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  • How to install Diablo 2 Lord of Destrucion on Ubuntu 12.04?

    - by user99666
    I tried to install Diablo 2 LOD from my iso image first with acetone iso and I coldn't the acetone iso mounted me the image but when I tried to install it said please install the labelled disc so I give up for a while ... Then I tried with Gmount and on some forums it was told me to create mount points and the install it ... said and done the problem is that it mounts me the image it starts to install but only the first 3 disc images the expansion no ,so I said whatever it's ok even without the expansion but when i tried to play it says me insert the labelled disc but the iso image was still mounted with Gmount .Now somebody can help me please to resolve this problems ?

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  • How can I determine which GPU card is running at PCI Express 2.0 x16 & which is using x8?

    - by M. Tibbits
    Is there a way to determine the speed of the PCI Express connection to a specific card? I have three cards plugged in: two Nvidia GTX 480's (one at x16 & and one at x8) one Nvidia GTX 460 running at x8 Is there some way, either by a function call in C or an option to lspci that I can determine the bus speed of the graphics cards? When I only use one of the cards for my CUDA program, I'd like to use the one which is running at x16. Thanks! Note: lspci -vvv dumps out For the two GTX 480s. I don't see any differences that pertain to bus speed. 03:00.0 VGA compatible controller: nVidia Corporation Device 06c0 (rev a3) Subsystem: eVga.com. Corp. Device 1480 Control: I/O- Mem+ BusMaster+ SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR- FastB2B- DisINTx- Status: Cap+ 66MHz- UDF- FastB2B- ParErr- DEVSEL=fast >TAbort- <TAbort- <MAbort- >SERR- <PERR- INTx- Latency: 0 Interrupt: pin A routed to IRQ 16 Region 0: Memory at d4000000 (32-bit, non-prefetchable) [size=32M] Region 1: Memory at b0000000 (64-bit, prefetchable) [size=128M] Region 3: Memory at bc000000 (64-bit, prefetchable) [size=64M] Region 5: I/O ports at df00 [disabled] [size=128] [virtual] Expansion ROM at b8000000 [disabled] [size=512K] Capabilities: <access denied> Kernel driver in use: nvidia Kernel modules: nvidia, nvidiafb, nouveau 03:00.1 Audio device: nVidia Corporation Device 0be5 (rev a1) Subsystem: eVga.com. Corp. Device 1480 Control: I/O- Mem- BusMaster- SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR- FastB2B- DisINTx- Status: Cap+ 66MHz- UDF- FastB2B- ParErr- DEVSEL=fast >TAbort- <TAbort- <MAbort- >SERR- <PERR- INTx- Interrupt: pin B routed to IRQ 5 Region 0: [virtual] Memory at d7ffc000 (32-bit, non-prefetchable) [disabled] [size=16K] Capabilities: <access denied> 04:00.0 VGA compatible controller: nVidia Corporation Device 06c0 (rev a3) Subsystem: eVga.com. Corp. Device 1480 Control: I/O+ Mem+ BusMaster+ SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR- FastB2B- DisINTx- Status: Cap+ 66MHz- UDF- FastB2B- ParErr- DEVSEL=fast >TAbort- <TAbort- <MAbort- >SERR- <PERR- INTx- Latency: 0 Interrupt: pin A routed to IRQ 16 Region 0: Memory at dc000000 (32-bit, non-prefetchable) [size=32M] Region 1: Memory at c0000000 (64-bit, prefetchable) [size=128M] Region 3: Memory at cc000000 (64-bit, prefetchable) [size=64M] Region 5: I/O ports at cf00 [size=128] [virtual] Expansion ROM at c8000000 [disabled] [size=512K] Capabilities: <access denied> Kernel driver in use: nvidia Kernel modules: nvidia, nvidiafb, nouveau 04:00.1 Audio device: nVidia Corporation Device 0be5 (rev a1) Subsystem: eVga.com. Corp. Device 1480 Control: I/O- Mem+ BusMaster+ SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR- FastB2B- DisINTx- Status: Cap+ 66MHz- UDF- FastB2B- ParErr- DEVSEL=fast >TAbort- <TAbort- <MAbort- >SERR- <PERR- INTx- Latency: 0, Cache Line Size: 64 bytes Interrupt: pin B routed to IRQ 5 Region 0: Memory at dfffc000 (32-bit, non-prefetchable) [size=16K] Capabilities: <access denied> And the only differences I see relate specifically to the memory mapping: myComputer:~> diff card1 card2 3c3 < Control: I/O- Mem+ BusMaster+ SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR- FastB2B- DisINTx- --- > Control: I/O+ Mem+ BusMaster+ SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR- FastB2B- DisINTx- 7,11c7,11 < Region 0: Memory at d4000000 (32-bit, non-prefetchable) [size=32M] < Region 1: Memory at b0000000 (64-bit, prefetchable) [size=128M] < Region 3: Memory at bc000000 (64-bit, prefetchable) [size=64M] < Region 5: I/O ports at df00 [disabled] [size=128] < [virtual] Expansion ROM at b8000000 [disabled] [size=512K] --- > Region 0: Memory at dc000000 (32-bit, non-prefetchable) [size=32M] > Region 1: Memory at c0000000 (64-bit, prefetchable) [size=128M] > Region 3: Memory at cc000000 (64-bit, prefetchable) [size=64M] > Region 5: I/O ports at cf00 [size=128] > [virtual] Expansion ROM at c8000000 [disabled] [size=512K] 18c18 < Control: I/O- Mem- BusMaster- SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR- FastB2B- DisINTx- --- > Control: I/O- Mem+ BusMaster+ SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR- FastB2B- DisINTx- 19a20 > Latency: 0, Cache Line Size: 64 bytes 21c22 < Region 0: [virtual] Memory at d7ffc000 (32-bit, non-prefetchable) [disabled] [size=16K] --- > Region 0: Memory at dfffc000 (32-bit, non-prefetchable) [size=16K]

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  • Recover data from Dynamic Disk (MBR) bigger than 2TB

    - by Helder
    Here is the situation: Promise Array FastTrak TX4310 with 3 disks (750 GB each) in RAID5. This comes to around 1500 GB of data. Last week I had the idea of expanding the RAID with an additional 750 GB disk. This would bring the volume to around 2250 GB. I plugged the disk and used the Webpam software to do the RAID expansion. However, I didn't count with the MBR 2TB limit, as I didn't remembered that the disk was using MBR instead of GPT and I didn't check it prior to the expansion. After a couple of days of expansion, today when I got home, the disk in Windows disk manager showed the message "Invalid disk" and when I try to activate it, it says "The operation is not allowed on the Invalid pack". From what I figured, the logical volume on the RAID expanded, and passed that info to the Windows layer and I ended up with an "larger than 2TB" MBR disk. I'm hopping that somehow I can still recover some data from this, and I was wondering if I can "rewrite" the MBR structure back to the 1500 GB partition size, so I can access the partition in Windows. Right now I'm doing an "Analyse" with TestDisk, as I hope the program will pickup the old 1500 structure and allow me to somehow revert back to it. I think that even though the Logical Drive in the RAID is bigger than the 2TB, I can somehow correct the MBR to show the 1500 GB partition again. I had a similar problem once, and I was able to recover the data using a similar method. What do you guys think? Is it a dead end? Am I totally screwed because there is the extra RAID layer that I'm not counting? Or is there other way to move with this? Thanks all!

