Algorithms. Revolut Wealth provides you with a model portfolio based on your investment objectives as outlined in the Questionnaire you complete. Model portfolios are generated by Revolut Wealth or third parties and, if generated by the third party, are reviewed by Revolut Wealth prior to recommending. The portfolio is managed via automatic portfolio rebalancing based on Revolut Wealth’s internal algorithms and is designed to reasonably keep your portfolio balanced within certain thresholds, while minimizing the number of rebalances and tax impact. If your portfolio deviates from the initial parameters due to market moves or otherwise, our algorithms will periodically monitor the investments and make adjustments to stay within your initial stated risk tolerance. Rebalancing on a particular date can fail for a variety of technical, operational, or business reasons, which can result in potential losses. Revolut Wealth will monitor algorithmic performance and will correct any failed rebalancing. Revolut Wealth will amend the specific algorithm parameters at any time to enhance portfolio performance and risk. Revolut Wealth may also unilaterally exercise its discretion to rebalance a portfolio.
Algorithms. Except as included in Licensed Technology, access by Orchid to algorithms for data mining and for informatics is not included in the licenses granted herein, but may be the subject of a separate agreement, subject to any Xxxxxxx agreements with third parties.
Algorithms. Key Generation The algorithm GKE:KGen, on input the set of clients C and a security pa- rameter `, performs the following steps:
Algorithms. We present three algorithms of increasing efficiency. The baseline is an offline, complete algorithm that computes the diversity graph for full local symmetry.
Algorithms. The categorization algorithms described here are available in the ACT. Individual evaluation for each of the algorithms has been performed on the Reuters-21578 corpus. The results of the evaluations can be found in the “Performance results” subchapter.
Algorithms. We present two algorithms for the proposed structured sparse PCA methods. Al- gorithm 1 obtains the rth principal component loading vector for a fixed tuning parameter τ . Algorithm 2 provides a data driven approach for selecting the optimal tuning parameter value τ from a range of values. The normalization in step (3) of Algorithm 1 eases interpretation, and usually facilitates a visual comparison of the coefficients. Once the principal component loading vector is obtained, the coefficients (in absolute value) can be ranked to assess the contribution of the variables to a given PC. If the variables are measured on different scales or on a common scale with widely differing ranges, then it is recommended to standardize the variables to have unit variance before implementing the proposed methods. Algorithm 1 is developed to obtain r PC loading vectors. For the best r, we can introduce tuning parameter selection in step (2) using, for example cross validation Algorithm 1 Optimization for r structured sparse PC 1: Initialize with nonsparse estimates, α˜r. These are the eigen-decomposition of XTX, and let α˜r be the rth eigen-vector corresponding to the rth largest eigen- value λ˜r of XTX. Here, one can use ideas in (Xxxxxx and Tibshirani, 2004) for the eigen analysis of XTX when p is very large.
Algorithms. We have identified several well-known algorithms that can be adapted to work with for online learning in the presence of imbalanced data. An alternative to algorithm modification is through sampling strategies. But sampling is typically performed as a preprocess to classification, and it does not fit the online context.
Algorithms. 3.1. Kadence is comprised of five principle algorithms for tuning (1) signal splits, (2) offsets, (3) cycle time, (4) phase sequence, and (5) time-of-day (XXX schedule). Second-by-second phase timing and detector data is polled from the controller, and new signal timing parameters are downloaded to field controllers every 3-4 cycles (minimum number of cycles is configurable by the user). The field controller then begins operating in an actuated-coordinated mode with the new settings.
Algorithms. 6.1 In the absence of a funded Cooperative Research and Development Agreement (CRADA) AIPL Algorithms developed solely by employee(s) of AIPL under this Agreement shall be owned by ARS and shall be made publicly available to others by ARS.
Algorithms. The computational study aims to evaluate the performance of different algorithms for solving the MDPC model. We compare the algorithms described in Sections 6 and 7 with two state-of-the- art generic algorithms, namely: CPLEX default implementations of branch-and-cut and of Benders
