Science topic

Optimization - Science topic

Explore the latest questions and answers in Optimization, and find Optimization experts.
Questions related to Optimization
  • asked a question related to Optimization
Question
2 answers
I am trying to optimize my antibodies for free floating IHC-IF on paraformaldehyde fixed mouse brain sections. I want to preserve my slices in an appropriate cryopreservation solution for later use and save their freshness. I can not order ethylene glycol due to some legal issues. They suggested diethylene glycol instead of ethylene glycol but I couldn't find any recipe or paper about its use on slice preservation. Is ıt possible to use diethylene glycol instead of ethylene glycol for slice cryopreservation? And lastly, can I use it with PVP40?
My protocol for cropreservation is:
Sucrose ----------------------------- 300 g
Polyvinyl-pyrrolidone (PVP-40) --- 10 g
0.1M PB ----------------------------- 500 ml
Ethylene glycol --------------------- 300 ml
Thank you in advance.
Relevant answer
Answer
Hi Muhammed,
I can't comment on diethylene glycol but what I've done in the past is to cryopreserve the full brain in 30% sucrose in TBS before sectioning, then store the brain sections free floating in a 1:1 glycerol-TBS solution at -20C. I find this preserves them almost indefinitely for IHC with the exception of some particularly sensitive stains.
  • asked a question related to Optimization
Question
3 answers
Can anyone suggest me some new optimization techniques and thair matlab codes.
Relevant answer
Answer
You can have a look at this article:
  • asked a question related to Optimization
Question
5 answers
Hello experts
I am dealing with an optimization problem in which the algorithm will choose the section of the column (RC structure) between a lower and an upper bound (e.g., LB and UB).
How can I ask the algorithm to change the cross section if the condition of the period isn't satisfied?
N.B: the period value is retrieved from the sap2000 software using the API, and it has no relation with the design variables to be set as a constriant.
Any one knows how to do it please?
Relevant answer
Answer
You can give a default fitness function value that is very large (in case you are in a minimization problem), the algorithm will automatically avoid such parameters in next iterations
Like this:
If period verified
Fitness = Your evaluation
If not
Fitness= large
End
  • asked a question related to Optimization
Question
4 answers
Please explain briefly.
Relevant answer
Answer
In general, integer problems are not convex. First of all, due to nonconvexity of the feasible set. However, there are linear integer programming problems possessing properties similar to continuous linear problems. For example, classical transportation, assignment and many network flow problems. The reason is that all vertices of the feasible set have integer components. See more, e.g., in L.Wolsey, Integer Programming.
  • asked a question related to Optimization
Question
4 answers
The set of optimal solutions obtained in the form of Pareto front includes all equally good trade-off solutions. But I was wondering, whether these solutions are global optima or local optima or mix of both. In other words, does an evolutionary algorithm like NSGA-II guaranties global optimum solutions?
Thank you in anticipation.
Relevant answer
Answer
No, a Pareto front produced by an evolutionary algorithm does not necessarily include both global and local optima. The Pareto front represents the set of non-dominated solutions in multi-objective optimization problems. These solutions are not dominated by any other solution in terms of all the objective functions simultaneously.
In a multi-objective optimization problem, there can be multiple optimal solutions, known as Pareto optimal solutions, that represent trade-offs between conflicting objectives. These solutions lie on the Pareto front and are considered efficient solutions because improving one objective would require sacrificing performance in another objective.
The Pareto front typically contains a mixture of global and local optima. Global optima are solutions that provide the best performance across all objectives in the entire search space. Local optima, on the other hand, are solutions that are optimal within a specific region of the search space but may not be globally optimal.
The evolutionary algorithm aims to explore the search space and find a diverse set of Pareto optimal solutions across the entire front, which may include both global and local optima. However, the algorithm's ability to discover global optima depends on its exploration and exploitation capabilities, the problem complexity, and the specific settings and parameters of the algorithm.
It's important to note that the distribution and representation of global and local optima on the Pareto front can vary depending on the problem and algorithm used. Analyzing the Pareto front and its solutions can provide valuable insights into the trade-offs and optimal solutions available in multi-objective optimization problems.
  • asked a question related to Optimization
Question
3 answers
What are the best practices for optimizing performance and efficiency in R programming, particularly when dealing with large datasets or computationally intensive tasks? Are there any specific techniques or packages that researchers should be aware of?
  • asked a question related to Optimization
Question
2 answers
Hi everyone,
I want to use the Casadi optimization package for my NLMPC controller in Matlab/Simulink.
I read the examples of https://web.casadi.org/ site, but I can't modify them for my problem.
I want to use Casadi for the tacking problem, so I will have a time-varying cost function and I have time-varying constraints as well.
All the examples which I saw aren't included the time-varying cost function or time-varying constraints.
I would be grateful if anyone can help me.
Regards,
Hossein.
Relevant answer
Answer
Thank you so much for your kind response, Dear Mostak.
After some deeper searches, I could find a good example in the Casadi blog which has implemented the Casadi for optimal control of the Vanderpol system. You can catch it up from the following link:
I could modify it for my problem (Nonlinear Model predictive control of a Wheeled Mobile Robot), but the tracking performance is not good as expected from an MPC controller. The tracking performance is sensitive to the cost function weighting factors and based I my knowledge the MPC weighting factors tunning is straightforward and should not be a challenging task.
Maybe I make mistakes in modifying the Simulink source file, but I checked the Simulink file over and over.
Moreover, I tried to convert the simulation file of the following code to the Simulink file and call the Casadi using the s-function, I did it but the simulation results is wrong.
Any positive experience in converting these examples to the Simulink file could help me.
Regards,
Hossein.
  • asked a question related to Optimization
Question
6 answers
Suppose we have a HEN with several multi-pass heat exchangers. However, due to some technical constraints all these exchangers are modelled simply using single pass equations.
What will the impact if such a simplistic model is used in optimization problems, such as network optimization for retrofitting or cleaning scheduling?
For instance, it is clear that we may not end up with global optimal solutions but still what will the qualitative impact of such approximations?
Relevant answer
Answer
To formulate of this model you need to consider the amount of information included in it:
  • asked a question related to Optimization
Question
1 answer
I am using Gaussian to estimate the ionization potential of a cluster of molecules including 6 molecules of Methanol and 1 molecule of Fluorobenzene, I have optimized the geometry of the neutral cluster and then did a single point energy calculation of the optimized geometry with +1 charge, the difference between energy of the two gives me a rough estimate of the IP, my question is how can we interpret this IP, is it the IP of the whole system or the molecule from which the electron has been removed? also by visualizing the HOMO orbitals in both cases I noticed that the HOMO in neutral case is one of the Pi orbitals of the Fluorobenzene ring while in the ionized case it's the bonding orbital of one of the methanol molecules, not sure how to interpret that?
Relevant answer
Answer
I hope you will be fine
I have read your problem, there is two way to find the IP. One is you will perform neutral molecules and visualize the HOMO LUMO energy. A highly occupied orbital shows the maximum energy to donate the electron so HOMO energy is equal to IP similarly LUMO shows maximum energy to accept an electron that is also equal to EA. But in another way, you will perform a neutral molecule and note down the energy of the molecule and again perform that molecule after adding + charge and calculating energy. In the end you can calculate the IP by differentiating energy .
