Our first paper "Boiling Point Models of Alkanes" has been accepted for publication in the journal MATCH which publishes papers in Mathematical Chemistry. Our current project involves the study of melting points of chemical molecules of interest in the production of synthetic diesel fuel. This diesel fuel project was motivated by discussions that I had with a chemical engineer at the University of Pittsburgh.
waste. The mill rolls are of different sizes (different production machines), and are of limited stock quantities. I altered the example model that you provide on your site to be able to have multiple stock, and currently I'm trying to validate it and execute it on your neos server.
We are working on bi-dimensional modeling of earth's conductivity distribution. We are testing new modeling techniques based on quadratic and also linear programming. We are a research center at Ensenada, Mexico, and our work is on applied research and education. Currently we use Knitro with the AMPL language.
interpolator. An interpolator is a device which is used to estimate the values between two adjacent samples. My domain is digital receivers where the receiver clock is not changed to match the transmitter clock so one needs an interpolator to find the necessary in-between samples. The interpolator will be used in a fractional delay filter. Normally delay filters work with integer delays; for my purposes I need to be able to delay by 0 < x < 1. This is for R&D purposes [relating to LAN/WAN systems].
problems involving large number of complex constraints, and my experience with NEOS is a very pleasant one. In particular I have used MINLP and SNOPT. The constant monitoring of the optimization problems being carried out is noteworthy and I especially thank the staff for killing my jobs from time to time and for making Kill Job facility available to users.
a system for protein structure prediction. As part of my system, I had need to incorporate a nonlinear programming solver to handle packing of sidechain atoms in the protein. I modified my code to output packing subproblems directly in AMPL, which I could then submit to NEOS. In this fashion, I was able to evaluate solutions provided by different nonlinear solvers (I tried DONLP2, LOQO, FILTER, KNITRO, and LANCELOT), in effect testing the code before deciding
which to acquire and attempt to link into my system. NEOS is a great resource for people like me. Keep up the good work!
AMPL to perform numerical optimization of a spinor Bose-Einstein condensate. This yields the ground state energy of the atomic system and the corresponding
wave functions as well as a variety of other properties of superfluids. The aim is to develop a fundamental theory which can explain the recent observations of
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Hibbitt, Karlsson & Sorensen, Inc.
We have tried various solvers on NEOS to see if this is possible at all and the yagi antenna has been used as a test case for which good validation is possible. The NEOS server offers a lot flexibility and various (well-known) solvers. It gives us the opportunity to find out which approach works best. Without this server it would probably be impossible to find out what strategy is most likely to succeed. Preliminary results are very encouraging for this approach.
decoupling bandwidths. The objective function in my optimization is assumed to be linear. The constraints are non-linear, exponential and sometimes conditional. The NEOS server helped me tremendously, since my background in optimization is weak. I was nevertheless able to further my research
using this service
Good luck with your efforts in making the NEOS server even better.
What these blobs are is sets of papers published in mathematics (specifically, papers reviewed in Mathematical Reviews 1980-1999). Each paper in MR is
assigned one or more classification codes from a subject classification system of mathematics. I used the incidence data showing how many papers published in various sub-disciplines were given secondary classifications in other sub-disciplines; these data gave a sort of metric on the subject areas, which indicates that say Group Theory is close to Ring Theory but far from Differential Equations. So conceptually I then have the various disciplines arrayed in some high-dimensional space, some pairs of them closer to each other than other pairs. In my mind I visualize the subjects hovering around like stars in the Milky Way galaxy.
Well, there are standard tools for projecting such a picture to a plane so as to show the distance relationships optimally -- that is, you can sort of figure out the "main plane" of the galaxy. The problem is that when you project the various blobs to the plane of the galaxy, some of them will obscure others which are directly above or below them. So here's where I used NEOS: I took the nominal positions of the different blobs and asked for the minimal amount of jiggle I would need to separate them on the plane. It's not a hard project, conceptually, and I imagine cartographers have packaged routines to deal with this. But certainly there were too many variables and constraints to allow an analytic solution (and way too much computation to appeal to a theoretician like me). It took me a little while to master the NEOS syntax (e.g. I had to remember to tell it to display the answer...) but once I got it down to a science I was able to have the server supply the results I wanted in a painless way. There's quite a bit of arbitrariness in the way the data are massaged to produce a picture: one shouldn't try to read too much into these images. On the other hand, when left to their own devices, the programs tended to draw pictures which corroborated what I would have drawn on my own (when I had some prior knowledge of a sub-discipline) and gave hints about what real connections existed in sub-disciplines with which I was not familiar. So I'm quite happy with the results so far.