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  • shell function and environment

    - by Michael
    How in Makefile export variable first then make another variable's expansion? somevar := apple export somevar update = $(shell perl -e 'print "$$ENV{somevar}"') all: @echo $(update) For some reason the expansion takes place first, then export. As a result the output is empty.

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  • patterns in case statement in bash scripting

    - by Ramiro Rela
    The man says that case statements use "filename expansion pattern matching". I usually want to have short names for some parameters, so I go: case $1 in req|reqs|requirements) TASK="Functional Requirements";; met|meet|meetings) TASK="Meetings with the client";; esac logTimeSpentIn "$TASK" I tried patterns like "req*" or "me{e,}t" which I understand would expand correctly to match those values in the context of filename expansion, but it doesn't work. Thanks.

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  • Wildcards not being substituted

    - by user21463
    #!/bin/bash loc=`echo ~/.gvfs/*/DCIM/100_FUJI` rm -f /mnt/fujifilmA100 ln -s "$loc" /mnt/fujifilmA100 For some reason the variable * doesn't get substituted with the only possible value and gets given the value /home/chris/.gvfs/*/DCIM/100_FUJI. Does anyone have an idea of why? Please note: If global expansion fails, the pattern is not substituted. I ran the commands: chris@comp2008:~$ loc=`echo ~/.gvfs/*/DCIM/100_FUJI ` chris@comp2008:~$ echo $loc /home/chris/.gvfs/gphoto2 mount on usb%3A001,008/DCIM/100_FUJI So we can see the expansion should work I have now switched to using: loc = `find ~/.gvfs -name 100_FUJI ` I am just curious why it doesn't work as is. Debugging output using sh -x echo /home/chris/.gvfs/*/DCIM/100_FUJI loc=/home/chris/.gvfs/*/DCIM/100_FUJI rm -f /mnt/fujifilmA100 ln -s /home/chris/.gvfs/*/DCIM/100_FUJI/mnt/fujifilmA100

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  • Caching of path environment variable on windows?

    - by jwir3
    I'm assisting one of our testers in troubleshooting a configuration problem on a Windows XP SP3 system. Our application uses an environment variable, called APP_HOME, to refer to the directory where our application is installed. When the application is installed, we utilize the following environment variables: APP_HOME = C:\application\ PATH = %PATH%;%APP_HOME%bin Now, the problem comes in that she's working with multiple versions of the same application. So, in order to switch between version 7.0 and 8.1, for example, she might use: APP_HOME = C:\application_7.0\ (for 7.0) and then change it to: APP_HOME = C:\application_8.1\ (for 8.1) The problem is that once this change is made, the PATH environment variable apparently still is looking at the old expansion of the APP_HOME variable. So, for example, after she has changed APP_HOME, PATH still refers to the 7.0 bin directory. Any thoughts on why this might be happening? It looks to me like the PATH variable is caching the expansion of the APP_HOME environment variable. Is there any way to turn this behavior off?

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  • Server 2012 Storage Pools, Raid Controller... can the Storage Pool deal with it?

    - by TomTom
    Before trying it out - I don't find any documentation. Given that Storage Pools have serious performance problems with parity, and do not rebalance data at the moment when you add discs, my preferred way to use them would be as think provisioned space, ISCSI targets - with every "Pool" running against 1 RAID that comes from a Raid controller (who also introduces SSD read and write caching - another thing missing from Storage Pools). The main question is - how does a Storage Pool handle the change in the underlying disc that can happen? I mostly talk about OCE (Online Capacity Expansion), where a disc after an expansion suddenly reports a larger space. Standard Windows allows you to use this additional space (and expand the partitions). How does a storage pool handle it?

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  • In BASH, are wildcard expansions guaranteed to be in order?

    - by ArtB
    Is the expansion of a wildcard in BASH guaranteed to be in alphabetical order? I forced to split a large file into [10Mb pieces][1] so that they can be be accepted by my Mercurial repository. So I was thinking I could use: split -b 10485760 Big.file BigFilePiece. and then in place of: cat BigFile | bigFileProcessor I could do: cat BigFilePiece.* | bigFileProcessor In its place. However, I could not find anywhere that guaranteed that the expansion of the asterisk (aka wildcard, aka '*' ) would always be in alphabetical order so that .aa came before .ab ( as opposed to be timestamp ordering or something like that ). Also, are there any flaws in my plan? How great is the performance cost of cating the file together?

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  • HP MSA 1000 SAN: Can I use 1 array/shelf?

    - by CC
    Hi all, I'm planning some expansion on an HP MSA1000 SAN. My boss says that we need to have two separate arrays on the new enclosure, one for Bays 1-7, the other for Bays 8-14. Is there any reason that we need to do this? My plan was to have the entire expansion shelf be 1 array, then create RAID 6 logical drives from that. I don't understand what splitting drives into separate arrays gain us. We don't have dual controllers, so there's no benefit there. Thanks, CC

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  • How does one view the "inline docs" of a .cpp file?

    - by Mala
    I have cpp files peppered with comments such as the following before every function: /** * @brief Set the normal and expansion handshake timeouts. * * @param wm Array of wiimote_t structures. * @param wiimotes Number of objects in the wm array. * @param normal_timeout The timeout in milliseconds for a normal read. * @param exp_timeout The timeout in millisecondsd to wait for an expansion handshake. */ I assume from the format that there has to be some way of exporting this into a "friendly" format, perhaps html, which can then be read in a manner similar to the Java API. How would I do this? (I'm on Windows 7, running MS Visual Studio 2010)

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  • How to rebuild fstab automatically