  • asked a question related to Optimization
Question
3 answers
Hi everyone,
I'm performing DFT Calculation / Geometry Optimization of some 5,7-dibromobenzofurane hydrazide derivatives using ORCA 4.2.1 and BY3LP basis set (! B3LYP RIJK def2-TZVP def2/JK).
Each of 5,7-dibromobenzofurane derivatives has two isomers: anti-isomer and syn-isomer.
While the anti-isomer of a compound was successfully calculated with SCF converged after 43 cycles, the syn- one was not able to obtain the final results with this error:
----------------------------------------------------------------------------------
FINAL SINGLE POINT ENERGY -6213.007766579487 (Wavefunction not fully converged!)
----------------------------------------------------------------------------------
ERROR
This wavefunction IS NOT FULLY CONVERGED!
You can't use it for properties or numerical calculations !
Aborting the run ...
Please restart calculation (with larger maxiter/different convergence flags)
----------------------------------------------------------------------------------
Herein I attached two ouput files corresponding two isomers of a compound.
Relevant answer
Answer
Hi,
This answer is purely technical, as I do not know much about your molecule.
You are not performing a geometry optimization. The input file is for a single point calculation.
You need TightOpt or Opt in your keywords.
Also try the RIJCOSX method instead of RIJK (Reasons are mentioned in the ORCA manual)
The B3LYP has its own problems (difficulty in convergence being the most prominent one).
I would also change the %maxcore to a reasonably high value.
I believe you can also check the GRID options given in the manual.
Please note that GRID options are different in ORCA 4 and ORCA 5
On that note:
For optimization I would use the following
! B3LYP def2-TZVP RIJCOSX def2/J TightOPT KDIIS
If this does not work try
=============================================
! BP86 def2-TZVP RIJCOSX def2/J TightOPT KDIIS
I would then perform a Single Point calculation for the geometry obtained from the above method.
For single point replace BP86 with B3LYP or any other fucntional that is suitable for your system, and remove the TightOPT keyword.
=============================================
You should also add dispersion related keyword to account for dispersion corrections.
In that case I would use
! B3LYP def2-TZVP RIJCOSX def2/J TightOPT KDIIS D3BJ
or
! BP86 def2-TZVP RIJCOSX def2/J TightOPT KDIIS D3BJ
Hope this helps.
Good luck,
Sachin
  • asked a question related to Optimization
Question
3 answers
I am having issues resolving PCR inhibition in rhino dung samples for a metagenome sequencing project. I used the DNeasy Blood and Tissue Kit to extract gDNA (with expected low yields, but DNA present all the same), and have attempted to clean extracts amplify the 18S region using NEB's Q5 HiFi 2x Mastermix. I know that the primers themselves are robust, the positive control looks great, and all fecal samples EXCEPT for rhino amplify well! Cleanups/Optimizations that I have tried: 1) Monarch Kit (best Nanodrop 260/230, 260/280 values), 2) Double extractions using Qiagen's DNeasy columns, 3) Dilutions (1:5, 1:10, 1:20, 1:50, 1:75), 4) Optimizing PCR by adding 5% DMSO and 5M Betaine, 5) Zymo's 1-Step PCR Inhibitor Cleanup Kit.
Help!
Relevant answer
Answer
PCR inhibition can be a common issue in fecal samples, especially in wildlife fecal samples, due to the presence of various inhibitors that can co-purify with the DNA during extraction. It seems that you have already tried several approaches to resolve the issue, but still facing the problem.
Here are some additional suggestions that may help you to address the PCR inhibition in your rhino dung samples:
  1. Try using different DNA extraction kits: Different extraction kits may have different efficiencies for removing inhibitors from fecal samples. You can try using different kits and compare their performance.
  2. Dilute the extracted DNA: In some cases, the concentration of inhibitors may be high enough to affect PCR amplification. Diluting the extracted DNA may reduce the concentration of inhibitors and help to overcome PCR inhibition.
  3. Use PCR enhancers: Certain chemicals, such as bovine serum albumin (BSA) or trehalose, can act as PCR enhancers and help to mitigate the effects of inhibitors. You can try adding them to your PCR reaction to see if it helps.
  4. Increase the annealing temperature of the PCR: Sometimes, inhibitors can affect the specificity of the PCR reaction by interfering with primer annealing. Increasing the annealing temperature of the PCR may help to overcome this issue.
  5. Use nested PCR: Nested PCR involves using two sets of primers to amplify the target region in two separate PCR reactions. The first reaction uses external primers to generate a larger product, and the second reaction uses internal primers to amplify a smaller product nested within the first product. This approach can help to reduce the effects of inhibitors by diluting them over two PCR reactions.
  6. Use a different DNA polymerase: Some DNA polymerases are less susceptible to the effects of inhibitors than others. You can try using a different DNA polymerase to see if it helps to overcome PCR inhibition in your rhino dung samples.
These video playlists might be helpful to you:
  • asked a question related to Optimization
Question
1 answer
I am trying to optimize an molecule containing C, H and O by using gaussian 09. Parameters which are mentioned in .com file are mentioned below:
%Chk=./vac_A_vac.chk
%RWF=./vac_A_vac.rwf
%Int=./vac_A_vac.int
%D2E=./vac_A_vac.d2e
%Mem=256MW
%NProcShared=16
#P B3LYP/6-31G(d,p) Opt(ModRedundant,MaxCycles=999) Pop=Full NoSymm ROHF
Geo_Optimization GeoOpt_vac_A_vac
Unfortunately the job has been terminated with showing the following error
Error in internal coordinate system.
Error termination via Lnk1e in /usr/local/g09/l103.exe at Mon Dec 2 16:35:01 2019.
Please help me to solve the problem.
Relevant answer
Answer
Hello:
I'm sure you already resolved this error of yours, but I had the same issue recently and I found out through guessing and missing that it is not a problem with the coordinate itself, but with the commands using. If you only leave the opt option with ModRedundant the problem should be fixed.
Hopefully this will help to somebody else.
  • asked a question related to Optimization
Question
1 answer
Could anyone please mention the inputs i require from an offgrid wind farm for the optimization of LCOH.
I am really new into this topic and currently doing research on the modelling aspect.
Relevant answer
Answer
perhaps something like equations B.24, B25 and B.26 and the predecessors-equations in ... http://dx.doi.org/10.1016/j.apenergy.2021.117481 ... might provide some initial help (same one here: )
  • asked a question related to Optimization
Question
2 answers
I have optimized several of my diastereomers on spartan (M06-2X, 311+G**, CPCM ACETONITRILE) I managed to have all positive frequencies. However I'm getting the syn isomer as the more favorable, however, my experimental data clearly indicates that the anti is the more favorable product. Can anyone suggest to me what to do? I have tried other methods and the results are the same.
Relevant answer
Answer
Thank you Michal. I will try right now your suggestions. I have invested a lot of time in this. I am working with an organic molecule furan type medium size 350 gr/mol. I just need the optimized geometry of the 2 isomers and compare the energy difference. The energy difference usually is between 0.5 to 1 Kcal/mol always favoring the syn isomer. in another case, I got a 6 kcal difference. My experimental data is clear and it is indeed the anti isomer the major product.
  • asked a question related to Optimization
Question
5 answers
Optimization is a statistical technique and done by using linear programming. it is being used in farming systems.
Relevant answer
Answer
Dear Reza
In your point 1, not everything is profit or cost. You can minimize water consumption as well of fertilizers or maximize land use.
In your point 4 you should say 'mathematical inequations' not 'equations'.
