EAGER: An Exploratory System for Inverse Parametric Optimization

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Perhaps the most prevalent scenario in science in general is reconstructing a phenomenon that is not directly observable using the data that can be measured. In biology, for example, this occurs when computing an evolutionary alignment of homologous proteins, predicting the secondary structure of a folded RNA molecule, inferring a phylogeny for a collection of taxa, recovering the regulatory network for a set of genes, or assembling the DNA sequence for a genome. These tasks are almost always modeled as optimization problems, where the optimal solution is intended to correspond to the correct reconstruction. A crucial ingredient in any such model is the objective function, whose role is to select out the correct solution as one that maximizes or minimizes the objective function. This objective function usually comes from a family of parameterized functions, and the correctness of the model can critically depend on the choice of parameter values for the function. In practice, the question of how to determine the right values for a model's parameters is both difficult and ubiquitous: improved models that better reflect the underlying biology have many parameters, but yield worse results unless their parameters are set to correct values, yet painstakingly exploring the high-dimensional parameter space to find a correct setting quickly becomes impossible. The team is looking to implement new finding of an algorithm in the area of inverse parametric optimization that can efficiently learn correct parameter values for any linear problem, such as those biology problems noted. The system readily yields efficient software for inverse shortest paths, inverse spanning trees, maximum flow, maximum matching and maximum branching, all of which have linear objective function can optimized, enabling efficient model learning for a multitude of computer science applications.The proposed work on inverse parametric optimization has extremely broad scientific impact in computer science and computational biology, as our techniques efficiently solve inverse optimization for any problem with a linear objective function. The PI will also create a new combined course that is an integral part of a new interdisciplinary degree program.