# 2020 Cornell Optimization Open Textbook Feedback: Difference between revisions

## Duality

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

## Computational complexity

• Numerical Example
1. Finding subsets of a set is NOT O(2n).
• Application
1. The applications mentioned need to be discussed further.

## Network flow problem

• Real Life Applications
1. There is NO need to include code. Simply mention how the problem was coded along with details on the LP solver used.
2. 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
1. Fix typos “where A ε R” in Lagrange Function subsection.
2. Please type “minimize” and “subject to” in formal optimization problem form throughout the whole page.
• A section to discuss and/or illustrate the applications
1. Please type optimization problem in the formal form.

## Optimization with absolute values

• An introduction of the topic
1. Add few sentences on how absolute values convert optimization problem into a nonlinear optimization problem
• Applications
1. 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
1. aij are not defined in this section.

## Quasi-Newton methods

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

## Markov decision process

• Introduction
1. Please fix typos such as “discreet”.
• Theory and Methodology
1. If abbreviations are defined like MDP, use the abbreviations throughout the Wiki

## Eight step procedures

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

## Facility location problem

• Numerical Example
1. 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
1. Use proper math notations for “greater than equal to”.
• Numerical Example
1. Please leave some space between equation and equation number.

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

## Newsvendor problem

• Formulation
1. 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.
2. 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
1. 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
1. References at the end of the sentence should be placed after the period.
• Theory, methodology and algorithmic discussions
1. Some minor typos/article agreement issues exist “is not partical in real-world”.

## Heuristic algorithms

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

## Branch and cut

• Methodology & Algorithm
1. Equation in most infeasible branching section is not properly formatted.
3. Step 5 contains latex code terms that are not properly formatted. Please fix the same.
4. Fix typos:  e.g., repeated “for the current”.

## Mixed-integer linear fractional programming (MILFP)

• Application and Modeling for Numerical Examples
1. Please check the index notation in Mass Balance Constraint

## Convex generalized disjunctive programming (GDP)

• Introduction
1. Please refrain from defining the same abbreviations multiple times.
2. Please use abbreviations throughout the page if they have been defined.
• Numerical Example
1. There is a duplicate figure 3.

## Fuzzy programming

• Applications
1. 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.

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

• Numerical Example
1. 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
1. Deep learning can become a subsection on its own.

## RMSProp

• Introduction
1. References at the end of the sentence should be placed after the period.
• Theory and Methodology
1. Please check grammar in this section.
• Applications and Discussion
1. The applications section does not contain any discussion on applications. Please mention a few applications of the widely used RMSprop and discuss them briefly.