This is where the strength of LP lies
  • asked a question related to Optimization
Question
4 answers
We know that Ant Colony Optimization (ACO) and Swarm Optimization are artificial intelligence related questions.
Is it important to present the unified approach in solving other problems?
Relevant answer
Answer
Thank you, Mena Maurice Farag.
  • asked a question related to Optimization
Question
4 answers
Although I have tried certain methods to remove the error.But it still persists.
I have cleaned the disk, restart the system and use the system best suited to satisfy the computational requirements.
In addition, I also try installing it again in the system.
But still error persist.
This was suggexted by someone.
Relevant answer
Answer
Sure Sir ....
I will look into them and will let you know.
Thank you
  • asked a question related to Optimization
Question
4 answers
Hello experts
This question is shared with one of my research team.
We are dealing with an optimization problem in which the algorithm will choose the cross-section of the column (RC structure) between a lower and an upper bound (e.g., LB and UB).
We know that the obtained optimal values for the section can be for example 330mm.
Recently, we came across this phrase in a paper dealing with the same problem where the author stated that 'The column sections are square with dimensions of 250 mm × 250 mm to 1200 mm × 1200 mm, by a step of 50 mm'
Our concern is how to oblige the algorithm to choose 50mm for the next candidate within the design interval.
Anyone knows how to do it, please?
Relevant answer
Answer
To ensure that the optimization algorithm chooses a step size of 50mm within the design interval, you can incorporate a discretization constraint into the optimization problem. This constraint will restrict the possible values for the cross-section to a finite set of discrete values, with a fixed step size of 50mm.
One way to implement this constraint is to introduce a binary variable for each possible cross-section dimension, where the variable takes the value of 1 if that dimension is selected and 0 otherwise. Then, you can use a set of linear constraints to ensure that only one dimension is selected, and the selected dimension is a multiple of 50mm within the given design interval.
For example, if the design interval is [250mm, 1200mm], you can define the binary variable x_i for each possible dimension d_i within this interval, where d_i = 250mm + 50mm * (i-1) for i = 1, 2, ..., 19. Then, you can add the following set of constraints to the optimization problem:
  • Only one dimension can be selected: Sum(x_i) = 1
  • The selected dimension is within the design interval: Sum(d_i * x_i) >= LB and Sum(d_i * x_i) <= UB
  • The selected dimension is a multiple of 50mm: d_i * x_i = k * 50mm for some integer k
These constraints will ensure that the algorithm chooses a cross-section dimension within the given design interval, with a step size of 50mm, and only one dimension is selected. By adding this discretization constraint to the optimization problem, you can ensure that the algorithm will always choose the next candidate within the design interval with a step size of 50mm.
  • asked a question related to Optimization
Question
3 answers
Where is the Jade due to the throwing out of a brick and a paving stone?
A brand new conception of preferable probability and its evaluation were created, the book was entitled "Probability - based multi - objective optimization for material selection", and published by Springer, which opens a new way for multi-objective orthogonal experimental design, uniform experimental design, respose surface design, and robust design, discretization treatment and sequential optimization, etc.
It aims to provide a rational approch without personal or other subjective coefficients, which is available at https://link.springer.com/book/9789811933509,
DOI: 10.1007/978-981-19-3351-6.
Best regards.
Yours
M. Zheng
  • asked a question related to Optimization
Question
5 answers
What challenges and use cases are associated with designing an optimization xApp for O-RAN?
In O-RAN (Open RAN), xApp communicates with RIC (Radio Intelligence Controller) to impose policies on the radio access network (RAN). The RIC is responsible for managing and coordinating the DUs (Distributed Units) and RUs (Radio Units) in the RAN. The workflow for this process is as follows:
  1. xApp generates and sends a policy to the RIC.
  2. The RIC receives the policy and translates it into a series of commands.
  3. The RIC sends the commands to the relevant DUs and RUs.
  4. The DUs and RUs execute the commands and update their configurations accordingly.
  5. The RIC monitors the status of the DUs and RUs and reports back to the xApp.
  6. The xApp has access to the status of the DUs and RUs and can make updates to the policies based on the received status.
In this way, xApp has access to both DU and RU activities through the RIC. The RIC acts as an intermediary between xApp and the DUs and RUs, ensuring that policies are applied consistently and effectively across the RAN.
The Question is How can we read and check the xApp activities? its inside the RIC or logged in CU DU?
Relevant answer
Esmaeil Amiri Many Thanks
  • asked a question related to Optimization
Question
2 answers
HCP has two parameters to optimize namely a and c/a; the process detailed here gives pretty accurate result to determine those parameters. Zr is taken as an example here, where positions are special, high symmetry positions, i.e. (1/3, 2/3,1/4) and (2/3, 1/3,3/4)
i. Take 7 to 8 different volumes across the estimated equilibrium volume (from published XRD data).
ii. Range of volume scanned should be typically within +/- 10%
iii. If atom positions are in high symmetry positions then atoms should not be moved. However, in the POSCAR file set 'selected dynamics' and motion should be set to T, to get force info in OUTCAR, but set ISIF=5 in INCAR to change only the shape of the unit cell.
An example of POSCAR -
HCP Zr parama (vary this to get different volume) 1.000000000 0.000000000 0.00000000000 -0.500000000 0.866025404 0.00000000000 0.000000000 0.000000000 1.60000000000 Zr 2 Selective dynamics Direct 0.6666666666666667 0.3333333333333333 0.7500000000000000 T T T 0.3333333333333333 0.6666666666666667 0.2500000000000000 T T T
iv. INCAR setting
ISTART=0 LREAL=.FALSE. PREC=Accurate ADDGRID=.TRUE. ENCUT=550 ISPIN=2 NPAR=6 MAGMOM=2*2.0
IBRION=2 ISIF=4 POTIM=0.15 ISMEAR=1 SIGMA=0.01
NSW=100 NELM=50 EDIFF=1.0E-6
v. Fit 3rd order BM EOS to determine the equilibrium energy, volume and bulk modulus (E0, V0 and B)
vi. For each run also find the final a and c/a
vii. Fit 3rd order BM EOS between E vs. a^3 to determine the equilibrium a0^3, during this fitting keep E0 constant (found from step v)
viii. Now we have both V0 and a0, from which c0/a0 can be calculated. Use these final cell parameters to determine the energy accurately using ISMEAR = -5 in INCAR.
Relevant answer
Answer
I never tried a particular HCP structure, but i am wondering why you can't follow the regular procedure,
i.e., change 'a' first by putting some optimal guess on 'c', plot its Ground state energy as a function of 'a' to get the optimized 'a'. And then keeping this optimized 'a' fixed, change 'c' to get its optimized value. (Keep ISIF = 2, NSW = 0, and ISMEAR = 0 here)
and at the end do a final relaxation with ISIF = 2, NSW = 200, and ISMEAR = 0.
I hope this should work.
Another way can be keeping ISIF = 3, ISMEAR = 0, NSW =200 to relax the system in one single step for 3D system. But VASP developers recommend the first one.
Hope this helps!
  • asked a question related to Optimization
Question
1 answer
Can anybody help me if in finding any existing literature where I can find the energy of Polyethylene optimized structure 12 monomer units using DFT level of theory
Relevant answer
Answer
I think this is a very specific system to be found in the literature. However, since it is not a huge system, I recommend you to calculate by yourself (at DFT level it will not take so long)
Best,
Fernando
  • asked a question related to Optimization
Question
4 answers
In consideration of lipemic samples being one of the most commonly encountered interferences in routine clinical chemistry assays (Ho et al., 2021, p. 1), are there any removal methods that are immediately available for samples affected with lipemia?
