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'''Welcome to the Northwestern University Process Optimization Open Textbook.''' <br />
 
'''Welcome to the Northwestern University Process Optimization Open Textbook.''' <br />
This electronic textbook is a student-contributed open-source text covering the materials used in the process optimization course CHEM_ENG 345 at Northwestern University, Evanston, IL, USA
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This electronic textbook is a student-contributed open-source text covering a variety of topics on process optimization.
 
<br />
 
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If you have any comments or suggestions on this open textbook, please contact [//www.mccormick.northwestern.edu/directory/profiles/Fengqi-You.html  Professor Fengqi You].
 
If you have any comments or suggestions on this open textbook, please contact [//www.mccormick.northwestern.edu/directory/profiles/Fengqi-You.html  Professor Fengqi You].

Revision as of 15:13, 22 March 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



  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
  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




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