    - by yvoyer
    I accidentally removed all the entries from the fstab files while doing a backup (Yeah, I know ;)). I would like to know if there is a way to rebuild it with the current mount options, since I did not restart the server since the deletion. If there is no such program, could anybody help me rebuild it. Using this, I have found the command to show the current setup, but I don't know what to do with it. $ sudo blkid /dev/sda1: UUID="3fc55e0f-a9b3-4229-9e76-ca95b4825a40" TYPE="ext4" /dev/sda5: UUID="718e611d-b8a3-4f02-a0cc-b3025d8db54d" TYPE="swap" /dev/sdb1: LABEL="Files_Server_Int" UUID="02fc2eda-d9fb-47fb-9e60-5fe3073e5b55" TYPE="ext4" /dev/sdc1: UUID="41e60bc2-2c9c-4104-9649-6b513919df4a" TYPE="ext4" /dev/sdd1: LABEL="Expansion Drive" UUID="782042B920427E5E" TYPE="ntfs" $ cat /etc/mtab /dev/sda1 / ext4 rw,errors=remount-ro 0 0 proc /proc proc rw,noexec,nosuid,nodev 0 0 none /sys sysfs rw,noexec,nosuid,nodev 0 0 none /sys/fs/fuse/connections fusectl rw 0 0 none /sys/kernel/debug debugfs rw 0 0 none /sys/kernel/security securityfs rw 0 0 none /dev devtmpfs rw,mode=0755 0 0 none /dev/pts devpts rw,noexec,nosuid,gid=5,mode=0620 0 0 none /dev/shm tmpfs rw,nosuid,nodev 0 0 none /var/run tmpfs rw,nosuid,mode=0755 0 0 none /var/lock tmpfs rw,noexec,nosuid,nodev 0 0 none /lib/init/rw tmpfs rw,nosuid,mode=0755 0 0 none /var/lib/ureadahead/debugfs debugfs rw,relatime 0 0 /dev/sdc1 /home ext4 rw 0 0 /dev/sdb1 /media/Files_Server ext4 rw 0 0 binfmt_misc /proc/sys/fs/binfmt_misc binfmt_misc rw,noexec,nosuid,nodev 0 0 /dev/sdd1 /media/Expansion\040Drive fuseblk rw,nosuid,nodev,allow_other,blksize=4096,default_permissions 0 0 gvfs-fuse-daemon /home/yvoyer/.gvfs fuse.gvfs-fuse-daemon rw,nosuid,nodev,user=yvoyer 0 0 /dev/sdd1 /media/Backup500 fuseblk rw,nosuid,nodev,sync,allow_other,blksize=4096,default_permissions 0 0 /dev/sr0 /media/DIR-615 iso9660 ro,nosuid,nodev,uhelper=udisks,uid=1000,gid=1000,iocharset=utf8,mode=0400,dmode=0500 0 0 gvfs-fuse-daemon /home/cdrapeau/.gvfs fuse.gvfs-fuse-daemon rw,nosuid,nodev,user=cdrapeau 0 0

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  • Saudi Arabian Retail Distribution Business Ajlan & Bros Selects Oracle Commerce

    - by Marie-Christin Hansen
    Ajlan & Bros has selected Oracle Commerce in a bid to improve its customer engagement capabilities and drive its expansion plans. The large Middle Eastern retail distribution business, which specializes in the design, manufacture and supply of clothing across the Middle East, is seeking to expand its operations, which consist of a distribution network of more than 7,000 points of sale and represent more than 15 international brands. The business is aiming to build brand awareness globally with an interest in the European and American markets. Choosing Oracle Commerce will provide Ajlan & Bros with the capability to optimize each customer engagement, which will help to increase cross-channel promotion and improve a unified online, mobile and social experience for customers. The company will be able to leverage Oracle Commerce’s advanced marketing and personalization capabilities, with enhanced integrated search and content management functionality across its channels. The selection of Oracle Commerce followed an extensive evaluation of competitor solutions, with Oracle selected due to the solutions strong capabilities in cross-channel ecommerce and customer experience management, as well as a solid track record of maintaining best practice. Press release: Ajlan & Bros Selects Oracle Commerce to Support Expansion Strategy

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  • The Benefits of Smart Grid Business Software

