In many fields one encounters the challenge of identifying, out of a pool of possible designs, those that simultaneously optimize multiple objectives. This means that usually there is not one optimal design but an entire set of Pareto-optimal ones with optimal tradeoffs in the objectives. In many applications, evaluating one design is expensive; thus, an exhaustive search for the Pareto-optimal...

We propose a new view of active learning algorithms as optimization. We show that many online active learning algorithms can be viewed as stochastic gradient descent on non-convex objective functions. Variations of some of these algorithms and objective functions have been previously proposed without noting this connection. We also point out a connection between the standard min-margin offline ...

The paper presents the way in which the active control can be improved via PC graphic software. It is describes an example in which for a dimensional control gauge, for different dimension shafts, the dimensional measuring can be aided by computer. In this way, all the necessary information regarding the form deviations of the shaft, could be generated and stored during the measuring process. T...

Many real-world problems have complicated objective functions. To optimize such functions, humans utilize sophisticated sequential decision-making strategies. Many optimization algorithms have also been developed for this same purpose, but how do they compare to humans in terms of both performance and behavior? We try to unravel the general underlying algorithm people may be using while searchi...

In an active database, an update may be constrained by integrity constraints, and may also trigger rules that, in turn, may a ect the database state. The general problem is to e ect the update while also managing the \side-e ects" of constraint enforcement and rule execution. In this paper an update calculus is proposed by which updates, constraints and rules are speci ed and managed within the...

Optimization is one of the most important issues in all fields of science and engineering. There are two main categories for optimization problems: continues optimization and discrete optimization. Traditional methods, such as gradient descent, are used for solving continues optimization problems, But for discrete optimization, traditional and many new algorithms are introduced. Due to long tim...

Self driving cars hold the promise of making transportation safer and more efficient than ever before possible. They will also be among the most complex robotic systems ever fielded and thus require an unprecedented level of machine learning throughout to achieve their desired performance. These learners create an everchanging environment for all algorithms operating in the system and optimizin...

Active Queue Management (AQM) is an important problem in networking. In this paper, we propose a general functional optimization model for designing AQM schemes. Unlike the previous static function optimization models based on the artificial notion of utility function, the proposed dynamic functional optimization formulation allows us to directly characterize the desirable system behavior of AQ...

First order stochastic convex optimization is an extremely well-studied area with a rich history of over a century of optimization research. Active learning is a relatively newer discipline that grew independently of the former, gaining popularity in the learning community over the last few decades due to its promising improvements over passive learning. Over the last year, we have uncovered co...