Main Page: Difference between revisions

Latest revision as of 20:45, 9 October 2023

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)
Duality Simplex algorithm Computational complexity Network flow problem Interior-point method for LP Optimization with absolute values Matrix game (LP for game theory)
NonLinear Programming (NLP)
Line search methods Trust-region methods Interior-point method for NLP Conjugate gradient methods Quasi-Newton methods Quadratic programming Sequential quadratic programming Subgradient optimization Mathematical programming with equilibrium constraints Dynamic optimization Geometric programming Nondifferentiable Optimization
Deterministic Global Optimization
Exponential transformation Logarithmic transformation McCormick envelopes Piecewise linear approximation Spatial branch and bound method
Dynamic Programming
Markov decision process Bellman equation Eight step procedures Stochastic dynamic programming
Traditional Applications
Facility location problem Traveling salesman problem Set covering problem Quadratic assignment problem Job shop scheduling Newsvendor problem Unit commitment problem Portfolio optimization

Mixed-Integer Linear Programming (MILP)
Mixed-integer cuts Disjunctive inequalities Lagrangean duality Column generation algorithms Heuristic algorithms Branch and cut Local branching
Mixed-Integer NonLinear Programming (MINLP)
Signomial problems Mixed-integer linear fractional programming (MILFP) Convex generalized disjunctive programming (GDP) Nonconvex generalized disjunctive programming (GDP) Branch and bound (BB) for MINLP Branch and cut for MINLP Generalized Benders decomposition (GBD) Outer-approximation (OA) Extended cutting plane (ECP)
Optimization under Uncertainty
Stochastic programming Chance-constraint method Fuzzy programming Classical robust optimization Adaptive robust optimization Data driven robust optimization
Optimization for Machine Learning and Data Analytics
Stochastic gradient descent Momentum AdaGrad RMSProp Adam Frank-Wolfe Sparse Reconstruction with Compressed Sensing
Emerging Applications
Wing shape optimization Optimization in game theory Quantum computing for optimization

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        <li>[[Duality]]</li>
        <li>[[Simplex algorithm]]</li>
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        <li>[[Line search methods]]</li>
        <li>[[Trust-region methods]]</li>
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        <li>[[Exponential transformation]]</li>
        <li>[[Logarithmic transformation]]</li>
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 |-
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+| style="color:#000;padding:2px 5px 5px 15px" | <div id="mp-dyk">
        <li>[[Markov decision process]]</li>
        <li>[[Bellman equation]]</li>
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 |-
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        <li>[[Facility location problem]]</li>
        <li>[[Traveling salesman problem]]</li>
-      <li>[[Portfolio optimization]]</li>
        <li>[[Set covering problem]]</li>
-      <li>[[Unit commitment problem]]</li>
        <li>[[Quadratic assignment problem]]</li>
+      <li>[[Job shop scheduling]]</li>
        <li>[[Newsvendor problem]]</li>
+      <li>[[Unit commitment problem]]</li>
+      <li>[[Portfolio optimization]]</li>
 </div>
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 |-
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        <li>[[Mixed-integer cuts]]</li>
        <li>[[Disjunctive inequalities]]</li>
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        <li>[[Heuristic algorithms]]</li>
        <li>[[Branch and cut]]</li>
-       <li>[[Local branching]]</li>
+       <li>[[Local branching]]</li></div>
-      <li>[[Feasibility pump]]</li>
-</div>
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 |-
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        <li>[[Signomial problems]]</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 cut for MINLP]]</li>
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        <li>[[Stochastic programming]]</li>
        <li>[[Chance-constraint method]]</li>
        <li>[[Fuzzy programming]]</li>
        <li>[[Classical robust optimization]]</li>
-      <li>[[Distributionally robust optimization]]</li>
        <li>[[Adaptive robust optimization]]</li>
        <li>[[Data driven robust optimization]]</li>
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        <li>[[Stochastic gradient descent]]</li>
        <li>[[Momentum]]</li>
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        <li>[[RMSProp]]</li>
        <li>[[Adam]]</li>
-      <li>[[Alternating direction method of multiplier (ADMM)]]</li>
        <li>[[Frank-Wolfe]]</li>
+      <li>[[Sparse Reconstruction with Compressed Sensing]]</li>
 </div>
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-      <li>[[Protein folding problem]]</li>
        <li>[[Wing shape optimization]]</li>
        <li>[[Optimization in game theory]]</li>

Main Page: Difference between revisions

Latest revision as of 20:45, 9 October 2023

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

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