    - by Sylvie MacKenzie, PMP
    Smart Grid Background What Are Smart Grids?Smart Grids use computer hardware and software, sensors, controls, and telecommunications equipment and services to: Link customers to information that helps them manage consumption and use electricity wisely. Enable customers to respond to utility notices in ways that help minimize the duration of overloads, bottlenecks, and outages. Provide utilities with information that helps them improve performance and control costs. What Is Driving Smart Grid Development? Environmental ImpactSmart Grid development is picking up speed because of the widespread interest in reducing the negative impact that energy use has on the environment. Smart Grids use technology to drive efficiencies in transmission, distribution, and consumption. As a result, utilities can serve customers’ power needs with fewer generating plants, fewer transmission and distribution assets,and lower overall generation. With the possible exception of wind farm sprawl, landscape preservation is one obvious benefit. And because most generation today results in greenhouse gas emissions, Smart Grids reduce air pollution and the potential for global climate change.Smart Grids also more easily accommodate the technical difficulties of integrating intermittent renewable resources like wind and solar into the grid, providing further greenhouse gas reductions. CostsThe ability to defer the cost of plant and grid expansion is a major benefit to both utilities and customers. Utilities do not need to use as many internal resources for traditional infrastructure project planning and management. Large T&D infrastructure expansion costs are not passed on to customers.Smart Grids will not eliminate capital expansion, of course. Transmission corridors to connect renewable generation with customers will require major near-term expenditures. Additionally, in the future, electricity to satisfy the needs of population growth and additional applications will exceed the capacity reductions available through the Smart Grid. At that point, expansion will resume—but with greater overall T&D efficiency based on demand response, load control, and many other Smart Grid technologies and business processes. Energy efficiency is a second area of Smart Grid cost saving of particular relevance to customers. The timely and detailed information Smart Grids provide encourages customers to limit waste, adopt energy-efficient building codes and standards, and invest in energy efficient appliances. Efficiency may or may not lower customer bills because customer efficiency savings may be offset by higher costs in generation fuels or carbon taxes. It is clear, however, that bills will be lower with efficiency than without it. Utility Operations Smart Grids can serve as the central focus of utility initiatives to improve business processes. Many utilities have long “wish lists” of projects and applications they would like to fund in order to improve customer service or ease staff’s burden of repetitious work, but they have difficulty cost-justifying the changes, especially in the short term. Adding Smart Grid benefits to the cost/benefit analysis frequently tips the scales in favor of the change and can also significantly reduce payback periods.Mobile workforce applications and asset management applications work together to deploy assets and then to maintain, repair, and replace them. Many additional benefits result—for instance, increased productivity and fuel savings from better routing. Similarly, customer portals that provide customers with near-real-time information can also encourage online payments, thus lowering billing costs. Utilities can and should include these cost and service improvements in the list of Smart Grid benefits. What Is Smart Grid Business Software? Smart Grid business software gathers data from a Smart Grid and uses it improve a utility’s business processes. Smart Grid business software also helps utilities provide relevant information to customers who can then use it to reduce their own consumption and improve their environmental profiles. Smart Grid Business Software Minimizes the Impact of Peak Demand Utilities must size their assets to accommodate their highest peak demand. The higher the peak rises above base demand: The more assets a utility must build that are used only for brief periods—an inefficient use of capital. The higher the utility’s risk profile rises given the uncertainties surrounding the time needed for permitting, building, and recouping costs. The higher the costs for utilities to purchase supply, because generators can charge more for contracts and spot supply during high-demand periods. Smart Grids enable a variety of programs that reduce peak demand, including: Time-of-use pricing and critical peak pricing—programs that charge customers more when they consume electricity during peak periods. Pilot projects indicate that these programs are successful in flattening peaks, thus ensuring better use of existing T&D and generation assets. Direct load control, which lets utilities reduce or eliminate electricity flow to customer equipment (such as air conditioners). Contracts govern the terms and conditions of these turn-offs. Indirect load control, which signals customers to reduce the use of on-premises equipment for contractually agreed-on time periods. Smart Grid business software enables utilities to impose penalties on customers who do not comply with their contracts. Smart Grids also help utilities manage peaks with existing assets by enabling: Real-time asset monitoring and control. In this application, advanced sensors safely enable dynamic capacity load limits, ensuring that all grid assets can be used to their maximum capacity during peak demand periods. Real-time asset monitoring and control applications also detect the location of excessive losses and pinpoint need for mitigation and asset replacements. As a result, utilities reduce outage risk and guard against excess capacity or “over-build”. Better peak demand analysis. As a result: Distribution planners can better size equipment (e.g. transformers) to avoid over-building. Operations engineers can identify and resolve bottlenecks and other inefficiencies that may cause or exacerbate peaks. As above, the result is a reduction in the tendency to over-build. Supply managers can more closely match procurement with delivery. As a result, they can fine-tune supply portfolios, reducing the tendency to over-contract for peak supply and reducing the need to resort to spot market purchases during high peaks. Smart Grids can help lower the cost of remaining peaks by: Standardizing interconnections for new distributed resources (such as electricity storage devices). Placing the interconnections where needed to support anticipated grid congestion. Smart Grid Business Software Lowers the Cost of Field Services By processing Smart Grid data through their business software, utilities can reduce such field costs as: Vegetation management. Smart Grids can pinpoint momentary interruptions and tree-caused outages. Spatial mash-up tools leverage GIS models of tree growth for targeted vegetation management. This reduces the cost of unnecessary tree trimming. Service vehicle fuel. Many utility service calls are “false alarms.” Checking meter status before dispatching crews prevents many unnecessary “truck rolls.” Similarly, crews use far less fuel when Smart Grid sensors can pinpoint a problem and mobile workforce applications can then route them directly to it. Smart Grid Business Software Ensures Regulatory Compliance Smart Grids can ensure compliance with private contracts and with regional, national, or international requirements by: Monitoring fulfillment of contract terms. Utilities can use one-hour interval meters to ensure that interruptible (“non-core”) customers actually reduce or eliminate deliveries as required. They can use the information to levy fines against contract violators. Monitoring regulations imposed on customers, such as maximum use during specific time periods. Using accurate time-stamped event history derived from intelligent devices distributed throughout the smart grid to monitor and report reliability statistics and risk compliance. Automating business processes and activities that ensure compliance with security and reliability measures (e.g. NERC-CIP 2-9). Grid Business Software Strengthens Utilities’ Connection to Customers While Reducing Customer Service Costs During outages, Smart Grid business software can: Identify outages more quickly. Software uses sensors to pinpoint outages and nested outage locations. They also permit utilities to ensure outage resolution at every meter location. Size outages more accurately, permitting utilities to dispatch crews that have the skills needed, in appropriate numbers. Provide updates on outage location and expected duration. This information helps call centers inform customers about the timing of service restoration. Smart Grids also facilitates display of outage maps for customer and public-service use. Smart Grids can significantly reduce the cost to: Connect and disconnect customers. Meters capable of remote disconnect can virtually eliminate the costs of field crews and vehicles previously required to change service from the old to the new residents of a metered property or disconnect customers for nonpayment. Resolve reports of voltage fluctuation. Smart Grids gather and report voltage and power quality data from meters and grid sensors, enabling utilities to pinpoint reported problems or resolve them before customers complain. Detect and resolve non-technical losses (e.g. theft). Smart Grids can identify illegal attempts to reconnect meters or to use electricity in supposedly vacant premises. They can also detect theft by comparing flows through delivery assets with billed consumption. Smart Grids also facilitate outreach to customers. By monitoring and analyzing consumption over time, utilities can: Identify customers with unusually high usage and contact them before they receive a bill. They can also suggest conservation techniques that might help to limit consumption. This can head off “high bill” complaints to the contact center. Note that such “high usage” or “additional charges apply because you are out of range” notices—frequently via text messaging—are already common among mobile phone providers. Help customers identify appropriate bill payment alternatives (budget billing, prepayment, etc.). Help customers find and reduce causes of over-consumption. There’s no waiting for bills in the mail before they even understand there is a problem. Utilities benefit not just through improved customer relations but also through limiting the size of bills from customers who might struggle to pay them. Where permitted, Smart Grids can open the doors to such new utility service offerings as: Monitoring properties. Landlords reduce costs of vacant properties when utilities notify them of unexpected energy or water consumption. Utilities can perform similar services for owners of vacation properties or the adult children of aging parents. Monitoring equipment. Power-use patterns can reveal a need for equipment maintenance. Smart Grids permit utilities to alert owners or managers to a need for maintenance or replacement. Facilitating home and small-business networks. Smart Grids can provide a gateway to equipment networks that automate control or let owners access equipment remotely. They also facilitate net metering, offering some utilities a path toward involvement in small-scale solar or wind generation. Prepayment plans that do not need special meters. Smart Grid Business Software Helps Customers Control Energy Costs There is no end to the ways Smart Grids help both small and large customers control energy costs. For instance: Multi-premises customers appreciate having all meters read on the same day so that they can more easily compare consumption at various sites. Customers in competitive regions can match their consumption profile (detailed via Smart Grid data) with specific offerings from competitive suppliers. Customers seeing inexplicable consumption patterns and power quality problems may investigate further. The result can be discovery of electrical problems that can be resolved through rewiring or maintenance—before more serious fires or accidents happen. Smart Grid Business Software Facilitates Use of Renewables Generation from wind and solar resources is a popular alternative to fossil fuel generation, which emits greenhouse gases. Wind and solar generation may also increase energy security in regions that currently import fossil fuel for use in generation. Utilities face many technical issues as they attempt to integrate intermittent resource generation into traditional grids, which traditionally handle only fully dispatchable generation. Smart Grid business software helps solves many of these issues by: Detecting sudden drops in production from renewables-generated electricity (wind and solar) and automatically triggering electricity storage and smart appliance response to compensate as needed. Supporting industry-standard distributed generation interconnection processes to reduce interconnection costs and avoid adding renewable supplies to locations already subject to grid congestion. Facilitating modeling and monitoring of locally generated supply from renewables and thus helping to maximize their use. Increasing the efficiency of “net metering” (through which utilities can use electricity generated by customers) by: Providing data for analysis. Integrating the production and consumption aspects of customer accounts. During non-peak periods, such techniques enable utilities to increase the percent of renewable generation in their supply mix. During peak periods, Smart Grid business software controls circuit reconfiguration to maximize available capacity. Conclusion Utility missions are changing. Yesterday, they focused on delivery of reasonably priced energy and water. Tomorrow, their missions will expand to encompass sustainable use and environmental improvement.Smart Grids are key to helping utilities achieve this expanded mission. But they come at a relatively high price. Utilities will need to invest heavily in new hardware, software, business process development, and staff training. Customer investments in home area networks and smart appliances will be large. Learning to change the energy and water consumption habits of a lifetime could ultimately prove even more formidable tasks.Smart Grid business software can ease the cost and difficulties inherent in a needed transition to a more flexible, reliable, responsive electricity grid. Justifying its implementation, however, requires a full understanding of the benefits it brings—benefits that can ultimately help customers, utilities, communities, and the world address global issues like energy security and climate change while minimizing costs and maximizing customer convenience. This white paper is available for download here. For further information about Oracle's Primavera Solutions for Utilities, please read our Utilities e-book.

