# 2020 Cornell Optimization Open Textbook Feedback: Difference between revisions

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==[[Interior-point method for LP]]== | ==[[Interior-point method for LP]]== | ||

* | * Introduction | ||

*# Fix typos “where A ε R” in Lagrange Function subsection. | *# Fix typos “where A ε R” in Lagrange Function subsection. | ||

*# Please type “minimize” and “subject to” in formal optimization problem form throughout the whole page. | *# Please type “minimize” and “subject to” in formal optimization problem form throughout the whole page. | ||

Line 29: | Line 29: | ||

* An introduction of the topic | * An introduction of the topic | ||

*# Add few sentences on how absolute values convert optimization problem into a nonlinear optimization problem | *# Add few sentences on how absolute values convert optimization problem into a nonlinear optimization problem | ||

* | * Applications | ||

*# Inline equations at the beginning of this section are not formatted properly. Please fix the notation for expected return throughout the section. | *# Inline equations at the beginning of this section are not formatted properly. Please fix the notation for expected return throughout the section. | ||

==[[Matrix game (LP for game theory)]]== | ==[[Matrix game (LP for game theory)]]== | ||

* Theory | * Theory and Algorithmic Discussion | ||

*# aij are not defined in this section. | *# aij are not defined in this section. | ||

==[[Quasi-Newton methods]]== | ==[[Quasi-Newton methods]]== | ||

* Theory | * Theory and Algorithm | ||

*# Please ensure that few spaces are kept between the equations and equation numbers. | *# Please ensure that few spaces are kept between the equations and equation numbers. | ||

== [[Markov decision process]] == | == [[Markov decision process]] == | ||

* | * Introduction | ||

*# Please fix typos such as “discreet”. | *# Please fix typos such as “discreet”. | ||

* Theory | * Theory and Methodology | ||

*# If abbreviations are defined like MDP, use the abbreviations throughout the Wiki | *# If abbreviations are defined like MDP, use the abbreviations throughout the Wiki | ||

==[[Eight step procedures]]== | ==[[Eight step procedures]]== | ||

* | * Numerical Example | ||

*# Data for the example Knapsack problem (b,w) are missing. | *# Data for the example Knapsack problem (b,w) are missing. | ||

*# How to arrive at optimal solutions is missing. | *# How to arrive at optimal solutions is missing. | ||

Line 57: | Line 57: | ||

==[[Facility location problem]]== | ==[[Facility location problem]]== | ||

* | * Numerical Example | ||

*# Mention how the formulated problem is coded and solved. No need to provide GAMS code. | *# Mention how the formulated problem is coded and solved. No need to provide GAMS code. | ||

==[[Set covering problem]]== | ==[[Set covering problem]]== | ||

* | * Integer linear program formulation & Approximation via LP relaxation and rounding | ||

*# Use proper math notations for “greater than equal to”. | *# Use proper math notations for “greater than equal to”. | ||

* | * Numerical Example | ||

*# Please leave some space between equation and equation number. | *# Please leave some space between equation and equation number. | ||

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==[[Newsvendor problem]]== | ==[[Newsvendor problem]]== | ||

* | * Formulation | ||

*# A math programming formulation of the optimization problem with objective function and constraints is expected for the formulation. Please add any variant of the newsvendor problem along with some operational constraints. | *# A math programming formulation of the optimization problem with objective function and constraints is expected for the formulation. Please add any variant of the newsvendor problem along with some operational constraints. | ||

*# A mathematical presentation of the solution technique is expected. Please consider any distribution for R and present a solution technique for that specific problem. | *# A mathematical presentation of the solution technique is expected. Please consider any distribution for R and present a solution technique for that specific problem. | ||

Line 80: | Line 80: | ||

==[[Mixed-integer cuts]]== | ==[[Mixed-integer cuts]]== | ||

* | * Applications | ||

*# MILP and their solution techniques involving cuts are extremely versatile. Yet, only two sentences are added to describe their applications. Please discuss their applications, preferably real-world applications, in brief. Example Wikis provided on the website could be used as a reference to do so. | *# MILP and their solution techniques involving cuts are extremely versatile. Yet, only two sentences are added to describe their applications. Please discuss their applications, preferably real-world applications, in brief. Example Wikis provided on the website could be used as a reference to do so. | ||

==[[Column generation algorithms]]== | ==[[Column generation algorithms]]== | ||

* | * Introduction | ||

*# References at the end of the sentence should be placed after the period. | *# References at the end of the sentence should be placed after the period. | ||

