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# [[Generalized disjunctive programming (GDP)]]
# [[Generalized disjunctive programming (GDP)]]
# [[Branch and bound (BB)]]
# [[Branch and bound (BB)]]
# [[Branch and cut]]
# [[Branch and cut for MINLP]]
# [[Generalized Benders decomposition (GBD)]]
# [[Generalized Benders decomposition (GBD)]]
# [[Outer-approximation (OA)]]
# [[Outer-approximation (OA)]]

Revision as of 11:32, 26 May 2014

Welcome to the Northwestern University Process 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.

Northwestern University Open Text Book on Process 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. Conjugate gradient methods
  4. Quasi-Newton methods
  5. Quadratic programming
  6. Sequential quadratic programming
  7. Subgradient optimization
  8. Mathematical programming with equilibrium constraints
  9. Dynamic optimization

  Mixed-Integer NonLinear Programming (MINLP)
  1. Signomial problems
  2. Mixed-integer linear fractional programming (MILFP)
  3. Generalized disjunctive programming (GDP)
  4. Branch and bound (BB)
  5. Branch and cut for MINLP
  6. Generalized Benders decomposition (GBD)
  7. Outer-approximation (OA)
  8. 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. Robust optimization
  3. Chance-constraint method
  4. Fuzzy programming
  5. Example page

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