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  • Efficient 4x4 matrix inverse (affine transform)

    - by Budric
    Hi, I was hoping someone can point out an efficient formula for 4x4 affine matrix transform. Currently my code uses cofactor expansion and it allocates a temporary array for each cofactor. It's easy to read, but it's slower than it should be. Note, this isn't homework and I know how to work it out manually using 4x4 co-factor expansion, it's just a pain and not really an interesting problem for me. Also I've googled and came up with a few sites that give you the formula already (http://www.euclideanspace.com/maths/algebra/matrix/functions/inverse/fourD/index.htm). However this one could probably be optimized further by pre-computing some of the products. I'm sure someone came up with the "best" formula for this at one point or another? Thanks.

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  • Globbing with MinGW on Windows

    - by Neil Butterworth
    I have an application built with the MinGW C++ compiler that works something like grep - acommand looks something like this: myapp -e '.*' *.txt where the thing that comes after the -e switch is a regex, and the thing after that is file name pattern. It seems that MinGW automatically expands (globs in UNIX terms) the command line so my regex gets mangled. I can turn this behaviour off, I discovered, by setting the global variable _CRT_glob to zero. This will be fine for bash and other sensible shell users, as the shell will expand the file pattern. For MS cmd.exe users however, it looks like I will have to expand the file pattern myself. So my question - does anyone know of a globbing library (or facility in MinGW) to do partial command line expansion? I'm aware of the _setargv feature of the Windows CRT, but that expands the full command line. Please note I've seen this question, but it really does not address partial expansion.

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  • How to check when animation finishes if animation block is

    - by pumpk1n
    I have a controller which adds as subviews a custom UIView class called Circle. Let's call a particular instance of Circle, "circle". I have a method in Circle, animateExpand, which expands the circle by animating the view. In the following code (which lives in the controller) I want to alloc and init a circle, add it to a NSMutableArray circleArray, animate the expansion, and at the end of the expansion, i want to remove the object from the array. My attempt: Circle *circle = [[Circle alloc] init]; [circleArray addObject:circle]; [circle animateExpand]; [circleArray removeObjectIdenticalTo:circle]; [circle release]; The problem is [circleArray removeObjectIdenticalTo:circle]; gets called before the animation finishes. Presumbly because the animation is done on a seperate thread. I cant implement the deletion in completion:^(BOOL finished){ }, because the Circle class does not know about a circleArray. Any solutions would be helpful, thanks!

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  • ArithmeticException thrown during BigDecimal.divide

    - by polygenelubricants
    I thought java.math.BigDecimal is supposed to be The Answer™ to the need of performing infinite precision arithmetic with decimal numbers. Consider the following snippet: import java.math.BigDecimal; //... final BigDecimal one = BigDecimal.ONE; final BigDecimal three = BigDecimal.valueOf(3); final BigDecimal third = one.divide(three); assert third.multiply(three).equals(one); // this should pass, right? I expect the assert to pass, but in fact the execution doesn't even get there: one.divide(three) causes ArithmeticException to be thrown! Exception in thread "main" java.lang.ArithmeticException: Non-terminating decimal expansion; no exact representable decimal result. at java.math.BigDecimal.divide It turns out that this behavior is explicitly documented in the API: In the case of divide, the exact quotient could have an infinitely long decimal expansion; for example, 1 divided by 3. If the quotient has a non-terminating decimal expansion and the operation is specified to return an exact result, an ArithmeticException is thrown. Otherwise, the exact result of the division is returned, as done for other operations. Browsing around the API further, one finds that in fact there are various overloads of divide that performs inexact division, i.e.: final BigDecimal third = one.divide(three, 33, RoundingMode.DOWN); System.out.println(three.multiply(third)); // prints "0.999999999999999999999999999999999" Of course, the obvious question now is "What's the point???". I thought BigDecimal is the solution when we need exact arithmetic, e.g. for financial calculations. If we can't even divide exactly, then how useful can this be? Does it actually serve a general purpose, or is it only useful in a very niche application where you fortunately just don't need to divide at all? If this is not the right answer, what CAN we use for exact division in financial calculation? (I mean, I don't have a finance major, but they still use division, right???).

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  • FORMSOF Thesaurus in SQL Server

    - by Coolcoder
    Has anyone done any performance measures with this in terms of speed where there is a high number of substitutes for any given word. For instance, I want to use this to store common misspellings; expecting to have 4-10 variations of a word. <expansion> <sub>administration</sub> <sub>administraton</sub> <sub>aministraton</sub> </expansion> When you run a fulltext search, how does performance degrade with that number of variations? for instance, I assume it has to do a separate fulltext search performing an OR? Also, having say 20/30K entries in the Thesaurus xml file - does this impact performance?

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  • About the fix for the interference between Company mode and Yasnippet

    - by janoChen
    Emacs wiki says: Company does interfere with Yasnippet’s native behaviour. Here’s a quick fix: http://gist.github.com/265010 The code is the following: (define-key company-active-map "\t" 'company-yasnippet-or-completion) (defun company-yasnippet-or-completion () (interactive) (if (yas/expansion-at-point) (progn (company-abort) (yas/expand)) (company-complete-common))) (defun yas/expansion-at-point () "Tested with v0.6.1. Extracted from `yas/expand-1'" (first (yas/current-key))) I placed that code in my .emacs and the following message appeared: Warning (initialization): An error occurred while loading `c:/Documents and Settings/Alex.AUTOINSTALL.001/Application Data/.emacs.elc': Symbol's value as variable is void: company-active-map To ensure normal operation, you should investigate and remove the cause of the error in your initialization file. Start Emacs with the `--debug-init' option to view a complete error backtrace. Do I have to place the fix code inside a YASnippet's .el file? or in my .emacs (which throws me an error)?