According to Nikolac (2014), the CLSI recommended method is ultracentrifugation among other methods, however, this method may be quite costly. So, I’m wondering if there are methods which would be more accessible for small, remote laboratories.
Reference Articles:
  • Ho, C. K., Chen, C., Setoh, J. W., Yap, W. W., & Hawkins, R. C. (2021). Optimization of hemolysis, icterus and lipemia interference thresholds for 35 clinical chemistry assays. Practical Laboratory Medicine, 25, e00232. https://doi.org/10.1016/j.plabm.2021.e00232
  • Nikolac N. (2014). Lipemia: causes, interference mechanisms, detection and management. Biochemia medica, 24(1), 57–67. https://doi.org/10.11613/BM.2014.008
Relevant answer
In addition to ultracentrifugation, Soleimani, Mohammadzadeh, and Asadian (2020) suggest the following additional elimination techniques: lipid extraction; sample dilution; and serum blank. They have suggested using serum blanks for samples that will be utilized to measure glucose, GGT, creatinine, albumin, CPK, and amylase as serum blanks eliminate the need for ultracentrifugation in cases of low degrees of lipemia.
For laboratories encountering cases of lipemia, specifically in the moderate to severe lipemic levels, ultracentrifugation is still the optimum removal technique for lipemic samples used for assessment of calcium, magnesium, phosphorus, total protein, iron, TIBC, urea, and chloride.
Reference:
  • Soleimani, N., Mohammadzadeh, S., & Asadian, F. (2020). Lipemia interferences in biochemical tests, investigating the efficacy of different removal methods in comparison with ultracentrifugation as the gold standard. Retrieved from Journal of Analytical Methods in Chemistry, 2020, 1-6. https://doi.org/10.1155/2020/9857636
  • asked a question related to Optimization
Question
3 answers
Interpretable, credible and responsible multimodal artificial intelligence preface--DIKWP model (beyond ChatGPT)
Already 312 times read 2023-2-11 15:23 |System Classification: Paper Exchange
First answer what is artificial intelligence (Artificial Intelligence, AI)?
Subjects and objects in the entire digital world and cognitive world can be consistently mapped to the five components of the DIKWP model and their transformations: Data Graph, Information Graph, Knowledge Graph, Wisdom Map (Wisdom Graph), intention map (Purpose Graph).
Each DIKWP component corresponds to the semantic level of cognition, the concept and concept instance level of human language: {semantic level, {concept, instance}}
Model <DIKWP Graphs>
::=(DIKWP Graphs)*(Semantics, {Concept, Instance})
::={ DIKWP Graphs*Semantics, DIKWP Graphs*Concept, DIKWP Graphs*Instance }
::={ DIKWP Semanics Graphs, DIKWP Concept Graphs, DIKWP Instance Graphs }
Interactive scene <DIKWP Graphs>
::={DIKWP Content Graph includes: Data Content Graph, Information Content Graph, Knowledge Content Graph, Wisdom Content Graph, Purpose Content Graph;
DIKWP cognitive model (DIKWP Cognition Graph) includes: Data Cognition Graph, Information Cognition Graph, Knowledge Cognition Graph, Wisdom Cognition Graph , Purpose Cognition Graph.
}
Artificial intelligence is the capability part of DIKWP interaction.
AI::=(DIKWP Graphs)*(DIKWP Graphs)*
Narrow definition: Artificial intelligence is the development-oriented elimination of duplication in the DIKWP interaction, the integrated storage-computing-transmission iterative capability and the cross-DIKWP-oriented (Open World Assumption) OWA scope conversion capability.
We are conducting ChatGPT's artificial intelligence ability test and look forward to sharing it with you.
Examples of our related work:
Typed Resource-Oriented Typed Resource-Based Resource Management System (Authorized)
  Value Driven Storage and Computing Collaborative Optimization System for Typed Resources
   Application publication number: CN107734000A
For a list of all relevant invention patents, see:
List of Chinese national invention patents authorized by the DIKWP team for the first inventor Duan Yucong during the three years from 2019 to 2022 (69/241 in total)
Relevant answer
Answer
Hi,
Simply put, AI, which stands for "artificial intelligence," refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve based on the information they gather. Artificial intelligence manifests itself in many forms.
  • Les assistants intelligents utilisent l’IA pour analyser les informations critiques à partir de grands ensembles de données en texte libre afin d’améliorer la planification.
  • Chatbots use AI to understand customer problems faster and respond more effectively.
  • Recommendation engines can automatically suggest TV shows based on viewers' habits.
Best regards
  • asked a question related to Optimization
Question
3 answers
Hello dear researchers.
Please, how do I know that I have done a good optimization of the structure (I use abinit), should I see something in the output file or just compare the cell parameters with the experimental values?
Thank you in advance.
Sincerely.
Relevant answer
Answer
Hello,
There are some important tutoriel in the website of Abinit, see this link:
  • asked a question related to Optimization
Question
4 answers
Hello! I am going to conduct a study on the application of Vehicle Routing Problem in real world. However, I am struggling with how I construct my networks. I would like to ask on how to define the edges/arcs/links between the vertices.
For example, what should the edge between cityA and cityB represent? Most literatures use travel times which is based on road distances from cityA to cityB. However, there are a lot of paths from cityA to cityB. The way to address this is to use the shortest path from cityA to city B. Are there any alternatives to address this issue? What should the edge between two vertices represent?
Relevant answer
Answer
You can use the real distance or travel time of the best path that link each pair of cities. Usually, travel times are calculated based on the real distance and an assumed vehicle average velocity.
You always need to calculate the Cost-Matrix between all the cities (nodes) as instance data. To make this possible recommend OSRM or Vallhalla or any proprietary solution like ArcGIS Desktop Network Analyst
  • asked a question related to Optimization
Question
5 answers
This new way of accessing information through a reward model for reinforcement learning requires collecting comparison data, which consists of two or more model responses classified by quality. Data collection is performed through conversations that the AI trainers had with the chatbot, enhanced by Reinforcement Learning with Human Feedback. A written message is randomly selected per model, trying out several alternative conclusions and asking the AI trainers to rate them.
The use of reward models can adjust the model using Proximal Policy Optimization. Models such as GPT-3 and Codex are also implemented.
Relevant answer
Answer
António Brandão ChatGPT, as a big language model, has the potential to transform the way we engage with the Internet in several ways:
1. Personalization: ChatGPT may be trained to recognize the context of a discussion and answer in a tailored manner, giving the user with more accurate and relevant information.
2. Speed: Using language models such as GPT-3 and Codex, ChatGPT can swiftly interpret and react to user questions, making information access faster and more efficient.
3. Interactivity: ChatGPT may be linked to a variety of platforms, including websites and applications, to give users an interactive experience. This can make information access more interesting and user-friendly.
4. Improved performance: By combining reward models and reinforcement learning with human input, ChatGPT's performance can be constantly improved, making it more accurate and dependable over time.
5. Better outcomes: Using Proximal Policy Optimization, the model may adjust itself to the feedback supplied by human trainers, resulting in better results for end users.
The combination of these qualities can improve the user experience by making information access over the Internet more customized, rapid, interactive, and accurate.
