Abstract. We discuss an implementation of a derivative-free generating set search method for linearly constrained minimization with no assumption of nondegeneracy placed on the constraints. The convergence guarantees for generating set search methods require that the set of search directions possesses certain geometrical properties that allow it to approximate the feasible region near the current iterate. In the hard case, the calculation of the search directions corresponds to finding the extreme rays of a cone with a degenerate vertex at the origin, a difficult problem. We discuss here how state-of-the-art computational geometry methods make it tractable to solve this problem in connection with generating set search. We also discuss a number of other practical issues of implementation, such as the careful treatment of equality constraints and the desirability of augmenting the set of search directions beyond the theoretically minimal set. We illustrate the behavior of the implementation on several problems from the CUTEr test suite. We have found it to be successful on problems with several hundred variables and linear constraints.

No abstract

Virtually every environmental planner at some time deals with environmental impact assessment (EIA). Public participation is required in most environmental impact assessment programmesaround the world. However, citizen involvement is often reduced to a procedural exercise instead of a substantive process to include the public in environmental decision making. This paper examines public participation in EIA and provides ways to improve its effectiveness. We first examine the rationales for public involvement and its institutionalization through EIA. Next, we analyse the shortcomings and strengths of common approaches to public involvement. Our analysis, supported by two case studies, suggests that going beyond the minimum requirements can benefit the public, the project proponent and the final plan. We conclude with practical steps to improve public participation programmes in environmental planning and decision making.

Problem-based learning (PBL) is a promising educational method to help students acquire the skills and knowledge to be more effective practitioners. Students learn how to learn as they tackle a real-world planning problem. The instructor, as a cognitive coach, ensures that students are active, collaborative, and reflective problem-solvers. In this article, we explore the theoretical foundations and the practical benefits of problem-based learning for planning education. We detail a process for implementing problem-based learning in the classroom, illustrated with an example from a planning course. This article suggests that PBL can help to bridge the gap between planning education and practice, and can help to improve traditional methods of academic instruction.

No abstract