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  • A Taxonomy of Numerical Methods v1

    - by JoshReuben
    Numerical Analysis – When, What, (but not how) Once you understand the Math & know C++, Numerical Methods are basically blocks of iterative & conditional math code. I found the real trick was seeing the forest for the trees – knowing which method to use for which situation. Its pretty easy to get lost in the details – so I’ve tried to organize these methods in a way that I can quickly look this up. I’ve included links to detailed explanations and to C++ code examples. I’ve tried to classify Numerical methods in the following broad categories: Solving Systems of Linear Equations Solving Non-Linear Equations Iteratively Interpolation Curve Fitting Optimization Numerical Differentiation & Integration Solving ODEs Boundary Problems Solving EigenValue problems Enjoy – I did ! Solving Systems of Linear Equations Overview Solve sets of algebraic equations with x unknowns The set is commonly in matrix form Gauss-Jordan Elimination http://en.wikipedia.org/wiki/Gauss%E2%80%93Jordan_elimination C++: http://www.codekeep.net/snippets/623f1923-e03c-4636-8c92-c9dc7aa0d3c0.aspx Produces solution of the equations & the coefficient matrix Efficient, stable 2 steps: · Forward Elimination – matrix decomposition: reduce set to triangular form (0s below the diagonal) or row echelon form. If degenerate, then there is no solution · Backward Elimination –write the original matrix as the product of ints inverse matrix & its reduced row-echelon matrix à reduce set to row canonical form & use back-substitution to find the solution to the set Elementary ops for matrix decomposition: · Row multiplication · Row switching · Add multiples of rows to other rows Use pivoting to ensure rows are ordered for achieving triangular form LU Decomposition http://en.wikipedia.org/wiki/LU_decomposition C++: http://ganeshtiwaridotcomdotnp.blogspot.co.il/2009/12/c-c-code-lu-decomposition-for-solving.html Represent the matrix as a product of lower & upper triangular matrices A modified version of GJ Elimination Advantage – can easily apply forward & backward elimination to solve triangular matrices Techniques: · Doolittle Method – sets the L matrix diagonal to unity · Crout Method - sets the U matrix diagonal to unity Note: both the L & U matrices share the same unity diagonal & can be stored compactly in the same matrix Gauss-Seidel Iteration http://en.wikipedia.org/wiki/Gauss%E2%80%93Seidel_method C++: http://www.nr.com/forum/showthread.php?t=722 Transform the linear set of equations into a single equation & then use numerical integration (as integration formulas have Sums, it is implemented iteratively). an optimization of Gauss-Jacobi: 1.5 times faster, requires 0.25 iterations to achieve the same tolerance Solving Non-Linear Equations Iteratively find roots of polynomials – there may be 0, 1 or n solutions for an n order polynomial use iterative techniques Iterative methods · used when there are no known analytical techniques · Requires set functions to be continuous & differentiable · Requires an initial seed value – choice is critical to convergence à conduct multiple runs with different starting points & then select best result · Systematic - iterate until diminishing returns, tolerance or max iteration conditions are met · bracketing techniques will always yield convergent solutions, non-bracketing methods may fail to converge Incremental method if a nonlinear function has opposite signs at 2 ends of a small interval x1 & x2, then there is likely to be a solution in their interval – solutions are detected by evaluating a function over interval steps, for a change in sign, adjusting the step size dynamically. Limitations – can miss closely spaced solutions in large intervals, cannot detect degenerate (coinciding) solutions, limited to functions that cross the x-axis, gives false positives for singularities Fixed point method http://en.wikipedia.org/wiki/Fixed-point_iteration C++: http://books.google.co.il/books?id=weYj75E_t6MC&pg=PA79&lpg=PA79&dq=fixed+point+method++c%2B%2B&source=bl&ots=LQ-5P_taoC&sig=lENUUIYBK53tZtTwNfHLy5PEWDk&hl=en&sa=X&ei=wezDUPW1J5DptQaMsIHQCw&redir_esc=y#v=onepage&q=fixed%20point%20method%20%20c%2B%2B&f=false Algebraically rearrange a solution to isolate a variable then apply incremental method Bisection method http://en.wikipedia.org/wiki/Bisection_method C++: http://numericalcomputing.wordpress.com/category/algorithms/ Bracketed - Select an initial interval, keep bisecting it ad midpoint into sub-intervals and then apply incremental method on smaller & smaller intervals – zoom in Adv: unaffected by function gradient à reliable Disadv: slow convergence False Position Method http://en.wikipedia.org/wiki/False_position_method C++: http://www.dreamincode.net/forums/topic/126100-bisection-and-false-position-methods/ Bracketed - Select an initial interval , & use the relative value of function at interval end points to select next sub-intervals (estimate how far between the end points the solution might be & subdivide based on this) Newton-Raphson method http://en.wikipedia.org/wiki/Newton's_method C++: http://www-users.cselabs.umn.edu/classes/Summer-2012/csci1113/index.php?page=./newt3 Also known as Newton's method Convenient, efficient Not bracketed – only a single initial guess is required to start iteration – requires an analytical expression for the first derivative of the function as input. Evaluates the function & its derivative at each step. Can be extended to the Newton MutiRoot method for solving multiple roots Can be easily applied to an of n-coupled set of non-linear equations – conduct a Taylor Series expansion of a function, dropping terms of order n, rewrite as a Jacobian matrix of PDs & convert to simultaneous linear equations !!! Secant Method http://en.wikipedia.org/wiki/Secant_method C++: http://forum.vcoderz.com/showthread.php?p=205230 Unlike N-R, can estimate first derivative from an initial interval (does not require root to be bracketed) instead of inputting it Since derivative is approximated, may converge slower. Is fast in practice as it does not have to evaluate the derivative at each step. Similar implementation to False Positive method Birge-Vieta Method http://mat.iitm.ac.in/home/sryedida/public_html/caimna/transcendental/polynomial%20methods/bv%20method.html C++: http://books.google.co.il/books?id=cL1boM2uyQwC&pg=SA3-PA51&lpg=SA3-PA51&dq=Birge-Vieta+Method+c%2B%2B&source=bl&ots=QZmnDTK3rC&sig=BPNcHHbpR_DKVoZXrLi4nVXD-gg&hl=en&sa=X&ei=R-_DUK2iNIjzsgbE5ID4Dg&redir_esc=y#v=onepage&q=Birge-Vieta%20Method%20c%2B%2B&f=false combines Horner's method of polynomial evaluation (transforming into lesser degree polynomials that are more computationally efficient to process) with Newton-Raphson to provide a computational speed-up Interpolation Overview Construct new data points for as close as possible fit within range of a discrete set of known points (that were obtained via sampling, experimentation) Use Taylor Series Expansion of a function f(x) around a specific value for x Linear Interpolation http://en.wikipedia.org/wiki/Linear_interpolation C++: http://www.hamaluik.com/?p=289 Straight line between 2 points à concatenate interpolants between each pair of data points Bilinear Interpolation http://en.wikipedia.org/wiki/Bilinear_interpolation C++: http://supercomputingblog.com/graphics/coding-bilinear-interpolation/2/ Extension of the linear function for interpolating functions of 2 variables – perform linear interpolation first in 1 direction, then in another. Used in image processing – e.g. texture mapping filter. Uses 4 vertices to interpolate a value within a unit cell. Lagrange Interpolation http://en.wikipedia.org/wiki/Lagrange_polynomial C++: http://www.codecogs.com/code/maths/approximation/interpolation/lagrange.php For polynomials Requires