Difference between revisions of "Main Page"

From Cornell University Computational Optimization Open Textbook - Optimization Wiki
Jump to navigation Jump to search
 
(28 intermediate revisions by the same user not shown)
Line 1: Line 1:
<!-- Header table. Introduction. -->
+
{| id="mp-topbanner" style="width:100%; background:#f6f6f6; margin-top:1.2em; border:1px solid #ddd;"
'''Welcome to the Cornell University Computational Optimization Open Textbook.''' <br />
+
| style="width:61%; color:#000;" |
This electronic textbook is a student-contributed open-source text covering a variety of topics on process optimization.
+
{| style="width:100%; border:none; background:none;"
<br />
+
| style="text-align:center; white-space:nowrap; color:#000;" |
If you have any comments or suggestions on this open textbook, please contact [//https://www.engineering.cornell.edu/faculty-directory/fengqi-you  Professor Fengqi You].
+
<div style="font-size:162%; border:none; margin:0; padding:.1em; color:#000;">Welcome to the Cornell University Computational Optimization Open Textbook</div>
<br />
 
----
 
<br /><br />
 
<font size="6">Cornell Open Textbook on Computational Optimization</font>
 
  
{| class="wikitable" style="padding: 1em; text-align:left"
+
This electronic textbook is a student-contributed open-source text covering a variety of topics on process optimization.<br />
|- valign="top"
+
'''If you have any comments or suggestions on this open textbook, please contact [https://www.engineering.cornell.edu/faculty-directory/fengqi-you  Professor Fengqi You].'''
|width = "400pt"|<br />'''&nbsp;&nbsp;Linear Programming (LP)'''
+
|}
# [[Duality]]
+
|}
# [[Computational complexity]]
 
# [[Network flow problem]]
 
# [[Interior-point method for LP]]
 
# [[Optimization with absolute values]]
 
# [[Matrix game (LP for game theory)]]
 
<br />
 
 
 
|width = "400pt"|<br />'''&nbsp;&nbsp;Mixed-Integer Linear Programming (MILP)'''
 
# [[Mixed-integer cuts]]
 
# [[Disjunctive inequalities]]
 
# [[Lagrangean duality]]
 
# [[Column generation algorithms]]
 
# [[Heuristic algorithms]]
 
# [[Branch and cut]]
 
# [[Local branching]]
 
# [[Feasibility pump]]
 
<br />
 
 
 
|- valign="top"
 
|<br />'''&nbsp;&nbsp;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]]
 
<br />
 
 
 
|<br />'''&nbsp;&nbsp;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)]]
 
<br />
 
 
 
|- valign="top"
 
 
 
|<br />'''&nbsp;&nbsp; Deterministic Global Optimization'''
 
# [[Exponential transformation]]
 
# [[Logarithmic transformation]]
 
# [[McCormick envelopes]]
 
# [[Piecewise linear approximation]]
 
# [[Spatial branch and bound method]]
 
<br />
 
 
 
|<br />'''&nbsp;&nbsp;Optimization under Uncertainty'''
 
# [[Stochastic programming]]
 
# [[Chance-constraint method]]
 
# [[Fuzzy programming]]
 
# [[Classical robust optimization]]
 
# [[Distributionally robust optimization]]
 
# [[Adaptive robust optimization]]
 
# [[Data driven robust optimization]]
 
<br />
 
 
 
 
 
|- valign="top"
 
 
 
 
 
|<br />'''&nbsp;&nbsp;Optimization for Machine Learning and Data Analytics'''
 
# [[Stochastic gradient descent]]
 
# [[Momentum]]
 
# [[AdaGrad]]
 
# [[RMSProp]]
 
# [[Adam]]
 
# [[Alternating direction method of multiplier (ADMM)]]
 
# [[Frank-Wolfe]]
 
<br />
 
 
 
|<br />'''&nbsp;&nbsp;Featured Applications'''
 
