Difference between revisions of "2020 Cornell Optimization Open Textbook Feedback"

From Cornell University Computational Optimization Open Textbook - Optimization Wiki
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*# Use proper math notations for “greater than equal to”.
 
*# Use proper math notations for “greater than equal to”.
 
* At least one numerical example
 
* At least one numerical example
*# Since Table 3 provides information on aij required to formulate the constraints, Table 2 serves no purpose and should be removed from the Wiki. Table 3 can be directly generated from Table 1.
 
*# The numerical example is solved manually without using greedy method nor LP solution method. Please solve this example both by the presented greedy algorithm and the newly added LP-based method and finally compare solutions.
 
 
*# Please leave some space between equation and equation number.
 
*# Please leave some space between equation and equation number.
  
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*# Discuss dynamic programming and cutting plane solution techniques briefly.
 
*# Discuss dynamic programming and cutting plane solution techniques briefly.
 
* A section to discuss and/or illustrate the applications
 
* A section to discuss and/or illustrate the applications
*# Please format the equation for definition of yij in the hospital layout subsection.
 
  
 
==[[Newsvendor problem]]==
 
==[[Newsvendor problem]]==
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* Theory, methodology, and/or algorithmic discussions
 
* Theory, methodology, and/or 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”.
*# Referencing subtitle "Methodology" is not a formal way.
 
  
 
==[[Heuristic algorithms]]==
 
==[[Heuristic algorithms]]==
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* At least one numerical example
 
* At least one numerical example
*# Please check the index notation in Mass Balance Constraints.
+
*# Please check the index notation in Mass Balance Constraint
* References
 
*# Please consider linking the citations to references in the reference list by using this as Wiki template, rather than using website links. [[Quantum computing for optimization|https://optimization.cbe.cornell.edu/index.php?title=Quantum_computing_for_optimization]]
 
  
 
==[[Convex generalized disjunctive programming (GDP)]]==
 
==[[Convex generalized disjunctive programming (GDP)]]==

Revision as of 01:21, 15 December 2020

Duality

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

Simplex algorithm

  • Theory, methodology, and/or algorithmic discussions
  • References

Computational complexity

  • Theory, methodology, and/or algorithmic discussions
  • At least one numerical example
    1. Finding subsets of a set is NOT O(2^n).
  • A section to discuss and/or illustrate the applications
    1. The applications mentioned need to be discussed further.

Network flow problem

  • At least one numerical example
    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.

Interior-point method for LP

  • An introduction of the topic
    1. Fix typos “where A ε R”
    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.
    2. Please double check typos and extra spaces.

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
  • A section to discuss and/or illustrate the 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, methodology, and/or algorithmic discussions
    1. aij are not defined in this section.

Quasi-Newton methods

  • Theory, methodology, and/or algorithmic discussions
    1. Please ensure that few spaces are kept between the equations and equation numbers.

Markov decision process

  • An introduction of the topic
    1. Please fix typos such as “discreet”.
  • Theory, methodology, and/or algorithmic discussions
    1. If abbreviations are defined like MDP, use the abbreviations throughout the Wiki

Eight step procedures

  • At least one 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

  • At least one numerical example
    1. Mention how the formulated problem is coded and solved. No need to provide GAMS code.

Set covering problem

  • Theory, methodology, and/or algorithmic discussions
    1. Use proper math notations for “greater than equal to”.
  • At least one 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.
  • A section to discuss and/or illustrate the applications

Newsvendor problem

  • Theory, methodology, and/or algorithmic discussions
    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

  • A section to discuss and/or illustrate the 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

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

Heuristic algorithms

  • Theory, methodology, and/or algorithmic discussions
    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

  • Theory, methodology, and/or algorithmic discussions
    1. Equation in most infeasible branching section is not properly formatted, please fix the same.
    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. Please fix the same.
    4. Fix typos:  e.g., repeated “for the current”, men Problem can viewed as partially” ..

Mixed-integer linear fractional programming (MILFP)

  • At least one numerical example
    1. Please check the index notation in Mass Balance Constraint

Convex generalized disjunctive programming (GDP)

  • An introduction of the topic
    1. Please refrain from defining the same abbreviations multiple times.
    2. Please use abbreviations throughout the page if they have been defined.
  • At least one numerical example
    1. There is a duplicate figure.

Fuzzy programming

  • An introduction of the topic
    1. References at the end of the sentence should be placed after the period.
  • At least one numerical example
    1. The numeric example should be before the applications section.
  • A section to discuss and/or illustrate the 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.
  • References
    1. Please consider linking the references by using this as Wiki template, https://optimization.cbe.cornell.edu/index.php?title=Quantum_computing_for_optimization

Adaptive robust optimization

  • Theory, methodology, and/or algorithmic discussions
    1. Please check typos such as "Let u bee a vector".
    2. The abbreviation KKT is not previously defined.

Stochastic gradient descent

  • At least one 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.
  • A section to discuss and/or illustrate the applications
    1. Deep learning can become a subsection on its own.

RMSProp

  • An introduction of the topic
    1. References at the end of the sentence should be placed after the period.
  • Theory, methodology, and/or algorithmic discussions
    1. Please check grammar in this section.
  • A section to discuss and/or illustrate the applications
    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.
    2. Please check grammar in this section.
  • References
    1. Please refer to the example Wikis provided to use proper citation style.
    2. Please consider linking the references by using this as Wiki template, https://optimization.cbe.cornell.edu/index.php?title=Quantum_computing_for_optimization

Adam

  • Theory, methodology, and/or algorithmic discussions
    1. References at the end of the sentence should be placed after the period.