2020 Cornell Optimization Open Textbook Feedback

From Cornell University Computational Optimization Open Textbook - Optimization Wiki
Jump to navigation Jump to search

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

  • Numerical Example and Solution
    1. There is NO need to include code. Simply mention how the problem was coded along with details on the LP solver used.

Interior-point method for LP

  • Introduction
    1. 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.

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.

Set covering problem

  • Numerical Example
    1. Please leave some space between equation and equation number.

Quadratic assignment problem

  • 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.

Heuristic algorithms

  • Methodology
    1. 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.
    2. Step 2 appears abruptly in the algorithm and does not explain much. Please add more information regarding the same.
    3. Step 5 contains latex code terms that are not properly formatted.

Mixed-integer linear fractional programming (MILFP)

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

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.

Stochastic gradient descent

  • 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.


  • 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.
  • Reference
    1. Many references listed here are not used in any of the text in the Wiki. Please link them appropriately.