recomputation for all terms for each distinct x value – can only be applied for small number of nodes Numerically unstable Barycentric Interpolation http://epubs.siam.org/doi/pdf/10.1137/S0036144502417715 C++: http://www.gamedev.net/topic/621445-barycentric-coordinates-c-code-check/ Rearrange the terms in the equation of the Legrange interpolation by defining weight functions that are independent of the interpolated value of x Newton Divided Difference Interpolation http://en.wikipedia.org/wiki/Newton_polynomial C++: http://jee-appy.blogspot.co.il/2011/12/newton-divided-difference-interpolation.html Hermite Divided Differences: Interpolation polynomial approximation for a given set of data points in the NR form - divided differences are used to approximately calculate the various differences. For a given set of 3 data points , fit a quadratic interpolant through the data Bracketed functions allow Newton divided differences to be calculated recursively Difference table Cubic Spline Interpolation http://en.wikipedia.org/wiki/Spline_interpolation C++: https://www.marcusbannerman.co.uk/index.php/home/latestarticles/42-articles/96-cubic-spline-class.html Spline is a piecewise polynomial Provides smoothness – for interpolations with significantly varying data Use weighted coefficients to bend the function to be smooth & its 1st & 2nd derivatives are continuous through the edge points in the interval Curve Fitting A generalization of interpolating whereby given data points may contain noise à the curve does not necessarily pass through all the points Least Squares Fit http://en.wikipedia.org/wiki/Least_squares C++: http://www.ccas.ru/mmes/educat/lab04k/02/least-squares.c Residual – difference between observed value & expected value Model function is often chosen as a linear combination of the specified functions Determines: A) The model instance in which the sum of squared residuals has the least value B) param values for which model best fits data Straight Line Fit Linear correlation between independent variable and dependent variable Linear Regression http://en.wikipedia.org/wiki/Linear_regression C++: http://www.oocities.org/david_swaim/cpp/linregc.htm Special case of statistically exact extrapolation Leverage least squares Given a basis function, the sum of the residuals is determined and the corresponding gradient equation is expressed as a set of normal linear equations in matrix form that can be solved (e.g. using LU Decomposition) Can be weighted - Drop the assumption that all errors have the same significance –-> confidence of accuracy is different for each data point. Fit the function closer to points with higher weights Polynomial Fit - use a polynomial basis function Moving Average http://en.wikipedia.org/wiki/Moving_average C++: http://www.codeproject.com/Articles/17860/A-Simple-Moving-Average-Algorithm Used for smoothing (cancel fluctuations to highlight longer-term trends & cycles), time series data analysis, signal processing filters Replace each data point with average of neighbors. Can be simple (SMA), weighted (WMA), exponential (EMA). Lags behind latest data points – extra weight can be given to more recent data points. Weights can decrease arithmetically or exponentially according to distance from point. Parameters: smoothing factor, period, weight basis Optimization Overview Given function with multiple variables, find Min (or max by minimizing –f(x)) Iterative approach Efficient, but not necessarily reliable Conditions: noisy data, constraints, non-linear models Detection via sign of first derivative - Derivative of saddle points will be 0 Local minima Bisection method Similar method for finding a root for a non-linear equation Start with an interval that contains a minimum Golden Search method http://en.wikipedia.org/wiki/Golden_section_search C++: http://www.codecogs.com/code/maths/optimization/golden.php Bisect intervals according to golden ratio 0.618.. Achieves reduction by evaluating a single function instead of 2 Newton-Raphson Method Brent method http://en.wikipedia.org/wiki/Brent's_method C++: http://people.sc.fsu.edu/~jburkardt/cpp_src/brent/brent.cpp Based on quadratic or parabolic interpolation – if the function is smooth & parabolic near to the minimum, then a parabola fitted through any 3 points should approximate the minima – fails when the 3 points are collinear , in which case the denominator is 0 Simplex Method http://en.wikipedia.org/wiki/Simplex_algorithm C++: http://www.codeguru.com/cpp/article.php/c17505/Simplex-Optimization-Algorithm-and-Implemetation-in-C-Programming.htm Find the global minima of any multi-variable function Direct search – no derivatives required At each step it maintains a non-degenerative simplex – a convex hull of n+1 vertices. Obtains the minimum for a function with n variables by evaluating the function at n-1 points, iteratively replacing the point of worst result with the point of best result, shrinking the multidimensional simplex around the best point. Point replacement involves expanding & contracting the simplex near the worst value point to determine a better replacement point Oscillation can be avoided by choosing the 2nd worst result Restart if it gets stuck Parameters: contraction & expansion factors Simulated Annealing http://en.wikipedia.org/wiki/Simulated_annealing C++: http://code.google.com/p/cppsimulatedannealing/ Analogy to heating & cooling metal to strengthen its structure Stochastic method – apply random permutation search for global minima - Avoid entrapment in local minima via hill climbing Heating schedule - Annealing schedule params: temperature, iterations at each temp, temperature delta Cooling schedule – can be linear, step-wise or exponential Differential Evolution http://en.wikipedia.org/wiki/Differential_evolution C++: http://www.amichel.com/de/doc/html/ More advanced stochastic methods analogous to biological processes: Genetic algorithms, evolution strategies Parallel direct search method against multiple discrete or continuous variables Initial population of variable vectors chosen randomly – if weighted difference vector of 2 vectors yields a lower objective function value then it replaces the comparison vector Many params: #parents, #variables, step size, crossover constant etc Convergence is slow – many more function evaluations than simulated annealing Numerical Differentiation Overview 2 approaches to finite difference methods: · A) approximate function via polynomial interpolation then differentiate · B) Taylor series approximation – additionally provides error estimate Finite Difference methods http://en.wikipedia.org/wiki/Finite_difference_method C++: http://www.wpi.edu/Pubs/ETD/Available/etd-051807-164436/unrestricted/EAMPADU.pdf Find differences between high order derivative values - Approximate differential equations by finite differences at evenly spaced data points Based on forward & backward Taylor series expansion of f(x) about x plus or minus multiples of delta h. Forward / backward difference - the sums of the series contains even derivatives and the difference of the series contains odd derivatives – coupled equations that can be solved. Provide an approximation of the derivative within a O(h^2) accuracy There is also central difference & extended central difference which has a O(h^4) accuracy Richardson Extrapolation http://en.wikipedia.org/wiki/Richardson_extrapolation C++: http://mathscoding.blogspot.co.il/2012/02/introduction-richardson-extrapolation.html A sequence acceleration method applied to finite differences Fast convergence, high accuracy O(h^4) Derivatives via Interpolation Cannot apply Finite Difference method to discrete data points at uneven intervals – so need to approximate the derivative of f(x) using the derivative of the interpolant via 3 point Lagrange Interpolation Note: the higher the order of the derivative, the lower the