust optimization]]</li>
       <li>[[Adaptive robust optimization]]</li>
       <li>[[Data driven robust optimization]]</li>
       <li>[[Data driven robust optimization]]</li>
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       <li>[[RMSProp]]</li>
       <li>[[RMSProp]]</li>
       <li>[[Adam]]</li>
       <li>[[Adam]]</li>
      <li>[[Alternating direction method of multiplier (ADMM)]]</li>
       <li>[[Frank-Wolfe]]</li>
       <li>[[Frank-Wolfe]]</li>
      <li>[[Sparse Reconstruction with Compressed Sensing]]</li>
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      <li>[[Protein folding problem]]</li>
       <li>[[Wing shape optimization]]</li>
       <li>[[Wing Shape Optimization]]</li>
       <li>[[Optimization in game theory]]</li>
       <li>[[Optimization in Game Theory]]</li>
       <li>[[Quantum computing for optimization]]</li>
       <li>[[Quantum computing for optimization]]</li>
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Revision as of 12:27, 1 April 2022

Welcome to the Cornell University Computational Optimization Open Textbook

This electronic textbook is a student-contributed open-source text covering a variety of topics on process optimization.
If you have any comments or suggestions on this open textbook, please contact Professor Fengqi You.

Linear Programming (LP)

NonLinear Programming (NLP)

Deterministic Global Optimization

Dynamic Programming

Traditional Applications

Mixed-Integer Linear Programming (MILP)

Mixed-Integer NonLinear Programming (MINLP)

Optimization under Uncertainty

Optimization for Machine Learning and Data Analytics

Emerging Applications

Cornell Prof. Fengqi You Research Group


Retrieved from "https://optimization.cbe.cornell.edu/index.php?title=Main_Page&oldid=6677"