  • asked a question related to Optimization
Question
6 answers
How can I calculate ANOVA table for the quadratic model by Python?
I want to calculathe a table like the one I uploaded in the image.
Relevant answer
Answer
To calculate an ANOVA (Analysis of Variance) table for a quadratic model in Python, you can use the statsmodels library. Here is an example of how you can do this:
#################################
import statsmodels.api as sm
# Fit the quadratic model using OLS (Ordinary Least Squares)
model = sm.OLS.from_formula('y ~ x + np.power(x, 2)', data=df)
results = model.fit()
# Print the ANOVA table
print(sm.stats.anova_lm(results, typ=2))
#################################
In this example, df is a Pandas DataFrame that contains the variables y and x. The formula 'y ~ x + np.power(x, 2)' specifies that y is the dependent variable and x and x^2 are the independent variables. The from_formula() method is used to fit the model using OLS. The fit() method is then used to estimate the model parameters.
The anova_lm() function is used to calculate the ANOVA table for the model. The typ parameter specifies the type of ANOVA table to compute, with typ=2 corresponding to a Type II ANOVA table.
This code will print the ANOVA table to the console, with columns for the source of variance, degrees of freedom, sum of squares, mean squares, and F-statistic. You can also access the individual elements of the ANOVA table using the results object, for example:
#################################
# Print the F-statistic and p-value
print(results.fvalue)
print(results.f_pvalue)
#################################
I hope that helps
  • asked a question related to Optimization
Question
3 answers
For difficult to transduce adherent cell lines, I was wondering if anybody has tried spinoculation with polybrene? If so, what is the protocol you used?
Relevant answer
Answer
Susan C Hubchak Hi Susan, thank you for your response. Have you tried spinning any adherent cells with polybrene before? If so, what was the recommended time of spinning and conditions?
  • asked a question related to Optimization
Question
3 answers
Please advise a reference to paper/book/sources with description of Complex value test functions for optimization like extended Rosenbrock or Powell? There are many analytical functions for testing non-linear/optimization algorithms but I was able to find only cases for real numbers. So the question is how to test a case when Jacobian/residual are complex value matrix/vector?
Relevant answer
Answer
You can look into the following Book:
Complex Valued Matrix Derivatives : With applications in Signal processing and Communication, by Are Hjorungnes.
  • asked a question related to Optimization
Question
5 answers
linear least squares problem::
fit f(t) = sin(t) on the interval [0, π] with polynomials of degree n with n = 1, 2, 3, · · · , 10. Here, we use equally spaced nodes.
Solve the normal equation with a backslash in Matlab. Save the norm of the residual and the condition numbers of AT A??
Could anybody please tell me how can find x, y, and A in that case ??
Relevant answer
Answer
The question seems incorrect. We needn't find x and y.
We have {(xi, yi = sin(xi))}, i=1,...,N and seek (a_n, ..., a_0) for a polynomial function
f(x) = a_n * x^n + ... + a_0.
Least Square minimization gives
A=
( Sum(xi^n) Sum(xi^(n-1)) ... Sum(xi^0);
Sum(xi^(n+1)) Sum(xi^(n-2)) ... Sum(xi^1);
.............................................
Sum(xi^(2*n)) Sum(xi^(2*n-1)) ... Sum(xi^n);
);
b = (Sum(yi); Sum(xi*yi);); ... Sum(xi^n*yi) );
At last,
(a_n a_(n-1) ... a_0) = A^(-1)*b
  • asked a question related to Optimization
Question
6 answers
Hello, I've been tasked with validating a 2kb region of DNA. A colleague designed primers with the F melting at 53.8 and the reverse at 59.7. I believe they used 54C which had multiple bands show up in their gel due to specificity issues. I am trying to optimize the reaction but don't know what temp would give the best results. Redesigning was my first thought but that's not an option. My plan now is to start at 56, see what I get if its still not specific enough than increase until I get clear bands. The problem however is this will be quite time consuming and will waste resources. Any input on next steps would be greatly appreciated.
Thank you
Relevant answer
Answer
I agree that redesigning the primers to a higher annealing temperature is the best option. One other possibility is to run a sample with increasing amounts of dmso from 0 to 8%dmso which can often clean up a pcr reaction in much the same way as an annealing temperature gradient
  • asked a question related to Optimization
Question
3 answers
I am working on topology optimization for photonic devices. I need to apply a custom spatial filter on the designed geometry to make it fabricable with the CMOS process. I know there exist spatial filters to remove the pixel-by-pixel and small features from the geometry. However, I have not seen any custom analytical or numerical filters in the literature. Can anyone suggest a reference to help me through this?
Thanks,
Relevant answer
Answer
Many thanks for your reply.
The list of papers you've provided perfectly developed the concept, particularly the tutorial paper that goes through the optimization problem step by step.
  • asked a question related to Optimization
Question
1 answer
Hello Researchers,
I am Marwen Amiri, A PhD student, my subject is "Optimization of RFID systems used in wireless body networks" it is a purely "internet of things" subject, I wanted to know if it would be possible to simulate WBAN networks with the physiological parameters of a human body using NetSim or Omnet ++ or another simulator ? i want to modify the TxPower and the distance between sensors to evaluate the performance of a WBAN network in terms of PLR, latency and energy consumption. The main objective is to determine some objectives of optimization by simulation and testing of some algorithms of multiobjective optimization (Node coverage, interference, control of Txpower, number of sinks, number of nodes, etc...)
if it's possible, I wanted to know also, how can I find the library of a Phantom of a human body with its physiological parameters to use it in one of the iot simulators. In fact, I found several simulations of BAN networks on Youtube, but without source code, since we want to work on a human body with physiological parameters to perform our simulations with normal or RFID sensor nodes.
Can you help me with information, papers or contacts ? I did not find many resources in this area of ​​research
Cordially
Relevant answer
Answer
The CSV / XL file has readings of from 11 different sensors measuring physiological parameters such ECG, EEG, Airflow, …, etc.
  • asked a question related to Optimization
Question
4 answers
There are various steps involved in western blot analysis. Any tips or optimization tricks you might have used that worked and made work easy for you. Please kindly, drop in in the discussion. For new and Old Grad students that are working with old equipments and not able to afford already- made reagents. Thanks
Relevant answer
Answer
Thank you. Your contribution is greatly appreciated. I know this will help a student someday.
  • asked a question related to Optimization
Question
7 answers
In his name is the judge
Hi
There is a fuzzy logic control system in python. The system contain 2 inouts and 18 outputs.
inference of system is mamdani and shape function used to be guassian.
Then in term of refine performance of the controller I need to optimize specifications belong to shape functions of both input and output. In order to that I need to use multi objective optimization.
We have 2 input and 1 output in case of this problem. I have developed 3 shape functions for each entrance and 3 for output and the shape function is gaussian so we have 18 parameters totally.
I defined my problem as a function in python. But notice this I there is not any clear relationship between input and output of function. It’s just a function which is so complicated with 2 inputs and 18 outputs.
I made my decision to use NSGAII algorithm and I really don't want to change the algorithm.
So I try every way to optimize my function but I didn’t find any success. By searching about python library which can do multiobjective optimization I find Pymoo as the best solution but I really failed to optimize my function which is complicated custom function, with it.
So It’s really my pleasure if you can introduce me a library in python or suggest me a way that I can use Pymoo in order to this aim.
wish you best
Take refuge in the right.
