Difference between revisions of "Main Page"

From optimization
Jump to: navigation, search
Line 94: Line 94:
<br />
<br />
<br />
[[Image:Centennial Logo.jpg|150px|x]]
[[Image:Centennial Logo.jpg|150px|x]]

Revision as of 17:35, 20 June 2015

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. 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

  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

  Non-gradient Optimization
  1. Nondifferentiable Optimization

  Featured Applications
  1. Wing Shape Optimization
  2. Applying Optimization in Game Theory
  3. This is a Sample Page