Analog circuits form an important part in integrated circuits and in particular in ASICs (Application Specific Integrated Circuits). However, due to the high complexity, design of this part has become a bottle-neck in the design flow (Gielen, 2007; Rutenbar et al., 2007). To overcome this problem and to guaranty that the analog part can be designed in reasonable time even for future technologie...

This paper models the process of a recommender system as a multiobjective optimization problem, a discrete particle swarm optimization framework is established and has been integrated into multiobjective optimization, consequently, a multiobjective discrete particle swarm optimization algorithm is proposed to solve the modeled optimization problem. Each run of the current mainstream recommender...

The Combinatorial problems are real world decision making problem with discrete and disjunctive choices. When these decision making problems involve more than one conflicting objective and constraint, it turns the polynomial time problem into NP-hard. Thus, the straight forward approaches to solve multi-objective problems would not give an optimal solution. In such case evolutionary based meta-...

We propose a new high-dimensional linear regression estimator: the Discrete Dantzig Selector, which minimizes the number of nonzero regression coefficients, subject to a budget on the maximal absolute correlation between the features and residuals. We show that the estimator can be expressed as a solution to a Mixed Integer Linear Optimization (MILO) problem, a computationally tractable framewo...

Many an agent needs to make a decision of what do to given a limited amount of resources. This is a discrete optimization problem. Recent advances in discrete optimization modelling technology have made it easier than ever before to model difficult ad hoc optimization problems, and apply off the shelf solving technology rapidly and efficiently, while rapid advances in discrete optimization solv...

In this paper, we apply the ideas from combinatorial optimization to find globally optimal solutions to continuous variational problems. At the heart of our method is an algorithm to solve for globally optimal discrete minimal surfaces. This discrete surface problem is a natural generalization of the planar-graph shortest path problem.

Solutions to optimization problems with pde constraints inherit special properties; the associated state solves the pde which in the optimization problem takes the role of a equality constraint, and this state together with the associated control solves an optimization problem, i.e. together with multipliers satisfies first and second order necessary optimality conditions. In this note we revie...

this paper deals with the problem of forbidden states in discrete event systems modeled by petri net. to avoid the forbidden states, some constraints which are called generalized mutual exclusion constraints can be assigned to them. enforcing these constraints on the system can be performed using control places. however, when the number of these constraints is large, a large number of control p...