A large body of discrete-choice demand studies estimate a model in which the consumer’s budget constraint is not taken into account. We illustrate how incorrectly specifying consideration set, when fact binds for some products, may bias estimates. and quantify nature three ways: (i) analytical examples; (ii) field data commonly used literature (iii) Monte Carlo study. find that price sensitivit...

the aim of this work is to investigate the variational discretization and mixed finite element methods for optimal control problem governed by semi linear parabolic equations with integral constraint. the state and co-state are approximated by the lowest order raviart-thomas mixed finite element spaces and the control is not discreted. optimal error estimates in l2 are established for the state...

Stability analysis for optimization problems with chance constraints concerns impact of variation of probability measure in the chance constraints on the optimal value and optimal solutions and research on the topic has been well documented in the literature of stochastic programming. In this paper, we extend such analysis to optimization problems with distributionally robust chance constraints...

Resistance to autonomous systems comes in part from the perceived unreliability of the systems. Concerns can be addressed by guarantees of the probability of success. This is achieved in chanceconstrained constraint programming (CC-CP) by imposing constraints required for success, and providing upper-bounds on the probability of violating constraints. This extended abstract reports on novel unc...

This paper presents a novel methodology for constructing maximum-margin classifiers which are robust to interval-valued uncertainty in examples. The idea is to employ chance-constraints which ensure that the uncertain examples are classified correctly with high probability. The key novelty is in employing Bernstein bounding schemes to relax the resulting chance-constrained program as a convex s...

It is generally assumed that the variables, domains and constraints of a Finite-Domain Constraint Satisfaction Problem are all pre-computed inputs to a black-box constraint satisfaction algorithm. The obvious advantages of such an assumption is the freedom in developing generic constraint solvers and the declarative use of constraint technology. However, it is useful to examine applications of ...

This paper aims at determining the optimal number of new facilities besides specifying both the optimal location and design level of them under the budget constraint in a competitive environment by a novel hybrid continuous and discrete firefly algorithm. A real-world application of locating new chain stores in the city of Tehran, Iran, is used and the results are analyzed. In addition, several...

Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for representing continuous chance variables in influence diagrams. Also, MTE potentials can be used to approximate utility functions. This paper introduces MTE influence diagrams, which can represent decision problems without restrictions on the relationships between continuous and discrete chance variable...