2024 Cornell Optimization Open Textbook Feedback: Difference between revisions

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== [[Genetic algorithm]] ==
== [[Genetic algorithm]] ==
 
*Are the Figures used in the text self-generated or cited? If cited, please add citations. Also the resolution of the figures may need to be improved. Looks like two Figure 1 are included, please fix it.
*Please provide some citations  for supporting your statement (e.g. in Introduction)
*Abbreviations should be consistent throughout the context (e.g. GA)
*Please avoid citing or adding links to Wikipedia.
*Please remove the sentence, "as documented in Computational Optimization and Applications."
*More details are expected for Algorithm Discussion section.
== [[Simulated annealing]] ==
== [[Simulated annealing]] ==
*Please provide citations in the application section to support your statements.
*Please provide citations in the application section to support your statements.

Revision as of 13:58, 13 December 2024

Computational complexity

Heuristic algorithms

Local branching

Trust-region methods

  • For the applications section, a better method is to briefly summarize the advantages of the method in multiple areas, instead of presents its application on two very specific cases.
  • Please try to update equations on page 5.
  • Please try to use the pseudocode for the procedure described on page 3.
  • m_k and p in eq.(1) are not defined.
  • Fix the typo “prediced reduction” in eq.(3).
  • Avoid using contraction (e.g., it’s, doesn’t) and pronouns (e.g., we, us) in scientific writing.
  • Rewrite the sentence, “Identical to line search, we do not need to compute the algorithm (2)” as (2) itself is not an algorithm.
  • The termination conditions are not complete in section “Termination Criteria”.
  • Fletcher is not the same author as cited in [5].

Quadratic programming

Sequential quadratic programming

Subgradient optimization

Dynamic optimization

Nondifferentiable Optimization

Evolutionary multimodal optimization

Stackelberg leadership model

Quadratic constrained quadratic programming

  • Please double-check that all cited Figures are clearly attached with citations.
  • Please double-check the citations of your references.
  • Need significant amount of citation to support the statement
  • Mentioned the ways to relax the non convex set, so please provide at least one example either from SDP or RLT.
  • Double check with abbreviations (e.g. KKT and SDP should be defined at the beginning sections)
  • Equations should be written in a formal way (e.g. 1/2 should be 1 on top and 2 on the bottom)
  • Abbreviations should be introduced only once throughout all sections (e.g., QCQP, QP, SDP)
  • Avoid using pronouns (e.g., we) in scientific writing.
  • Please revise “Ex. Objective” and “Ex. Constraint” parts in a more professional way in the Application section.

Derivative free optimization

Signomial problems

Adadelta

Adafactor

AdamW

Adamax

FTRL algorithm

Lion algorithm

LossscaleOptimizer

Nadam

Beyesian optimization

Genetic algorithm

  • Are the Figures used in the text self-generated or cited? If cited, please add citations. Also the resolution of the figures may need to be improved. Looks like two Figure 1 are included, please fix it.
  • Please provide some citations for supporting your statement (e.g. in Introduction)
  • Abbreviations should be consistent throughout the context (e.g. GA)
  • Please avoid citing or adding links to Wikipedia.
  • Please remove the sentence, "as documented in Computational Optimization and Applications."
  • More details are expected for Algorithm Discussion section.

Simulated annealing

  • Please provide citations in the application section to support your statements.
  • Provide some figures for the numerical study
  • Substitute those symbols in a formal way (e.g. T_min should be in a formal way).
  • Once the abbr. is defined please use it throughout the context (e.g. SA)
  • There are many extra words throughout the sections, (e.g., “Spaces” in the last sentence of Introduction section, “D.” in section 2.1)
  • Avoid using pronouns (e.g., we) in scientific writing.

Particle swarm optimization

Differential evolution