* Theory, methodology | * Theory, methodology and algorithmic discussions | ||

*# Some minor typos/article agreement issues exist “is not partical in real-world”. | *# Some minor typos/article agreement issues exist “is not partical in real-world”. | ||

==[[Heuristic algorithms]]== | ==[[Heuristic algorithms]]== | ||

* | * Methodology | ||

*# Please use proper symbol for "greater than or equal to". | *# Please use proper symbol for "greater than or equal to". | ||

*# Greedy method to solve minimum spanning tree seems to be missing. | *# Greedy method to solve minimum spanning tree seems to be missing. | ||

Line 98: | Line 98: | ||

==[[Branch and cut]]== | ==[[Branch and cut]]== | ||

* | * Methodology & Algorithm | ||

*# Equation in most infeasible branching section is not properly formatted. | *# Equation in most infeasible branching section is not properly formatted. | ||

*# Step 2 appears abruptly in the algorithm and does not explain much. Please add more information regarding the same. | *# Step 2 appears abruptly in the algorithm and does not explain much. Please add more information regarding the same. | ||

Line 106: | Line 106: | ||

== [[Mixed-integer linear fractional programming (MILFP)]] == | == [[Mixed-integer linear fractional programming (MILFP)]] == | ||

* | * Application and Modeling for Numerical Examples | ||

*# Please check the index notation in Mass Balance Constraint | *# Please check the index notation in Mass Balance Constraint | ||

==[[Convex generalized disjunctive programming (GDP)]]== | ==[[Convex generalized disjunctive programming (GDP)]]== | ||

* | * Introduction | ||

*# Please refrain from defining the same abbreviations multiple times. | *# Please refrain from defining the same abbreviations multiple times. | ||

*# Please use abbreviations throughout the page if they have been defined. | *# Please use abbreviations throughout the page if they have been defined. | ||

* | * Numerical Example | ||

*# There is a duplicate figure 3. | *# There is a duplicate figure 3. | ||

==[[Fuzzy programming]]== | ==[[Fuzzy programming]]== | ||

* | * Applications | ||

*# Applications of fuzzy programming are quite versatile. Please discuss few of the mentioned applications briefly. The provided example Wikis can be used as a reference to write this section. | *# Applications of fuzzy programming are quite versatile. Please discuss few of the mentioned applications briefly. The provided example Wikis can be used as a reference to write this section. | ||

==[[Adaptive robust optimization]]== | ==[[Adaptive robust optimization]]== | ||

* | * Problem Formulation | ||

*# Please check typos such as "Let ''u'' bee a vector". | *# Please check typos such as "Let ''u'' bee a vector". | ||

*# The abbreviation KKT is not previously defined. | *# The abbreviation KKT is not previously defined. | ||

== [[Stochastic gradient descent]] == | == [[Stochastic gradient descent]] == | ||

* | * Numerical Example | ||

*# Amount of whitespace can be reduced by changing orientation of example dataset by converting it into a table containing 3 rows and 6 columns. | *# Amount of whitespace can be reduced by changing orientation of example dataset by converting it into a table containing 3 rows and 6 columns. | ||

* | * Application | ||

*# Deep learning can become a subsection on its own. | *# Deep learning can become a subsection on its own. | ||

==[[RMSProp]]== | ==[[RMSProp]]== | ||

* | * Introduction | ||

*# References at the end of the sentence should be placed after the period. | *# References at the end of the sentence should be placed after the period. | ||

* Theory | * Theory and Methodology | ||

*# Please check grammar in this section. | *# Please check grammar in this section. | ||

* | * Applications and Discussion | ||

*# The applications section does not contain any discussion on applications. Please mention a few applications of the widely used RMSprop and discuss them briefly. | *# The applications section does not contain any discussion on applications. Please mention a few applications of the widely used RMSprop and discuss them briefly. | ||

==[[Adam]]== | ==[[Adam]]== | ||

* | * Background | ||

*# References at the end of the sentence should be placed after the period. | *# References at the end of the sentence should be placed after the period. |

## Revision as of 11:55, 15 December 2020

## Duality

- Theory, methodology, and/or algorithmic discussions
- Remove colon in the subsection title.

## Computational complexity

- Numerical Example
- Finding subsets of a set is NOT O(2
^{n}).