Relevant answer
Answer
You can stick with Mamdani Fuzzy System if it is comfortable for you to design a "workable" FIS. You probably don't need optimization if your FIS works satisfactorily. From the "workable" FIS to the "satisfactory" FIS, you probably just need make minor adjustment to the design parameters. That's the Spirit of Engineering Design.
  • asked a question related to Optimization
Question
3 answers
I am trying to optimize zeolite bulk structure with some of the silicon atoms being replaced by Al in VASP. But it's not converging and the job stops after 1 iteration. There is no error message also. I am using vdw-RE tag. What should I do to converge it successfully?
Relevant answer
Answer
Sure, I am attaching INCAR and the POSCAR files below.
  • asked a question related to Optimization
Question
1 answer
I have L16 for Machining and some researchers do RSM after GRA using GRG data.
How we can do that?
Thanks
Relevant answer
Answer
@Asim You can do GRA irrespective of RSM or L16 Taguchi. GRA is applied where you have multiple responses and if you need to find out the optimal responses
  • asked a question related to Optimization
Question
8 answers
When I try to perform the following calculation, Python gives the wrong answer.
2*(1.1-0.2)/(2-0.2)-1
I have attached a photo of the answer.
Relevant answer
Answer
Mathematically, the answer to the equation is zero; the answer Python spat out is pretty much as close as you can get to the representation of zero with a typical computer.
This is a classic floating point problem: https://en.wikipedia.org/wiki/Floating-point_error_mitigation
  • asked a question related to Optimization
Question
10 answers
I am working on a non-linear optimization problem. Even though I wrote the code correctly and it doesn't give an error message, the optimal values are zero. please if there is someone who masters in lingo model help me .
Thank you
Relevant answer
Answer
Dear Sima
Thank you for sending me your material
As I told you I don't know LINGO. I work with Solver in Excel.
As a first difference LINGO puts alternatives in rows and criteia in columns, which is exacly opposite as Solver, but of course, it does not influence in the result.
Inspecting your matrix, I notice that are many symbols that you dont say what they mean. For instance
Pi, Yj,Pw, W, Pin
Of course, the sum of performance values for un criterion should be ≤ since you arf maximizying.
An issue that called immediately my attention is the large value for 📷
It appears that there is a contradiction here, because in your first column you establish
📷
📷
The same for the other columns
That is, in one inequation the sum of all performance values in column 1, should be less or equal to 6750, and in the other inequation you say that the weighted sum should be less or equal to 56850000.
Unfortunately, I don’t know the meaning of W, which I guess are weights (although you don’t need weights in LP), but it seems that it must comply simultaneously with two very different values. If it is ≤ 6750, I don’t see how it could be less than 56850000.
Obviously, the result is zero because this is not feasible.
I don’t understand the expression of your objective function. Pls. explain
It would help if I could k now the meaning of each criterion.
I hope it helps
  • asked a question related to Optimization
Question
1 answer
I'm searching for new collaborations. I'm focused mostly on numerics on obtaining solitons bifurcating from band edges and localized modes. I'm specialized in shooting methods with more than one parameter, NR optimized for CUDA obtaining solutions, parametric curve step to obtain branches that cannot be described by a function. Optimization of split-step FFT dynamics with coupled Nonlinear partial differential equations, Ex: SHG was the harder one. I worked previously with V. Konotop, F. Abdullaev and B. Malomed. I was first author on all papers. They provided the equations and researched on obtaining solutions.
  • asked a question related to Optimization
Question
9 answers
Although the genetic algorithm is very helpful to find a good solution, it is very time-consuming. I've recently read an article in which the authors used the genetic algorithm for pruning a neural network on a very small dataset. They pruned a third of their network and reduces the size of the model from 90MG to 60MG. Their pruning also decreased the inference time of their model by around 0.5 seconds. Although, unlike other methods, pruning using the genetic algorithm does not deteriorate their model's performance, finding redundant parts of the network using the genetic algorithm is an overhead. I wonder why they think their method is useful and why people still use the genetic algorithm.
Relevant answer
Answer
Genetic algorithms have the several characteristics over others such as , (1) Natural selection and natural genetics are at the heart of the Genetic Algorithm. -(2)These are simple to comprehend and use. -(3) These are frequently encountered in optimization issues. (4)These are based on a population of points rather than a single point. -(5) Instead of deterministic rules, these utilize probabilistic transition rules. -(6)Genetic algorithms can handle a large number of variables efficiently. - (7)No derivative information is required for these methods. -(8)These are more successful when dealing with complicated difficulties than when dealing with basic problems.
  • asked a question related to Optimization
Question
7 answers
Please answer the question or recommend some articles.
Relevant answer
Answer
The NFLT is valid only on a set of problems closed under permutations (c.u.p.). It never happens in practice.
And for a set of problem that is _not_ c.u.p. it can be proved that there exists a best algorithm.
Unfortunately the proof is not constructive.
So it is worth trying to improve algorithms and, indeed, we need to know how to compare then.
Here is what I usually do for two stochastic algorithms A1 and A2 on a given problem:
- run 100 times A1, with a given search effort (usually a number of evaluations), plot the CDF_A1 (cumulative distribution function) of the 100 final best results.
- do the same for A2 => CDF_A2
If, on the figure, CDF_A1 is completely "above" CDF_A2, then A1 can safely be said "better", for this function.
And vice versa, of course.
If the two curves cross on say a value r, the conclusion is not that clear, unless you consider only best final values smaller than r. Then you have to be more precise. Something like: "If I accept only final results smaller than r, then _this_ algorithm is better".
  • asked a question related to Optimization
Question
4 answers
I have trained a regression svm model for a set of experimental data which has 4 parameters - initial concentration, pH, T and dosage. The response is equilibrium loading. Now, I want to use the trained model to get optimal conditions for the same, i.e., to find the experimental parameters at which we get maximum equilibrium loading. Since, a svm model doesn't give the parameters of the model it would be difficult to find the parameters. Kindly suggest some ways to do this.
Relevant answer
Answer
  • asked a question related to Optimization
Question
3 answers
Dear friends,
Would you please tell me where I can find a dynamic list (updated constantly) of new meta-heuristic algorithms?
Thanks in advance for your guidance,
Reza
Relevant answer
Answer
For a humorous treatment, see the "bestiary":
  • asked a question related to Optimization
Question
3 answers
I am looking for examples of using home energy storage to reduce energy peaks on a daily basis in the case of PV. There is a need to use many home energy storage (several hundred thousand in Poland) to cut PV energy peaks.
Relevant answer
Answer
  • asked a question related to Optimization
Question
2 answers
In robust optimization, random variables are modeled as uncertain parameters belonging to a convex uncertainty set and the decision-maker protects the system against the worst case within that set.
In the context of nonlinear multi-stage max-min robust optimization problems:
What are the best robustness models such as Strict robustness, Cardinality constrained robustness, Adjustable robustness, Light robustness, Regret robustness, and Recoverable robustness?
How to solve max-min robust optimization problems without linearization/approximations efficiently? Algorithms?
How to approach nested robust optimization problems?
For example, the problem can be security-constrained AC optimal power flow.
Relevant answer
Answer
To tractably reformulate robust nonlinear constraints, you can use the Fenchel duality scheme proposed by Ben Tal, Hertog and Vial in
"Deriving Robust Counterparts of Nonlinear Uncertain Inequalities"
Also, you can use Affine Decision Rules to deal with the multi-stage decision making structure. Check for example: "Optimality of Affine Policies in Multistage Robust Optimization" by Bertsimas, Iancu and Parrilo.
