Abstract: Systems, methods, and computer-readable media for solving large, sparse convex optimization problems on highly parallel hardware. A computing apparatus receives an initial point and iteratively updates an optimization point via a first subsystem that constructs and updates a Karush-Kuhn-Tucker (KKT) system and applies small steps in an improving direction. In parallel, a second subsystem incrementally solves the updated KKT system, while a third subsystem optimizes parameters such as preconditioners and matrix permutations. Outputs are exchanged among subsystems during each iteration to enable continuous refinement. A small-step criterion, enforced via line search or trust-region procedures, ensures convergence. Execution leverages GPUs or TPUs to partition and process KKT systems concurrently, dynamically adjusting step size based on convergence indicators.

Abstract: A system, method and non-transitory computer-readable storage medium for computing a full dependency graph before obtaining a result of an analytic; and constructing a scheduling graph to optimally distribute work between the available threads, based on the full dependency graph. This may include receiving a request for a result of an algorithm executed on a node; checking, by the processor, the algorithm for a secondary dependency algorithm and executing, by the processor, the algorithm on the node.

Abstract: A platform that includes a plurality of CPU cores and a plurality of GPU cores that receive input of a chain of linear operations; represent or transform each linear operation in the chain to a respective linear operation graph; connect one or more input nodes of each component in the chain to one or more output nodes of a previous component in the chain, with an edge of weight one; iteratively optimize the linear operation graph, thereby improving one or more characteristics of each linear operation graph; map the linear operations graph into a runtime execution plan that is tailored for a specific processing hardware; iteratively optimize the runtime execution plan, thereby improving one or more specific processing hardware execution characteristics; and run the runtime execution plan that has been optimized on the specific processing hardware.

Abstract: Disclosed herein are methods and systems for solving a production network model by iteratively cascading supply decisions. Upstream supply decisions are fixed iteratively, and cascaded down the network in a general way that is independent of any particular use case.

Abstract: Disclosed herein are methods and systems for solving a production network model by iteratively cascading supply decisions. Upstream supply decisions are fixed iteratively, and cascaded down the network in a general way that is independent of any particular use case.

Abstract: Disclosed herein are methods and systems that construct an optimization capability that is able to solve optimization models of supply chain problems faster and with higher quality solutions than prior approaches.

Abstract: Disclosed herein are methods and systems for solving a production network model by iteratively cascading supply decisions. Upstream supply decisions are fixed iteratively, and cascaded down the network in a general way that is independent of any particular use case.

Abstract: Disclosed herein are methods and systems that construct an optimization capability that is able to solve optimization models of supply chain problems faster and with higher quality solutions than prior approaches.

Abstract: A system, method and non-transitory computer-readable storage medium for computing a full dependency graph before obtaining a result of an analytic; and constructing a scheduling graph to optimally distribute work between the available threads, based on the full dependency graph.

Abstract: Aspects of the present disclosure relate to building a user profile data repository. A computer accesses, from a data repository, profile data of a first entity. The computer determines that a set of information items from the accessed profile data of the first entity are associated with a target activity. The computer determines that the set of information items associated with the target activity includes a subset of information items associated with a second entity. The computer creates or edits, within the data repository, profile data of the second entity based on the subset of information items. The computer provides a digital transmission of at least a portion of the profile data of the second entity.

Abstract: A computer receives, from over an electronic network, a plurality of communications associated with a primary individual. The computer determines, based on analysis of the plurality of communications, at least one location associated with the primary individual. Moreover, the computer identifies other individuals associated with the primary individual. The computer retrieves, from one or more databases, information relating to the other individuals.