Balanced Combinations of Solutions in Multi-Objective Optimization

Details Balanced Combinations of Solutions in Multi-Objective Optimization Balanced Combinations of Solutions in Multi-Objective Optimization

2010-07-30

arxiv.org/abs/1007.5475v1

Computer Science

Data Structures and Algorithms

Scientific paper

For every list of integers x_1, ..., x_m there is some j such that x_1 + ... + x_j - x_{j+1} - ... - x_m \approx 0. So the list can be nearly balanced and for this we only need one alternation between addition and subtraction. But what if the x_i are k-dimensional integer vectors? Using results from topological degree theory we show that balancing is still possible, now with k alternations. This result is useful in multi-objective optimization, as it allows a polynomial-time computable balance of two alternatives with conflicting costs. The application to two multi-objective optimization problems yields the following results: - A randomized 1/2-approximation for multi-objective maximum asymmetric traveling salesman, which improves and simplifies the best known approximation for this problem. - A deterministic 1/2-approximation for multi-objective maximum weighted satisfiability.

Affiliated with

Also associated with

No associations

Rating

Balanced Combinations of Solutions in Multi-Objective Optimization does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Balanced Combinations of Solutions in Multi-Objective Optimization, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Balanced Combinations of Solutions in Multi-Objective Optimization will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-701123