# [[Facility location problem]]
 
# [[Traveling salesman problem]]
 
# [[Portfolio optimization]]
 
# [[Set covering problem]]
 
# [[Unit commitment problem]]
 
# [[Wing Shape Optimization]]
 
# [[Optimization in Game Theory]]
 
<br />
 
  
 +
{| id="mp-upper" style="width: 100%; margin:6px 0 0 0; background:none; border-spacing: 0px;"
 +
| class="MainPageBG" style="width:50%; border:1px solid #cef2e0; background:#f5fffa; vertical-align:top; color:#000;" |
 +
{| id="mp-left" style="width:100%; vertical-align:top; background:#f5fffa;"
 +
! 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>
 +
|-
 +
| style="color:#000;" | <div id="mp-tfa" style="padding:2px 5px">
 +
      <li>[[Duality]]</li>
 +
      <li>[[Simplex algorithm]]</li>
 +
      <li>[[Computational complexity]]</li>
 +
      <li>[[Network flow problem]]</li>
 +
      <li>[[Interior-point method for LP]]</li>
 +
      <li>[[Optimization with absolute values]]</li>
 +
      <li>[[Matrix game (LP for game theory)]]</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;">NonLinear Programming (NLP)</h2>
 +
|-
 +
| style="color:#000;padding:2px 5px 5px" | <div id="mp-dyk">
 +
      <li>[[Line search methods]]</li>
 +
      <li>[[Trust-region methods]]</li>
 +
      <li>[[Interior-point method for NLP]]</li>
 +
      <li>[[Conjugate gradient methods]]</li>
 +
      <li>[[Quasi-Newton methods]]</li>
 +
      <li>[[Quadratic programming]]</li>
 +
      <li>[[Sequential quadratic programming]]</li>
 +
      <li>[[Subgradient optimization]]</li>
 +
      <li>[[Mathematical programming with equilibrium constraints]]</li>
 +
      <li>[[Dynamic optimization]]</li>
 +
      <li>[[Geometric programming]]</li>
 +
      <li>[[Nondifferentiable 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>
 +
|-
 +
| style="color:#000;padding:2px 5px 5px" | <div id="mp-dyk">
 +
      <li>[[Exponential transformation]]</li>
 +
      <li>[[Logarithmic transformation]]</li>
 +
      <li>[[McCormick envelopes]]</li>
 +
      <li>[[Piecewise linear approximation]]</li>
 +
      <li>[[Spatial branch and bound method]]</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;">Dynamic Programming</h2>
 +
|-
 +
| style="color:#000;padding:2px 5px 5px" | <div id="mp-dyk">
 +
      <li>[[Markov decision process]]</li>
 +
      <li>[[Bellman equation]]</li>
 +
      <li>[[Eight step procedures]]</li>
 +
      <li>[[Stochastic dynamic programming]]</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;">Traditional Applications</h2>
 +
|-
 +
| style="color:#000;padding:2px 5px 5px" | <div id="mp-dyk">
 +
      <li>[[Facility location problem]]</li>
 +
      <li>[[Traveling salesman problem]]</li>
 +
      <li>[[Set covering problem]]</li>
 +
      <li>[[Quadratic assignment problem]]</li>
 +
      <li>[[Newsvendor problem]]</li>
 +
      <li>[[Unit commitment problem]]</li>
 +
      <li>[[Portfolio optimization]]</li>
 +
</div>
 +
|}
  
 +
| style="border:1px solid transparent;" |
 +
<!--        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" | <div id="mp-otd">
 +
      <li>[[Mixed-integer cuts]]</li>
 +
      <li>[[Disjunctive inequalities]]</li>
 +
      <li>[[Lagrangean duality]]</li>
 +
      <li>[[Column generation algorithms]]</li>
 +
      <li>[[Heuristic algorithms]]</li>
 +
      <li>[[Branch and cut]]</li>
 +
      <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" | <div id="mp-otd">
 +
      <li>[[Signomial problems]]</li>
 +
      <li>[[Mixed-integer linear fractional programming (MILFP)]]</li>
 +
      <li>[[Convex 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>
 +
      <li>[[Generalized Benders decomposition (GBD)]]</li>
 +
      <li>[[Outer-approximation (OA)]]</li>
 +
      <li>[[Extended cutting plane (ECP)]]</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;">Optimization under Uncertainty</h2>
 +
|-
 +
| 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>
 +
</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;">Optimization for Machine Learning and Data Analytics</h2>
 +
|-
 +
| style="color:#000;padding:2px 5px 5px" | <div id="mp-dyk">
 +
      <li>[[Stochastic gradient descent]]</li>
 +
      <li>[[Momentum]]</li>
 +
      <li>[[AdaGrad]]</li>
 +
      <li>[[RMSProp]]</li>
 +
      <li>[[Adam]]</li>
 +
      <li>[[Alternating direction method of multiplier (ADMM)]]</li>
 +
      <li>[[Frank-Wolfe]]</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;">Emerging Applications</h2>
 +
|-
 +
| 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>[[Quantum computing for optimization]]</li>
 +
</div>
 +
|}
 
|}
 
|}
  
 +
== Sponsor ==
 +
[[File:Peese-logo.jpg|Cornell Prof. Fengqi You Research Group |link=https://www.peese.org]]
  
== External Links ==
+
</noinclude>__NOTOC____NOEDITSECTION__
* [https://www.peese.org PEESE @ Cornell]
 

Latest revision as of 18:06, 17 November 2020

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)

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

Cornell Prof. Fengqi You Research Group