A new set of axioms and new method (equal gap seeding) are designed. The equal gap seeding is the unique seeding that, under the deterministic domain assumption, satisfies the delayed confrontation, fairness, increasing competitive intensity and equal rank differences axioms. The equal gap seeding is the unique seeding that, under the linear domain assumption, maximizes the probability that the strongest participant is the winner, the strongest two participants are the finalists, the strongest four participants are the quarterfinalists, etc.
We present an elementary self-contained proof for the Lagrange multiplier rule. It does not refer to any preliminary material and it is only based on the observation that a certain limit is positive. At the end of this note, the power of the Lagrange multiplier rule is analyzed.
We consider reformulations of fractional (hyperbolic) 0-1 programming problems as equivalent mixed-integer linear programs (MILP). The key idea of the proposed technique is to exploit binary representations of certain linear combinations of the 0-1 decision variables. Consequently, under some mild conditions, the number of product terms that need to be linearized can be greatly decreased. We perform numerical experiments comparing the proposed approach against the previous MILP reformulations used in the literature.
The paper suggests a new --- to the best of the author's knowledge --- characterization of Pareto-optimal decisions for the case of two-dimensional utility space which is not supposed to be convex. The main idea is to use the angle distances between the bisector of the first quadrant and points of utility space. A necessary and sufficient condition for Pareto optimality in the form of an equation is derived. The first-order necessary condition for optimality in the form of a pair of equations is also obtained.
We consider a class of convex mixed-integer nonlinear programs motivated by speed scaling of heterogeneous parallel processors with sleep states and convex power consumption curves. We show that the problem is NP-hard and identify some polynomially solvable classes. Furthermore, a dynamic programming and a greedy approximation algorithms are proposed to obtain a fully polynomial-time approximation scheme for a special case. For the general case, we implement an outer approximation algorithm.
We use the imputation distribution procedure approach to ensure sustainable cooperation in a multistage game with vector payoffs. In order to choose a particular Pareto optimal and time consistent strategy profile and the corresponding cooperative trajectory we suggest a refined leximin algorithm. Using this algorithm we design a characteristic function for a multistage multicriteria game. Furthermore, we provide sufficient conditions for strong time consistency of the core.
To ensure sustainable cooperation in multistage games with vector payoffs we use the payment schedule based approach. The main dynamic properties of cooperative solutions used in single-criterion multistage games are extended to multicriteria games.
We design two recurrent payment schedules that satisfy such advantageous properties as the efficiency and the time consistency conditions, non-negativity and irrational behavior proofness.