{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/eb3deb51a6584ec3bb5a118aa89eedac\" frameborder=\"0\" width=\"1832\" height=\"1374\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1374,"width":1832,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1374,"thumbnail_width":1832,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/eb3deb51a6584ec3bb5a118aa89eedac-99063e58e7e0bea0.gif","duration":681.87,"title":"Understanding TitanQ's Optimization Speed and Performance","description":"Hi everyone, I'm Savin Patel, the CTO at InfinityQ. Today, I'll explain how TitanQ'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's capabilities."}