  • asked a question related to Optimization
Question
3 answers
In his name is the judge
Hi
I want to learn multi-objective optimization with NSGAII in python for my research.
Please recommend a good source for learning NSGAII in python.
wish you best
Take refuge in the right.
Relevant answer
Answer
thank you for your suggestion.
  • asked a question related to Optimization
Question
8 answers
How does one optimize a set of data which is comprised of 3 input variables and 1 output variable (numerical variables) using a Genetic Algorithm? and also how can I create a fitness function? How is a population selected in this form of data? What will the GA result look like, will it be in a form of 3 inputs and 1 output?
I do understand how the GA works, however, I am confused about how to execute it with the form of data that I have.
My data is structured as follows, just for better understanding:
3 columns of Input data, and the fourth column of Output Data. (4 Columns and 31 rows) data. The 3 input variables are used to predict the fourth variable. I want to use GA to improve the prediction results.
Lastly, Can I use decimal numbers (E.g. 0.23 or 24.45); or I should always use whole numbers for chromosomes.
Relevant answer
Answer
Decide first whether you want to improve the features or parameters of an algorithm or both? When designing your chromosome in terms of features You must first create a population of candidates that you can do randomly .... On some chromosomes you may want to turn off some features or on some features you can use 1 or 0 for this. Zero to indicate an added feature and to exclude a feature from a given set. In the case of parameters, the value of the chromosome can be a decimal point in the given range.
  • asked a question related to Optimization
Question
9 answers
I am using a hybrid optimization algorithm (Grey Wolf + Cuckoo Search) to find the optimal size of hybrid renewable energy system based on Total Net Present Cost of the system. Details of Optimization Problem are as follows:
Objective Function: TNPC[$]: f(Number of components)=f(N_pv N_wt N_bat N_inv)
Lower Bound: [1 1 1 1] Upper Bound: [1000 1000 100 100]
Constraints: 1. System size is within allowed min. and max. system size. 2. battery SOC remains within allowed limit
The Algorithm do end up with the least system cost however the optimal size of system comes to be just equal to lower bound values i.e. optimal size of system: [1 1 1 1] .
Optimization Algorithm Convergence Curve is attached.
Relevant answer
Answer
well, Muhammad Abdullah Malik, shortly, the number of some usual steps in a modeling process is 6-7-8 or more
steps like: data collection, model development, solution, verification, validation, implementation etc.
so, technically, based on the goals that you want to achieve with this specific model, you are currently positioned at the verification-validation stage; that is, it could be the case that any of the near-optimal or sub-optimal solutions is okay for some particular cases, or !! it could be the case that you have to go back to the initial step for data collection plus some additional model adjustments, etc. - and, it is your job: to answer such questions or to resolve any similar issues related to the model goals (like "search space was too small") - sorry, for the general writing.
  • asked a question related to Optimization
Question
2 answers
I have running GA optimization using "gamultiobj" in Matlab. The upper bound and lower bound of my design variables are like [10 30 175 1] and [30 60 225 3]. But after convergence, I see the design variables (like all of them) near 20,47,175 and 1. I am not getting a Pareto Front. What could be the possible reasons for that?
Relevant answer
Answer
Can you please elaborate on your objective functions?
  • asked a question related to Optimization
Question
3 answers
I've been using GMAT in my free time to optimize trajectories, and have varied burn component values and spacecraft states, usually with success. The vary command in GMAT, with the Yukon optimizer that I am using, has the following parameters that can be changed:
  • Initial value: The initial guess. I know the gradient descent optimization method that GMAT uses is very sensitive to initial conditions and so this must be feasible or reasonably close.
  • Perturbation: The step size used to calculate the finite difference derivative.
  • Max step: The maximum allowed change in the control variable during a single iteration of the solver.
  • Additive scale factor: Number used to nondimensionalize the independent variable. This is done with the equation xn = m (xd + a), where xn is the non-dimensional parameter, xd is the dimensional parameter and this parameter is a.
  • Multiplicative scale factor: Same as above, but it's the variable d in the equation.
For the initial value, I can usually see when my chosen value is feasible by observing the solver window or a graphical display of the orbit in different iterations. The max step is the most intuitive of these parameters for me, and by trial and error, observation of the solver window and how sensitive my target variables are to changes in the control variables I can usually get it right and get convergence. It is still partially trial and error though.
However, I do not understand the effect of the other parameters on the optimization. I read a bit about finite difference and nondimensionalization/rescaling, and I think I understand them conceptually, but I still don't understand what values they have to be to get an optimal optimization process.
This is especially a problem now because I have started to vary epochs (TAIModJulian usually) or time intervals (e.g. "travel for x days" and find optimal x, or to find optimal launch windows), and I cannot get the optimizer to vary them properly, even when I use a large step size. The optimizer usually stays close to the initial values, and eventually leads to a non-convergence message.
I have noticed that using large values for the two scale factors sometimes gives me larger step sizes and occasionally what I want, but it's still trial and error. As far as perturbation goes, I do not understand its influence on how the optimization works. Sometimes for extremely small values I get array dimension errors, sometimes for very large values I get similar results to if I'm using too large a max step size, and that's about it. I usually use 1e-5 to 1e-7 and it seems to work most of the time. Unfortunately information on the topic seems sparse, and from what I can tell GMAT's documentation uses different terminology for these concepts than what I can find online.
So I guess my question is two-fold: how to understand the optimization parameters of GMAT and what they should be in different situations, and what should they be when I want GMAT to consider a wide array of possible trajectories with different values of control variables, especially when those control variables are epochs or time intervals? Is there a procedure or automatic method that takes into account the scale of the optimization problem and its sensitivity, and gives an estimate of what the optimization parameters should be?
Relevant answer
Answer
Not sure what you are looking for but I think you can use Automatic Differentiation libraries like #Casadi #yulmip, I prefer Casadi, because it is easy to use, you can simply define your objective function and your constraints
  • asked a question related to Optimization
Question
3 answers
In your opinion, what are the main aspects of Econo-Mathematical Optimization ?
Relevant answer
Answer
More details please... single variable, multi-variate, linear, nonlinear, constrained, unconstrained, etc.
  • asked a question related to Optimization
Question
6 answers
I have designed the optimization experiment using Box-Behnken approach.
What should I do if any of the factors combination fails, for example because the aggregation occurs.
Should I review whole optimization or is there any method to skip the particular factors combination?
And if I need to review the whole experiment, what method should I use to evaluate boundary factors values? Screening methods I have seen require at least 6 factors to be screened.
Any help is appreciated.
Greetings.
Relevant answer
Answer
If particular factor combinations are leading to "loss of data" because you can not evaluate it, then you can set so-called constraints in your future DOE. As a result, you will work with D- (in screening) or I-Optimal (in optimization) Designs. You need these computer generated designs in your case, because the design is not orthogonal so classical factorial designs are not appropriate.
  • asked a question related to Optimization
Question
6 answers
I have generated 16 variable probability distributions in the form of a 16 dimensional NumPy array in python. How could I determine all the peaks in this function in python or using some software?
