<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/eb3deb51a6584ec3bb5a118aa89eedac&quot; frameborder=&quot;0&quot; width=&quot;1832&quot; height=&quot;1374&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1374</height><width>1832</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1374</thumbnail_height><thumbnail_width>1832</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/eb3deb51a6584ec3bb5a118aa89eedac-99063e58e7e0bea0.gif</thumbnail_url><duration>681.87</duration><title>Understanding TitanQ&apos;s Optimization Speed and Performance</title><description>Hi everyone, I&apos;m Savin Patel, the CTO at InfinityQ. Today, I&apos;ll explain how TitanQ&apos;s optimization speed, demonstrated through the index tracking example, outperforms competitors like Garobi. By showcasing the quick solve time and real performance gains of TitanQ, I illustrate how our platform excels in solving complex non-convex problems efficiently. No action requested, just a deep dive into TitanQ&apos;s capabilities.</description></oembed>