MATLAB: Does MATLAB 7.0 (R14) crash when I attempt to edit a 3-D plot with many data points

crashMATLABmemoryplotplottoolssegfaultsegvtools

When I try to use PLOTTOOLS with a figure containing subplots and containing a large number of data points, I receive the following segmentation violation::
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Segmentation violation detected at Tue Nov 09 17:27:56 2004
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Configuration:
MATLAB Version: 7.0.1.24704 (R14) Service Pack 1
Operating System: Microsoft Windows XP
Window System: Version 5.1 (Build 2600: Service Pack 1)
Processor ID: x86 Family 15 Model 2 Stepping 9, GenuineIntel
Virtual Machine: Java 1.4.2_04 with Sun Microsystems Inc. Java HotSpot(TM) Client VM
(mixed mode)
Default Charset: ibm-5348_P100-1997
Register State:
EAX = 00000018 EBX = 00000000
ECX = 00ccbb82 EDX = 7928f7a0
ESI = 7928f7a0 EDI = 00000018
EBP = 00cdce88 ESP = 00cdce7c
EIP = 7918780f FLG = 00010206
Stack Trace:
[0] hg.dll:??$_Uninit_fill_n@PAUPAAvgList@@KU1@V?$MatlabAllocator@UPAAvgList@@@@@std@@YAXPAUPAAvgList@@KABU1@AAV?$MatlabAllocator@UPAAvgList@@@@U_Nonscalar_ptr_iterator_tag@0@@Z(0, 0x00ccbb83 "w+£õw", 0x7928f7a0, 0x00cdcf00) + 31 bytes
[1] hg.dll:public: void __thiscall std::vector<struct PAAvgList,class MatlabAllocator<struct PAAvgList> >::_Construct_n(unsigned long,struct PAAvgList const &)(0x00ccbb83 "w+£õw", 0x7928f7a0, 0, 0x0f7027b0) + 77 bytes
[2] hg.dll:int __cdecl DrawAxisKidsSorted(struct GObject_tag *,struct RenderBuffer *,class std::vector<struct ObjList,class MatlabAllocator<struct ObjList> > &,void *,void *,int)(0, 0x70040e07, 0x2b041e25, 0x00ccbb83 "w+£õw") + 84 bytes
[3] hg.dll:_RenderAxis(0x0f7027b0, 0, 0, 0) + 825 bytes
[4] hg.dll:_GO_Render(0x0f7027b0, 0, 0, 0) + 173 bytes
[5] hg.dll:bool __cdecl RenderChildrenRecursive(struct GObject_tag *)(0x0f665590, 0x0f5d6790, 0x0f665590, 0) + 133 bytes
<snip>

Best Answer

This bug has been fixed in Release 14 Service Pack 3 (R14SP3). For previous product releases, read below for any possible workarounds:
We have verified there is a bug in MATLAB 7.0 (R14) in the way PLOTTOOLS handles large amounts of data. There are currently no known workarounds.