Himanshu Jain

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Having developed multi-objective optimization algorithms using evolutionary optimization methods and demonstrated their niche on various practical problems involving mostly two and three objectives, there is now a growing need for developing evolutionary multi-objective optimization (EMO) algorithms for handling many-objective (having four or more(More)
In the precursor paper [1], a many-objective optimization method (NSGA-III), based on the NSGA-II framework, was suggested and applied to a number of unconstrained (with box constraints alone) test and practical problems. In this paper, we extend NSGA-III to solve generic constrained many-objective optimization problems. In the process, we also suggest(More)
The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set. Embedding based approaches attempt to make training and prediction tractable by assuming that the training label matrix is low-rank and reducing the effective number(More)
Model checking techniques applied to large industrial circuits suffer from the state space explosion problem. A major technique to address this problem is abstraction. The most commonly used abstraction technique for hardware verification is localization reduction, which removes latches that are not relevant to the property. However, localization reduction(More)
Checking the equivalence of a system-level model against an RTL design is a major challenge. The reason is that usually the system-level model is written by a system architect, whereas the RTL implementation is created by a hardware designer. This approach leads to two models that are significantly different. Checking the equivalence of real-life designs(More)
The use of Craig interpolants has enabled the development of powerful hardware and software model checking techniques. Efficient algorithms are known for computing interpolants in rational and real linear arithmetic. We focus on subsets of integer linear arithmetic. Our main results are polynomial time algorithms for obtaining proofs of unsatisfiability and(More)
Boolean satisfiability (SAT) solvers are used heavily in hardware and software verification tools for checking satisfiability of Boolean formulas. Most state-of-the-art SAT solvers are based on the Davis-Putnam-Logemann-Loveland (DPLL) algorithm and require the input formula to be in conjunctive normal form (CNF). We present a new SAT solver that operates(More)
The choice of the loss function is critical in extreme multi-label learning where the objective is to annotate each data point with the most relevant subset of labels from an extremely large label set. Unfortunately, existing loss functions, such as the Hamming loss, are unsuitable for learning, model selection, hyperparameter tuning and performance(More)
The primary motivations for the formation of airline alliances have been to increase revenues and decrease costs for alliance partners. A major advantage comes through increase in the number of destinations served by an airline at little costs, by using codesharing. Airlines share seat inventory on each other's codeshare flights which complicates their(More)