We present a fixed point theorem for a class of (potentially) non-monotonic functions over specially structured complete lattices. The theorem has as a special case the Knaster-Tarski fixed point theorem when restricted to the case of monotonic functions and Kleene's theorem when the functions are additionally continuous. From the practical side, the theorem has direct applications in the semantics of negation in logic programming. In particular, it leads to a more direct and elegant proof of the least fixed point result of [Rondogiannis and W.W.Wadge, ACM TOCL 6(2): 441-467 (2005)]. Moreover, the theorem appears to have potential for possible applications outside the logic programming domain.
A more recent work that is also connected to our approach is reported in @cite_10 . In that paper the authors consider the case of product lattices as-well-as stratifiable operators on such lattices. In our terminology, an operator @math is stratifiable iff for all @math and for all @math , if @math then @math . The authors demonstrate @cite_10 [Theorem 3.5] that for every stratifiable operator @math it holds that every fixed point of @math can be constructed using the fixed points of a family of operators called the components of @math (intuitively, to every sublattice @math of the product lattice @math there corresponds a subfamily of the components). However, the least fixed point of @cite_10 [Theorem 3.5] is with respect to the pointwise partial order that is defined on the product lattice @math while in our case @math is not necessarily pointwise. Moreover, our construction does not only apply to product lattices but to all lattices that satisfy the axioms of Subsection . For example, the non-standard product model of Subsection does not fall within the scope of the results developed in @cite_10 .
@cite_10
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We investigate nonparametric multiproduct pricing problems, in which we want to find revenue maximizing prices for products @math based on a set of customer samples @math . We mostly focus on the unit-demand case, in which products constitute strict substitutes and each customer aims to purchase a single product. In this setting a customer sample consists of a number of nonzero values for different products and possibly an additional product ranking. Once prices are fixed, each customer chooses to buy one of the products she can afford based on some predefined selection rule. We distinguish between the min-buying, max-buying, and rank-buying models. Some of our results also extend to single-minded pricing, in which case products are strict complements and every customer seeks to buy a single set of products, which she purchases if the sum of prices is below her valuation for that set. For the min-buying model we show that the revenue maximization problem is not approximable within factor @math for some constant @math , unless @math , thereby almost closing the gap between the known algorithmic results and previous lower bounds. We also prove inapproximability within @math , @math being an upper bound on the number of nonzero values per customer, and @math under slightly stronger assumptions and provide matching upper bounds. Surprisingly, these hardness results hold even if a price ladder constraint, i.e., a predefined order on the prices of all products, is given. Without the price ladder constraint we obtain similar hardness results for the special case of uniform valuations, i.e., the case that every customer has identical values for all the products she is interested in, assuming specific hardness of the balanced bipartite independent set problem in constant degree graphs or hardness of refuting random 3CNF formulas. Introducing a slightly more general problem definition in which customers are given as an explicit probability distribution, we obtain inapproximability within @math assuming @math . These results apply to single-minded pricing as well. For the max-buying model a polynomial-time approximation scheme exists if a price ladder is given. We give a matching lower bound by proving strong NP-hardness. Assuming limited product supply, we analyze a generic local search algorithm and prove that it is 2-approximate. Finally, we discuss implications for the rank-buying model.