- Finding subsets of a set is NOT O(2
- Application
- The applications mentioned need to be discussed further.

## Network flow problem

- Real Life Applications
- There is NO need to include code. Simply mention how the problem was coded along with details on the LP solver used.
- The subsection title style should be consistent. Subsection titles in Real Life Applications section are not in title case like the ones in Theory section.

## Interior-point method for LP

- Introduction
- Fix typos “where A ε R” in Lagrange Function subsection.
- Please type “minimize” and “subject to” in formal optimization problem form throughout the whole page.

- A section to discuss and/or illustrate the applications
- Please type optimization problem in the formal form.

## Optimization with absolute values

- An introduction of the topic
- Add few sentences on how absolute values convert optimization problem into a nonlinear optimization problem

- Applications
- Inline equations at the beginning of this section are not formatted properly. Please fix the notation for expected return throughout the section.

## Matrix game (LP for game theory)

- Theory and Algorithmic Discussion
- aij are not defined in this section.

## Quasi-Newton methods

- Theory and Algorithm
- Please ensure that few spaces are kept between the equations and equation numbers.

## Markov decision process

- Introduction
- Please fix typos such as “discreet”.

- Theory and Methodology
- If abbreviations are defined like MDP, use the abbreviations throughout the Wiki

## Eight step procedures

- Numerical Example
- Data for the example Knapsack problem (b,w) are missing.
- How to arrive at optimal solutions is missing.

## Facility location problem

- Numerical Example
- Mention how the formulated problem is coded and solved. No need to provide GAMS code.

## Set covering problem

- Integer linear program formulation & Approximation via LP relaxation and rounding
- Use proper math notations for “greater than equal to”.

- Numerical Example
- Please leave some space between equation and equation number.

## Quadratic assignment problem

- Theory, methodology, and/or algorithmic discussions
- Discuss dynamic programming and cutting plane solution techniques briefly.

## Newsvendor problem

- Formulation
- A math programming formulation of the optimization problem with objective function and constraints is expected for the formulation. Please add any variant of the newsvendor problem along with some operational constraints.
- A mathematical presentation of the solution technique is expected. Please consider any distribution for R and present a solution technique for that specific problem.

## Mixed-integer cuts

- Applications
- MILP and their solution techniques involving cuts are extremely versatile. Yet, only two sentences are added to describe their applications. Please discuss their applications, preferably real-world applications, in brief. Example Wikis provided on the website could be used as a reference to do so.

## Column generation algorithms

- Introduction
- References at the end of the sentence should be placed after the period.

- Theory, methodology and algorithmic discussions
- Some minor typos/article agreement issues exist “is not partical in real-world”.

## Heuristic algorithms

- Methodology
- Please use proper symbol for "greater than or equal to".
- Greedy method to solve minimum spanning tree seems to be missing.

## Branch and cut

- Methodology & Algorithm
- Equation in most infeasible branching section is not properly formatted.
- Step 2 appears abruptly in the algorithm and does not explain much. Please add more information regarding the same.
- Step 5 contains latex code terms that are not properly formatted. Please fix the same.
- Fix typos: e.g., repeated “for the current”.

## Mixed-integer linear fractional programming (MILFP)

- Application and Modeling for Numerical Examples
- Please check the index notation in Mass Balance Constraint

## Convex generalized disjunctive programming (GDP)

- Introduction
- Please refrain from defining the same abbreviations multiple times.
- Please use abbreviations throughout the page if they have been defined.

- Numerical Example
- There is a duplicate figure 3.

## Fuzzy programming

- Applications
- Applications of fuzzy programming are quite versatile. Please discuss few of the mentioned applications briefly. The provided example Wikis can be used as a reference to write this section.

## Adaptive robust optimization

- Problem Formulation
- Please check typos such as "Let
*u*bee a vector". - The abbreviation KKT is not previously defined.

- Please check typos such as "Let

## Stochastic gradient descent

- Numerical Example
- Amount of whitespace can be reduced by changing orientation of example dataset by converting it into a table containing 3 rows and 6 columns.

- Application
- Deep learning can become a subsection on its own.

## RMSProp

- Introduction
- References at the end of the sentence should be placed after the period.

- Theory and Methodology
- Please check grammar in this section.

- Applications and Discussion
- The applications section does not contain any discussion on applications. Please mention a few applications of the widely used RMSprop and discuss them briefly.

## Adam

- Background
- References at the end of the sentence should be placed after the period.