We used NEOS to solve nonlinear optimization problems associated with models of physical properties in chemistry. Our models were functions involving variables (call topological indices). A typical function has a constant term plus a sum of terms each of which has a coefficient times a variable raised to a power. FILTER adjusted the constant term, the coefficients, and the powers to minimize our objective function. Our objective function was the sum of squares of the differences between the experimental value of a physical property and the value predicted by our model function.
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.
I am using neos server to solve a polyfilm mill rolls slitting problem. I'm working in the IT dept of Dubai PolyFilm (DPF) manufacturing plant, in Dubai. I'm requested to find a solution that minimizes the slitting
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.
I am using your xpress solver to solve integer linear programming models built during my research work. At the end of the research my work will be submitted for publication.
I am a Petroleum Engineer and now I am working at my MSc. degree Thesis at North Fluminense University, in Macaé - Rio de Janeiro, Brazil. My MSc. Thesis is about Optimization of the gas balance planning in a production system that all the platforms are interlinked in a complex pipeline network. First I want to thanks a lot for your contribution in my academic study clearing questions about optimization solvers of NEOS. I would like to send my congratulations for your excellent work in the NEOS Server.
Your NEOS server has been of great help for us,
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.
The NEOS servers is useful for me to benchmark different optimization software.
I am using NEOS to compare the efficiency of my code with some codes, such as LOQO, LANCELOT, SNOPT, MINOS, .., when they solve nonlinearly constrained nonlinear network problems. It is research work for me.
I have extensively used your solver PCx to answer the question: can a linear combination of knowledge-based potentials be used as a fitness function in a genetic algorithm to predict the native folding of a globular protein? The NEOS server is very useful because it allows to save the time necessary in programming optimisation methods.
I have used NEOS with LOQO solver to optimize an
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].
I've been using NEOS lately to compare performance of various NLP algorithms that utilize second order information. We're a business of course, but this usage is a mixture of commercial and research work. I've been testing Knitro and filter and will use other NLP codes as time and bandwidth permit. Thanks for providing this useful service.
I have been using NEOS for circuit optimization
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.
Our company is working with various projects concerning R&D of internal combustion engines for cars and brakes for heavy vehicles.
For the past several years I have been working on
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!
I wish to extend my regards to your extraordinary work. I have just gone through the text associated with the site and I found it very interesting and so much related to my work. I should have told you that I'm an electrical power engineer involved in the optimization of medium and high voltage power systems. I build mathematical models with either technical, economical, or hybrid objectives and use numerical optimization algorithms to find the optimal design parameters of power systems.
NEOS is really cool! I am a die-hard GAMS user and would like to see GAMS interfaces to NLP and MINLP solvers, as my interest is primarily in solving nonlinear chemical engineering problems (production planning, scheduling, etc.) Keep up the good work!
Just discovered your great site. I have a problem arising in biophysics in which I need to find the center of a polytope generated by an LP problem (or equivalently all the vertices).
I am using the LOQO solver and code written in
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
I have used NEOS as an excellent guide to optimisation methods and locating associated software. Its the best resource I know of on the web and has been very useful in the development of optimisation methods in our process modeling software. Therefore, I welcome any developments to improve the site.
-- AspenTech Ltd
We believe the NEOS optimization server provides a very valuable service.
--ABAQUS/Design Development Manager
Hibbitt, Karlsson & Sorensen, Inc.
We are using the NEOS Server for solving linear and nonlinear complementarity problems in engineering mechanics and in robotics (till now, mainly unilateral contact problems and friction).
The problem we are considering is the design of a yagi antenna. These antennas consist of an array of wires and are frequently used as TV antennas. In the 1960s a lot of research has been done on the best design of this type of antenna. However, all research was experimental and therefore very costly and time consuming. Our idea is trying to design antennas by using the computer as a tuning device, i.e. starting from an initial design to have the computer finding the optimal solution by using constrained optimization methods. If this approach works, it could be used for the design of the future Square Kilometer Phased Array (SKA) for radio astronomy to look back in time to the first moments of the universe, which is to be build in about 10 years from now.
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.
I would like to express my support for this service. I am using this server to solve a non-linear optimization problem to determine the resource requirements for broadband networks using effective and
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
I just used AMPL-MINOS to solve an LP arising from a stochastic programming application that Jeff and I are looking at. The debug-resubmit process could not have been simpler nor more convenient. Fine stuff.
Here at our group at the Philips Research Laboratories we have used the NEOS Server in our search for nonlinear optimization methods for circuit simulation purposes.
I just found NEOS while looking around for a solution to my problem (forgive the pun) attempting to optimize a couple of functions for a computer game I am designing. It's very helpful, thank you
I am using the NEOS Server to solve optimization problems that arise in (1) distance geometry and multidimensional scaling; and (2) design of experiments.
We have used the NEOS server to solve NLP problems we formulated from Integrated Circuits Computer Aided Design. And we plan to use it again in the near future.
I have been using your AMPL front end and solvers to develop an assignment problem. BTW, this is a great service; allows me to develop without incurring client costs or requesting software my university cannot afford.
I used your codes (Minpack) in the past. Any deployment of nonlinear methods on a server are beneficial and their importance will increase.
Thank you for your help. NEOS is very useful to me : I am solving huge multi-commodity flow problems written in AMPL, and we do not have AMPL available here. I have been using NEOS for solving multi-commodity flow problems arising in the optimization of weights for routing in the Internet. Congratulations for this nice tool and your continuous efforts to improve it.
I did send problems to the PCx and the BPMPD solver and was very pleased with the outcome. These were rather small problems in the field of chip design. Larger problems consumed too much computer memory, let alone the time a solution might have taken. Nevertheless, information is well categorized, easily available and very supportive, as well as the people involved are. I had misunderstood the right-hand-side labels of the MPS-format. Not realizing that they supported different versions, I thought each RHS had a different label. I reported the resulting output as not good and by the time I noticed my mistake, one of you pointed it out to me as well.
Good luck with your efforts in making the NEOS server even better.
I have used some NLP solver from NEOS to solve a circuit optimization problem. It's very helpful to use it this way, other than installing those packages by myself.
By way of introduction let me mention that as a sideline to my professorial duties, I maintain a web site which seeks to direct people to appropriate parts of mathematics; my intended audience is students and professionals who have at least a couple of years exposure to college level mathematics. Turn to http://www.math-atlas.org/welcome.html and you'll see a set of blobs on the screen; there's also one on each index page e.g. http://www.math-atlas.org/index/15-XX.html . The positions of these blobs were determined by the NEOS server. I was happy to have access to the server because I think having the blobs positioned appropriately helps understand the topic, and therefore adds to the quality of the web pages.
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.