Optimal Matching Analysis (OMA) google
Optimal matching is a sequence analysis method used in social science, to assess the dissimilarity of ordered arrays of tokens that usually represent a time-ordered sequence of socio-economic states two individuals have experienced. Once such distances have been calculated for a set of observations (e.g. individuals in a cohort) classical tools (such as cluster analysis) can be used. The method was tailored to social sciences from a technique originally introduced to study molecular biology (protein or genetic) sequences. Optimal matching uses the Needleman-Wunsch algorithm.

Apache Flink google
Apache Flink is an open source platform for scalable batch and stream data processing. Flink’s core is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations over data streams. Flink includes several APIs for creating applications that use the Flink engine:
1. DataSet API for static data embedded in Java, Scala, and Python,
2. DataStream API for unbounded streams embedded in Java and Scala, and
3. Table API with a SQL-like expression language embedded in Java and Scala.
Flink also bundles libraries for domain-specific use cases:
1. Machine Learning library, and
2. Gelly, a graph processing API and library.
You can integrate Flink easily with other well-known open source systems both for data input and output as well as deployment.

Disciplined Convex Optimization google
An object-oriented modeling language for disciplined convex programming (DCP). It allows the user to formulate convex optimization problems in a natural way following mathematical convention and DCP rules. The system analyzes the problem, verifies its convexity, converts it into a canonical form, and hands it off to an appropriate solver to obtain the solution.
“Disciplined Convex Programming”