approximation precision Numerical Integration Estimate finite & infinite integrals of functions More accurate procedure than numerical differentiation Use when it is not possible to obtain an integral of a function analytically or when the function is not given, only the data points are Newton Cotes Methods http://en.wikipedia.org/wiki/Newton%E2%80%93Cotes_formulas C++: http://www.siafoo.net/snippet/324 For equally spaced data points Computationally easy – based on local interpolation of n rectangular strip areas that is piecewise fitted to a polynomial to get the sum total area Evaluate the integrand at n+1 evenly spaced points – approximate definite integral by Sum Weights are derived from Lagrange Basis polynomials Leverage Trapezoidal Rule for default 2nd formulas, Simpson 1/3 Rule for substituting 3 point formulas, Simpson 3/8 Rule for 4 point formulas. For 4 point formulas use Bodes Rule. Higher orders obtain more accurate results Trapezoidal Rule uses simple area, Simpsons Rule replaces the integrand f(x) with a quadratic polynomial p(x) that uses the same values as f(x) for its end points, but adds a midpoint Romberg Integration http://en.wikipedia.org/wiki/Romberg's_method C++: http://code.google.com/p/romberg-integration/downloads/detail?name=romberg.cpp&can=2&q= Combines trapezoidal rule with Richardson Extrapolation Evaluates the integrand at equally spaced points The integrand must have continuous derivatives Each R(n,m) extrapolation uses a higher order integrand polynomial replacement rule (zeroth starts with trapezoidal) à a lower triangular matrix set of equation coefficients where the bottom right term has the most accurate approximation. The process continues until the difference between 2 successive diagonal terms becomes sufficiently small. Gaussian Quadrature http://en.wikipedia.org/wiki/Gaussian_quadrature C++: http://www.alglib.net/integration/gaussianquadratures.php Data points are chosen to yield best possible accuracy – requires fewer evaluations Ability to handle singularities, functions that are difficult to evaluate The integrand can include a weighting function determined by a set of orthogonal polynomials. Points & weights are selected so that the integrand yields the exact integral if f(x) is a polynomial of degree <= 2n+1 Techniques (basically different weighting functions): · Gauss-Legendre Integration w(x)=1 · Gauss-Laguerre Integration w(x)=e^-x · Gauss-Hermite Integration w(x)=e^-x^2 · Gauss-Chebyshev Integration w(x)= 1 / Sqrt(1-x^2) Solving ODEs Use when high order differential equations cannot be solved analytically Evaluated under boundary conditions RK for systems – a high order differential equation can always be transformed into a coupled first order system of equations Euler method http://en.wikipedia.org/wiki/Euler_method C++: http://rosettacode.org/wiki/Euler_method First order Runge–Kutta method. Simple recursive method – given an initial value, calculate derivative deltas. Unstable & not very accurate (O(h) error) – not used in practice A first-order method - the local error (truncation error per step) is proportional to the square of the step size, and the global error (error at a given time) is proportional to the step size In evolving solution between data points xn & xn+1, only evaluates derivatives at beginning of interval xn à asymmetric at boundaries Higher order Runge Kutta http://en.wikipedia.org/wiki/Runge%E2%80%93Kutta_methods C++: http://www.dreamincode.net/code/snippet1441.htm 2nd & 4th order RK - Introduces parameterized midpoints for more symmetric solutions à accuracy at higher computational cost Adaptive RK – RK-Fehlberg – estimate the truncation at each integration step & automatically adjust the step size to keep error within prescribed limits. At each step 2 approximations are compared – if in disagreement to a specific accuracy, the step size is reduced Boundary Value Problems Where solution of differential equations are located at 2 different values of the independent variable x à more difficult, because cannot just start at point of initial value – there may not be enough starting conditions available at the end points to produce a unique solution An n-order equation will require n boundary conditions – need to determine the missing n-1 conditions which cause the given conditions at the other boundary to be satisfied Shooting Method http://en.wikipedia.org/wiki/Shooting_method C++: http://ganeshtiwaridotcomdotnp.blogspot.co.il/2009/12/c-c-code-shooting-method-for-solving.html Iteratively guess the missing values for one end & integrate, then inspect the discrepancy with the boundary values of the other end to adjust the estimate Given the starting boundary values u1 & u2 which contain the root u, solve u given the false position method (solving the differential equation as an initial value problem via 4th order RK), then use u to solve the differential equations. Finite Difference Method For linear & non-linear systems Higher order derivatives require more computational steps – some combinations for boundary conditions may not work though Improve the accuracy by increasing the number of mesh points Solving EigenValue Problems An eigenvalue can substitute a matrix when doing matrix multiplication à convert matrix multiplication into a polynomial EigenValue For a given set of equations in matrix form, determine what are the solution eigenvalue & eigenvectors Similar Matrices - have same eigenvalues. Use orthogonal similarity transforms to reduce a matrix to diagonal form from which eigenvalue(s) & eigenvectors can be computed iteratively Jacobi method http://en.wikipedia.org/wiki/Jacobi_method C++: http://people.sc.fsu.edu/~jburkardt/classes/acs2_2008/openmp/jacobi/jacobi.html Robust but Computationally intense – use for small matrices < 10x10 Power Iteration http://en.wikipedia.org/wiki/Power_iteration For any given real symmetric matrix, generate the largest single eigenvalue & its eigenvectors Simplest method – does not compute matrix decomposition à suitable for large, sparse matrices Inverse Iteration Variation of power iteration method – generates the smallest eigenvalue from the inverse matrix Rayleigh Method http://en.wikipedia.org/wiki/Rayleigh's_method_of_dimensional_analysis Variation of power iteration method Rayleigh Quotient Method Variation of inverse iteration method Matrix Tri-diagonalization Method Use householder algorithm to reduce an NxN symmetric matrix to a tridiagonal real symmetric matrix vua N-2 orthogonal transforms     Whats Next Outside of Numerical Methods there are lots of different types of algorithms that I’ve learned over the decades: Data Mining – (I covered this briefly in a previous post: http://geekswithblogs.net/JoshReuben/archive/2007/12/31/ssas-dm-algorithms.aspx ) Search & Sort Routing Problem Solving Logical Theorem Proving Planning Probabilistic Reasoning Machine Learning Solvers (eg MIP) Bioinformatics (Sequence Alignment, Protein Folding) Quant Finance (I read Wilmott’s books – interesting) Sooner or later, I’ll cover the above topics as well.

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  • DundeeWealth Selects Oracle CRM On Demand as Core Platform

    - by andrea.mulder
    "Oracle CRM On Demand enhances our existing Oracle platform, providing an integrated solution with incredible flexibility, mobility, agility and lowered total cost of ownership," said To Anh Tran, Senior Vice President of Business Transformation and Technology at DundeeWealth Inc. "Using Oracle as a partner in the expansion of DundeeWealth's CRM processes reinforces our client-centric approach to customer service and we believe it gives us a competitive advantage. As we begin our deployment, we are confident that Oracle is with us every step of the way." Click here to read more about more about DundeeWealth's plans.

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