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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.



Cornell University Open Text Book on Computational Optimization


  Linear Programming (LP)
  1. Computational complexity
  2. Matrix game (LP for game theory)
  3. Network flow problem
  4. Interior-point method for LP
  5. Optimization with absolute values
  Mixed-Integer Linear Programming (MILP)
  1. Facility location problems
  2. Traveling salesman problems
  3. Mixed-integer cuts
  4. Disjunctive inequalities
  5. Lagrangean duality
  6. Column generation algorithms
  7. Heuristic algorithms
  8. Branch and cut



  NonLinear Programming (NLP)
  1. Line search methods
  2. Trust-region methods
  3. Interior-point method for NLP
  4. Conjugate gradient methods
  5. Quasi-Newton methods
  6. Quadratic programming
  7. Sequential quadratic programming
  8. Subgradient optimization
  9. Mathematical programming with equilibrium constraints
  10. Dynamic optimization
  11. Geometric programming
  12. Nondifferentiable Optimization
  Mixed-Integer NonLinear Programming (MINLP)
  1. Signomial problems
  2. Mixed-integer linear fractional programming (MILFP)
  3. Convex Generalized disjunctive programming (GDP)
  4. Nonconvex Generalized disjunctive programming (GDP)
  5. Branch and bound (BB)
  6. Branch and cut for MINLP
  7. Generalized Benders decomposition (GBD)
  8. Outer-approximation (OA)
  9. Extended cutting plane (ECP)



  Global Optimization
  1. Exponential transformation
  2. Logarithmic transformation
  3. McCormick envelopes
  4. Piecewise linear approximation
  5. Spatial branch and bound method
  Optimization under Uncertainty
  1. Stochastic programming
  2. Chance-constraint method
  3. Fuzzy programming
  4. Classical robust optimization
  5. Adaptive robust optimization
  6. Data driven robust optimization




  Optimization for Machine Learning and Data Analytics
  1. Stochastic gradient descent
  2. Moment
  3. RMSProp
  4. ADAM
  Featured Applications
  1. Wing Shape Optimization
  2. Applying Optimization in Game Theory




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