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Revision as of 16:51, 21 December 2021

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 Feasibility pump
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 Distributionally robust optimization Adaptive robust optimization Data driven robust optimization
Optimization for Machine Learning and Data Analytics
Stochastic gradient descent Momentum AdaGrad RMSProp Adam Alternating direction method of multiplier (ADMM) Frank-Wolfe Sparse Reconstruction with Compressed Sensing
Emerging Applications
Protein folding problem Wing shape optimization Optimization in game theory Bayesian optimization Quantum computing for optimization

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 ! style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#cef2e0; font-size:120%; font-weight:bold; border:1px solid #a3bfb1; text-align:left; color:#000; padding:0.2em 0.4em;">Linear Programming (LP)</h2>
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        <li>[[Duality]]</li>
        <li>[[Simplex algorithm]]</li>
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 ! style="padding:2px" | <h2 id="mp-dyk-h2" style="margin:3px; background:#cef2e0; font-size:120%; font-weight:bold; border:1px solid #a3bfb1; text-align:left; color:#000; padding:0.2em 0.4em;">NonLinear Programming (NLP)</h2>
 |-
-| style="color:#000;padding:2px 5px 5px 15px" | <div id="mp-dyk">
+| style="color:#000;padding:2px 5px 5px" | <div id="mp-dyk">
        <li>[[Line search methods]]</li>
        <li>[[Trust-region methods]]</li>
@@ Line 40: / Line 40: @@
        <li>[[Geometric programming]]</li>
        <li>[[Nondifferentiable Optimization]]</li>
-      <li>[[Evolutionary multimodal optimization]]</li>
-      <li>[[Stackelberg leadership model]]</li>
-      <li>[[Quadratic constrained quadratic programming]]</li>
-      <li>[[Derivative free optimization]]</li>
 </div>
 |-
 ! style="padding:2px" | <h2 id="mp-dyk-h2" style="margin:3px; background:#cef2e0; font-size:120%; font-weight:bold; border:1px solid #a3bfb1; text-align:left; color:#000; padding:0.2em 0.4em;">Deterministic Global Optimization</h2>
 |-
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+| style="color:#000;padding:2px 5px 5px" | <div id="mp-dyk">
        <li>[[Exponential transformation]]</li>
        <li>[[Logarithmic transformation]]</li>
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 ! style="padding:2px" | <h2 id="mp-dyk-h2" style="margin:3px; background:#cef2e0; font-size:120%; font-weight:bold; border:1px solid #a3bfb1; text-align:left; color:#000; padding:0.2em 0.4em;">Dynamic Programming</h2>
 |-
-| style="color:#000;padding:2px 5px 5px 15px" | <div id="mp-dyk">
+| style="color:#000;padding:2px 5px 5px" | <div id="mp-dyk">
        <li>[[Markov decision process]]</li>
        <li>[[Bellman equation]]</li>
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 ! style="padding:2px" | <h2 id="mp-dyk-h2" style="margin:3px; background:#cef2e0; font-size:120%; font-weight:bold; border:1px solid #a3bfb1; text-align:left; color:#000; padding:0.2em 0.4em;">Traditional Applications</h2>
 |-
-| style="color:#000;padding:2px 5px 5px 15px" | <div id="mp-dyk">
+| style="color:#000;padding:2px 5px 5px" | <div id="mp-dyk">
        <li>[[Facility location problem]]</li>
        <li>[[Traveling salesman problem]]</li>
@@ Line 76: / Line 72: @@
        <li>[[Unit commitment problem]]</li>
        <li>[[Portfolio optimization]]</li>
-      <li>[[A-star algorithm]]</li>
-</div>
-|-
-! style="padding:2px" | <h2 id="mp-dyk-h2" style="margin:3px; background:#cef2e0; font-size:120%; font-weight:bold; border:1px solid #a3bfb1; text-align:left; color:#000; padding:0.2em 0.4em;"> Emerging Applications</h2>
-|-
-| style="color:#000;padding:2px 5px 5px 15px" | <div id="mp-dyk">
-      <li>[[Wing shape optimization]]</li>
-      <li>[[Optimization in game theory]]</li>
-      <li>[[Quantum computing for optimization]]</li>
 </div>
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+<!--        IN THE NEWS; ON THIS DAY        -->
 | class="MainPageBG" style="width:50%; border:1px solid #cedff2; background:#f5faff; vertical-align:top;"|
 {| id="mp-right" style="width:100%; vertical-align:top; background:#f5faff;"
 ! style="padding:2px" | <h2 id="mp-otd-h2" style="margin:3px; background:#cedff2; font-size:120%; font-weight:bold; border:1px solid #a3b0bf; text-align:left; color:#000; padding:0.2em 0.4em;">Mixed-Integer Linear Programming (MILP)</h2>
 |-
-| style="color:#000;padding:2px 5px 5px 15px" | <div id="mp-otd">
+| style="color:#000;padding:2px 5px 5px" | <div id="mp-otd">
        <li>[[Mixed-integer cuts]]</li>
        <li>[[Disjunctive inequalities]]</li>
@@ Line 102: / Line 89: @@
        <li>[[Heuristic algorithms]]</li>
        <li>[[Branch and cut]]</li>
-       <li>[[Local branching]]</li></div>
+       <li>[[Local branching]]</li>
+      <li>[[Feasibility pump]]</li>
+</div>
 |-
 ! style="padding:2px" | <h2 id="mp-otd-h2" style="margin:3px; background:#cedff2; font-size:120%; font-weight:bold; border:1px solid #a3b0bf; text-align:left; color:#000; padding:0.2em 0.4em;">Mixed-Integer NonLinear Programming (MINLP)</h2>
 |-
-| style="color:#000;padding:2px 5px 5px 15px" | <div id="mp-otd">
+| style="color:#000;padding:2px 5px 5px" | <div id="mp-otd">
        <li>[[Signomial problems]]</li>
        <li>[[Mixed-integer linear fractional programming (MILFP)]]</li>
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 |-
-| style="color:#000;padding:2px 5px 5px 15px" | <div id="mp-dyk">
+| style="color:#000;padding:2px 5px 5px" | <div id="mp-dyk">
        <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>
-      <li>[[Adadelta]]</li>
-      <li>[[Adafactor]]</li>
-      <li>[[AdamW]]</li>
-      <li>[[Adamax]]</li>
-      <li>[[FTRL algorithm]]</li>
-      <li>[[Lion algorithm]]</li>
-      <li>[[LossScaleOptimizer]]</li>
-      <li>[[Nadam]]</li>
 </div>
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+! style="padding:2px" | <h2 id="mp-otd-h2" style="margin:3px; background:#cedff2; font-size:120%; font-weight:bold; border:1px solid #a3b0bf; text-align:left; color:#000; padding:0.2em 0.4em;">Emerging Applications</h2>
 |-
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+| style="color:#000;padding:2px 5px 5px" | <div id="mp-dyk">
+      <li>[[Protein folding problem]]</li>
+      <li>[[Wing shape optimization]]</li>
+      <li>[[Optimization in game theory]]</li>
        <li>[[Bayesian optimization]]</li>
-       <li>[[Genetic algorithm]]</li>
+       <li>[[Quantum computing for optimization]]</li>
-      <li>[[Simulated annealing]]</li>
-      <li>[[Particle swarm optimization]]</li>
-      <li>[[Differential evolution]]</li>
 </div>
 |}

Main Page: Difference between revisions

Revision as of 16:51, 21 December 2021

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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