I wanted to solve the following equation in Anylogic:
0=44.00*m^6+7.84*m^4-43.2*m^3+7.84*m^2+1.16
Anylogic have a lot of Numeric tools. Can somebody help me how to use this tools?
Jan
You could use an online calculator to solve it, e.g. Symbolab, which will also detail the steps, which may give you sufficient information to construct a solution in AnyLogic.
It solves it thus.
Related
I'm working on a problem that will eventually run in an embedded microcontroller (ESP8266). I need to perform some fairly simple operations on linear equations. I don't need much, but do need to be able work with points and linear equations to:
Define an equations for lines either from two known points, or one
point and a gradient
Calculate a new x,y point on an equation line that is a specific distance from another point on that equation line
Drop a perpendicular onto an equation line from a point
Perform variations of cosine-rule calculations on points and triangle sides defined as equations
I've roughed up some code for this a while ago based on high school "y = mx + c" concepts, but it's flawed (it fails with infinities when lines are vertical), and currently in Scala. Since I suspect I'm reinventing a wheel that's not my primary goal, I'd like to use someone else's work for this!
I've come across CGAL, and it seems very likely it's capable of all this and more, but I have two questions about it (given that it seems to take ages to get enough understanding of this kind of huge library to actually be able to answer simple questions!)
It seems to assert some kind of mathematical perfection in it's calculations, but that's not important to me, and my system will be severely memory constrained. Does it use/offer memory efficient approximations?
Is it possible (and hopefully easy) to separate out just a limited subset of features, or am I going to find the entire library (or even a very large subset) heading into my memory limited machine?
And, I suppose the inevitable follow up: are there more suitable libraries I'm unaware of?
TIA!
The problems that you are mentioning sound fairly simple indeed, so I'm wondering if you really need any library at all. Maybe if you post your original code we could help you fix it--your problem sounds like you need to redo a calculation avoiding a division by zero.
As for your point (2) about separating a limited number of features from CGAL, giving the size and the coding style of that project, from my experience that will be significantly more complicated (if at all possible) than fixing your own code.
In case you want to try a simpler library than CGAL, maybe you could try Boost.Geometry
Regards,
I have an optimization problem that is subjected to linear constraints.
How to know which method is better for modelling and solving the problem.
I am generally asking about solving a problem as a satisfiability problem (SAT or SMT) vs. Solving as a linear programming problem (ILP OR MILP).
I don't have much knowledge in both. So, please simplify your answer if you have any.
Generally speaking, the difference is that SAT is only trying for feasible solutions, while ILP is trying to optimize something subject to constraints. I believe some ILP solvers actually use SAT solvers to get an initial feasible solution. The sensor array problem you describe in a comment is formulated as an ILP: "minimize this subject to that." A SAT version of that would instead pick a maximum acceptable number of sensors and use that as a constraint. Now, this is a satisfiability problem, but not one that's easily expressed in conjunctive normal form. I'd recommend using a solver with a theory of integers. My favorite is Z3.
However, before you give up on optimizing, you should try GMPL / GLPK. You might be surprised by how tractable your problem is. If you're not so lucky, turn it into a satisfiability problem and bring out Z3.
What is the correct parameter to give an initial solution to a LP problem in IBM ILOG CPLEX using OPL.
I'm modelling a large scale school timetabling problem and as a way of reducing the total solution time, i'd like to begin the process with an initial solution, i.e last year's timetable.
You have to use IloOplCplexVectors for that purpose. The example shipped in opl/examples/opl/warmstart/warmstart.mod does exactly what you have in mind. It should help to get you going.
I am a university student and trying to write a program to solve 1-D Schrodinger's equation with some kinda of potential equations.
I am not a CS major so really have no clue to start.
I did research online but didn't find thing that is suitable for entry level :( The only thing I understand now is that I probably need to use some algorithm solving differential equations
Could anyone give me some suggestions or references on how I could start? For example, how to transform the physical problem into computer science program and what kinda algorithm I should look for?
I am interested in computational so trying on this HARD problem ;)
Thank you all!
go to www.google.com
search for :how can i solve Schrodinger in matlab
go to here
Return stackoverflow
How do u decide on a test statistic while developing a test for random number testing and its likely distribution. How do u calculate and decide on the formula for calculating a p value for the test statistics distribution.
TIA
What is the likely distribution in the first place? Is it a normal one, a uniform one, ... I guess that random numbers should be uniformily distributed, and this is something you can test by using the appropriate Kolmogorov-Smirnov test. Basically you decide on that by applying the correct statistical methods. If you don't know the KS test, please ask advice to a statistician. This is not something you can easily implement and interprete yourself without some decent guidance a website like this cannot give you.
By the way, if you take enough random numbers, any test will show a deviation from the proposed distribution. The best way of judging a distribution is an expert look at a QQ-plot. This is all done very easily in R.
If you need more information, ask a more detailed question at stats.stackexchange.com
Cheers