Products and Tools
Numeric Solvers
Sparsix provides a suite of high-performance numeric solvers optimized for large sparse linear systems.
SparSol™ is a library of highly-efficient algorithms intended for the preconditioned, iterative solution of large sparse linear systems with real coefficients. It includes sets of iterative methods, preconditioners, scaling and reordering algorithms that allow users to choose the optimal combination of algorithms for a particular problem.
SparSol Datasheet (pdf)
SparSol Overview (pdf)
SparSol Whitepaper (pdf)
LinCoS™ is a highly-efficient and scalable parallel solver designed to provide optimal performance when solving large, complex sparse linear systems, in particular those arising from Helmholtz and Maxwell equations. Its unique data model is optimized for handling complex numbers and provides a significant increase in performance over other solvers. LinCoS includes a rich library of partitioners, preconditioners and iterative methods for both serial and parallel computing environments.
LinCoS Overview (pdf)
LinCoS Whitepaper (pdf)
Machine Learning Algorithm Library
Machine learning is a broad field of computational science, strongly grounded in modern mathematics, that encompasses systems that learn and evolve based on data from their environment. Machine learning methodologies create descriptive and predictive models representing the relationships between inputs and outputs of a system. Both simulated and measured data from multiple sources can be used to create models that “teach” themselves from existing examples of a system. The design process is similar to how humans learn from experience.
Numerous applications in the energy sector can benefit from machine learning technology, including
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demand load forecasting
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demand load monitoring
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data health monitoring
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neural network-based predictive controllers for nonlinear control systems
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forecasts of financial instruments such as financial transmission rights and day-ahead pricing
The Sparsix R&D team has over a decade of experience developing and implementing machine learning-based solutions in real-world applications. That experience is captured in a library of algorithms and tools that can be called upon to create custom solutions for a wide variety of customer needs. To learn more about how machine learning methodologies can be applied to challenges in the energy sector, please see our whitepaper:
Machine Learning Opportunities in the Energy Sector (pdf)
Data Optimizers
Optimus™ is a data preprocessing tool for linear programming solvers. Sparsix also develops scaling, reordering and filtration tools that can be used with different solvers in a variety of situations.
Specialized Parallel Partitioning and Preconditioning Tools
Simplex Solver/Optimizers