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# Optimization - Science topic

Explore the latest questions and answers in Optimization, and find Optimization experts.

Questions related to Optimization

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.

Can anyone suggest me some new optimization techniques and thair matlab codes.

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?

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.

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?

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.

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?

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?

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.

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!

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.

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.

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.

Optimization is a statistical technique and done by using linear programming. it is being used in farming systems.

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?

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.

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?

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

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:

- xApp generates and sends a policy to the RIC.
- The RIC receives the policy and translates it into a series of commands.
- The RIC sends the commands to the relevant DUs and RUs.
- The DUs and RUs execute the commands and update their configurations accordingly.
- The RIC monitors the status of the DUs and RUs and reports back to the xApp.
- 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?

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.

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

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

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)

Please visit: https://blog.sciencenet.cn/blog-3429562-1354842.html

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.

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?

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.

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.

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?

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?

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 ??

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

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,

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

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

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.

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?

I have L16 for Machining and some researchers do RSM after GRA using GRG data.

How we can do that?

Thanks

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.

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

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.

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.

Please answer the question or recommend some articles.

I am trying to Optimize TiO2 bulk (Anatase), I need an examined INCAR file.

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.

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

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.

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.

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.

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.

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.

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?

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?

In your opinion, what are the main aspects of Econo-Mathematical Optimization ?

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.

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?

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

Could you recommend me any journals with examples? There is any easy way to link/call CPLEX code to MATLAB code?

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)

Hi..anybody can suggest best tool to do a feature selection using Fruit Fly Optimization?

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?

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.

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?

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?

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？

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

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