2024

Heure:	11:00 - 12:00
Lieu:	Salle C308, Institut Galilée, Université de Villetaneuse
Résumé:	A Knowledge Compilation Take on Binary Boolean Optimization
Description:	Florent Capelli The Binary Boolean Optimization (BPO) problem aims at finding the maximal value that a rational polynomial P(x) can take when x is supposed to be a vector with 0 and 1 values. This non-linear optimization problem has recently received renewed attention. Current techniques for solving it either involve to solve a linear relaxation of the problem or use dedicated algorithm exploiting some structure in the way monomials are interacting with one another, allowing one to skip large parts of the search space compared to the brute force approach. In this talk, we present and explore the consequences of an interesting connection between BPO instances and another well studied problem on Boolean functions: the Algebraic Model Counting (AMC) problem. Given a Boolean function f on variables X and a weight on each of its variable, the AMC problem aims at finding the sum of the weights of every satisfying assignments of f. This problem can encode a lot of different tasks by simply changing the underlying algebraic structure where the sum and products are made. This way, we show how one can reformulate BPO instances as an AMC problem on an algebraic structure known as the (max,+)-semiring. The consequences of this connection are manyfold. In particular, we are able to recover every known results on the tractablability of BPO problem from this connection and the existing literature on the complexity of AMC. More importantly, this connection allows us to discover new tractable classes for BPO and is flexible enough so that we can find tractable instances of the slight variations of BPO such as BPO with cardinality constraints or pseudo-Boolean BPO, two problems for which few tractability results where known. More importantly, this approach yields practical results: by running a modified version of d4, a tool originally made for knowledge compilation, so that it performs AMC on the (max,+)-semiring instead, we show that our approach is competitive with the existing ones on hard instances. This talk will cover a gentle presentation of the BPO problem and its connection with AMC. We will then give a quick overview on existing techniques for solving AMC that are based on Knowledge Compilation and how this approach is fruitful for solving extensions of the BPO problem. We will conclude by a presentation of the way d4 works and of our practical results.

Heure:	10:30 - 11:00
Lieu:	Salle B107, bâtiment B, Université de Villetaneuse
Résumé:	Online policy selection for inventory problems
Description:	Adeline Fermanian After a general presentation of the company Califrais and of research problems arising in food supply chain, we will focus on a recent work on online inventory problems. These are decision problems where at each time period the manager has to make a replenishment decision based on partial historical information in order to meet demands and minimize costs. To solve such problems, we build upon recent works in online learning and control, use insights from inventory theory and propose a new algorithm called GAPSI. This algorithm follows a new feature-enhanced base-stock policy and deals with the troublesome question of non-differentiability which occurs in inventory problems. Our method is illustrated in the context of a complex and novel inventory system involving multiple products, lost sales, perishability, warehouse-capacity constraints and lead times. Extensive numerical simulations are conducted to demonstrate the good performances of our algorithm on real-world data.

Heure:	10:30 - 11:30
Lieu:	Salle B107, bâtiment B, Université de Villetaneuse
Résumé:	New perspectives on invexity and its algorithmic applications
Description:	Ksenia Bestuzheva One of the key properties of convex problems is that every stationary point is a global optimum, and nonlinear programming algorithms that converge to local optima are thus guaranteed to find the global optimum. However, some nonconvex problems possess the same property. This observation has motivated research into generalizations of convexity. This talk proposes a new generalization which we refer to as optima-invexity: the property that only one connected set of optimal solutions exists. We state conditions for optima-invexity of unconstrained problems and discuss structures that are promising for practical use, and outline algorithmic applications of these structures.