Relevant answer
Answer
Thank you all for the clarifications
  • asked a question related to Optimization
Question
2 answers
Hello everyone, I am looking for a good MPC quadratic optimization mathematical model to optimize a cost function or performance index, for a battery energy storage state space model. Would anybody suggest a good research paper or post a formulation that contains a good mathematical model for quadratic optimization? An objective function with viable constraints, which can be possible to implement in function solvers such as quadprog or cplex would be ideal. Thank you
  • asked a question related to Optimization
Question
7 answers
Could you recommend me any journals with examples? There is any easy way to link/call CPLEX code to MATLAB code?
Relevant answer
Answer
Does anybody know CPLEX can be installed for MATLAB in MAC? I only see directions for windows
  • asked a question related to Optimization
Question
17 answers
I want to compare two theorems and see which one has the largest feasibility domain. like the attached picture.
for example, I have the following matrices: A1=[-0.5 5; a -1]; A2=[-0.5 5; a+b -1]; they are a function of 'a' and 'b' I want to plot according to 'a' and 'b' the points where an LMI is  feasible for example the following LMI Ai'*P+P*Ai<0
then I want to plot the points where another LMI is  feasible, for example:
Ai'*Pi+Pi*Ai<0
I have seen similar maps in many articles where the authors demonstrated that an LMI is better than another because it is applicable for more couples (a,b)
Relevant answer
Answer
Usually, these problems are easily solved by YALMIP toolbox in MATLAB.
Here, I write the following pseudocode for your problem:
It can be solved by defining two 'for-loop' as follows:
for min(a)<a<max(a)
for min(b)<b<max(b)
solve the proposed LMI-based optimization problem
if the LMI problem is feasible
figure(1)
hold on
plot(a,b,'.r')
end
end
end
OR, you can use the following sample:
for a = 0:1:15
for b = -12:1:0
yalmip('clear')
% Model data
A1 = [a 0.02;0.35463 0.2035];
A2 = [0.7025 0.02;0.2525 0.1025];
sdpvar P1(2);
sdpvar P2(2);
con1 = P1>=0;
con2 = P2>=0;
con3 = A1'*P1+P1*A1<0
con4 = A2'*P2+P2*A2<0
constraints = con1+con2+con3+con4;
opt = sdpsettings('solver','mosek','verbose',0);
optimize(constraints,gamma,opt);
P1 = value(P1);
P2 = value(P2);
eigP1 = min(eig(P11));
eigP2 = min(eig(P12));
if eigP1>0 && eigP2>0
figure(1)
hold on
plot(a,b,'.k','MarkerSize',5)
end
end
end
  • asked a question related to Optimization
Question
3 answers
I am trying to code this optimizer for a linear regression model. What i want to confirm from is that the update of model parameter are happening even if they cause the increase in cost function, isn't ?
Or we only update the coefficients values if they decreased the value of the cost function?
  • asked a question related to Optimization
Question
2 answers
I have a new idea (by a combination of a well-known SDP formulation and a randomized procedure) to introduce an approximation algorithm for the vertex cover problem (VCP) with a performance ratio of $2 - \epsilon$.
You can see the abstract of the idea in attached file and the last version of the paper in https://vixra.org/abs/2107.0045
I am grateful if anyone can give me informative suggestions.
  • asked a question related to Optimization
Question
7 answers
In the optimization of gas turbine cycle, many researchers have used isentropic efficiency of gas turbine and air compressor as decision variables. Even I did the same. But recently while submitting a paper I got one comment from the reviewer which really made me think.
The reviewer comment:
"AC and GT isentropic efficiency are used as optimization parameters. Are these easily controllable metrics? The other metrics (pressure ratio and temperatures) are but I wonder about the isentropic efficiencies."
How should I justify?
Relevant answer
Answer
No isentropic efficiencies are not the optimization parameters; there is not much control and also not wide range of this efficiency. It depends on the design of the compressor and turbine. Rather you should treat them as external parameters and concentrate on heat exchanger efficiency (regenerator efficiency), maximum cycle temperature, two or three stage compression with intercooling, reheating point, pressure ratio, etc.
  • asked a question related to Optimization
Question
7 answers
Hi. I'm going to optimize the layout design of the satellite with Abaqus and Isight. I designed and analyzed the Abaqus model, which is pinned below. Now I want to enter my model to Isight to optimize satellite, but I face a big obstacle. The Abaqus should enter the reference points and constraints points in Isight to optimize the parts' location, but there is nothing like RF points or constraints points. I couldn't find a way to solve this problem. May someone helps me to figure it out?
Relevant answer
Answer
Thank you for sharing the finite element model. Do you consider additional constraints such as natural frequencies, moments of inertia, center of gravity?
  • asked a question related to Optimization
Question
8 answers
When using Wasserstein balls to describe the uncertainty set in distributionally robust optimization, can multiple sources of uncertainty be considered at the same time, such as wind power and solar power forecast error?
  • asked a question related to Optimization
Question
4 answers
Hi
I'm working on a research for developing a nonlinear model (e.g. exponential, polynomial and...) between a dependent variable (Y) and 30 independent variables ( X1, X2, ... , X30).
As you know I need to choose the best variables that have most impacts on estimating (Y).
But the question is that can I use Pearson Correlation coefficient matrix to choose the best variables?
I know that Pearson Correlation coefficient calculates the linear correlation between two variables but I want to use the variables for a nonlinear modeling ,and I don't know the other way to choose my best variables.
I used PCA (Principle Component Analysis) for reduce my variables but acceptable results were not obtained.
I used HeuristicLab software to develop Genetic Programming - based regression model and R to develop Support Vector Regression model as well.
Thanks
Relevant answer
Answer
Hello Amirhossein Haghighat. The type of univariable pre-screening of candidate predictors you are describing is a recipe for producing an overfitted model. See Frank Harrell's Author Checklist (link below), and look especially under the following headings:
  • Use of stepwise variable selection
  • Lack of insignificant variables in the final model
There are much better alternatives you could take a look at--e.g., LASSO (2nd link below). If you indicate what software you use, someone may be able to give more detailed advice or resources. HTH.
  • asked a question related to Optimization
Question
5 answers
I wish to extend a paper by incorprating the particular feature the authors havent used or considered. However after going through the litreature It isnt clear how much that particular feature plays a role, all I know it does play an very important role for the output that I care about. For experimentation I am assuming a simple linear regression function ax+by where a serves as the contribution to the paper I am extending and x its feature set, my goal is to find the parameter b (mse minimization) by encoding the feature in variable y and thus determine the strength that y plays
However there are some limitation first of that I am assuming the relationship be linear which is very wide of assumption , and I m hoping to consider some kind of non linearity
Question is how do I proceed from here. Is there any mathematical equation I can consider as intial assumption
PS: Note Y is here a continous value not categorical
Relevant answer
Answer
In my view scientific research is about explaining or predicting phenomena. And that is impossible without the use of models, whether they are implicit, or explicit.
And the data are only "visible" by using , whether you are aware of that or not, models. And very often the language of mathematics is used to generate these models. In that sense we all are descendants of Isaac Newton.
Very often I do not see any explicit model, but instead a multitude of procedures in order to process raw data, and basically all is then a question of curve fitting, or one step ahead prediction, where the curve is not known, and the measure of goodness of fit is often not clear at all. And the scientists, that use these models very often do not have a clue about the relation between data and model.
And people from statistics or machine learning barely speak the language of each other. There is hard work to do in University, to redress this, and to partly destroy the Tower of Babel. But as long as we get our papers published , while nearly no one is reading them, the atomization of research will continue.
And that w