Aptech Systems, Inc. Worldwide Headquarters
Aptech Systems, Inc.
2350 East Germann Road, Suite #21
Chandler, AZ 85286
Ready to Get Started?
Request Quote & Product Information
Training & Events
Step-by-step, informative lessons for those who want to dive into GAUSS and achieve their goals, fast.
Have a Specific Question?
Q&A: Register and Login
Premier Support and Platinum Premier Support are annually renewable membership programs that provide you with important benefits including technical support, product maintenance, and substantial cost-saving features for your GAUSS System or the GAUSS Engine.
Join our community to see why our users are considered some of the most active and helpful in the industry!
Where to Buy
Recent Tagsapplications character vectors CML CMLMT Constrained Optimization datasets dates dlibrary dllcall error error handling errors Excel FANPACMT file i/o floating network GAUSS Engine graphics GUI hotkeys installation Java API license licensing linux loading data matrices matrix matrix manipulation Maxlik MaxLikMT Memory optimization Optmum output PQG graphics procs RAM random numbers string functions strings structures threading Time Series writing data
Time Series 2.0 MT
Find out more now
Time Series MT 2.1
Error G0037 : Result too large in GAUSS Light
What is this?
D:\gausslt13\running(15) : error G0037 : Result too large
in a GAUSS Light
GAUSS Light has a matrix size limitation. The largest matrix you can create in GAUSS Light is 10,000 elements. For example 100x100 or 1000x10 would be allowed, but 100x101 or 1001x10 would not.
What can I do if my data is larger than the GAUSS Light matrix limit size? What is the best alternative?
Probably the best answer would be to purchase the student version of GAUSS. It is available for students at a very low price. The student version is a full featured version of GAUSS that has no data size limitation and the debugger.
The other option is to try and split up your computations into smaller chunks. For example, if you have a 1000x11 matrix that represents 1000 observations of 11 variables, you could split this data into 11 different 1000x1 vectors and process each of them independently. If your data is not much above the 10,000 limit, this may work just fine. However, if your data is very large, you will probably be much happier with the full student version.