All-orders behaviour and renormalons in top-mass observables

We study a simplified model of top production and decay, consisting in a virtual vector boson $W^*$ decaying into a massive-massless $t$-$\bar{b}$ quark-antiquark pair. The top has a finite width and further decays into a stable vector boson $W$ and a $b$ quark. We then consider the emission or the virtual exchange of one gluon, with all possible light-quark loop insertions. These are the dominant diagrams in the limit of an infinite number of light flavours. We devise a procedure to compute this process fully, by analytic and numerical methods, and for any infrared-safe final-state observables. We examine the results at arbitrary orders in perturbation theory, and assess the factorial growth associated with renormalons. We look for renormalon effects leading to corrections of order $\Lambda_{\rm QCD}$, that we dub `linear' renormalons, in the inclusive cross section (with and without selection cuts), in the mass of the reconstructed-top system, and in the average energy of the final-state $W$ boson, considering both the pole and the $\overline{\rm MS}$ scheme for the top mass. We find that the total cross section without cuts, if expressed in terms of the $\overline{\rm MS}$ mass, does not exhibit linear renormalons, but, as soon as selection cuts are introduced, jets-related linear renormalons arise in any mass scheme. In addition, we show that the reconstructed mass is affected by linear renormalons in any scheme and that the average energy of the $W$ boson (that we consider as a simplified example of leptonic observable), in any mass scheme, has a renormalon in the narrow-width limit, that is however screened at large orders for finite top widths, provided the top mass is in the $\overline{\rm MS}$ scheme.Comment: 40 pages, 17 figure

Models and Solutions of Resource Allocation Problems based on Integer Linear and Nonlinear Programming

In this thesis we deal with two problems of resource allocation solved through a Mixed-Integer Linear Programming approach and a Mixed-Integer Nonlinear Chance Constraint Programming approach. In the first part we propose a framework to model general guillotine restrictions in two dimensional cutting problems formulated as Mixed-Integer Linear Programs (MILP). The modeling framework requires a pseudo-polynomial number of variables and constraints, which can be effectively enumerated for medium-size instances. Our modeling of general guillotine cuts is the first one that, once it is implemented within a state of-the-art MIP solver, can tackle instances of challenging size. Our objective is to propose a way of modeling general guillotine cuts via Mixed Integer Linear Programs (MILP), i.e., we do not limit the number of stages (restriction (ii)), nor impose the cuts to be restricted (restriction (iii)). We only ask the cuts to be guillotine ones (restriction (i)). We mainly concentrate our analysis on the Guillotine Two Dimensional Knapsack Problem (G2KP), for which a model, and an exact procedure able to significantly improve the computational performance, are given. In the second part we present a Branch-and-Cut algorithm for a class of Nonlinear Chance Constrained Mathematical Optimization Problems with a finite number of scenarios. This class corresponds to the problems that can be reformulated as Deterministic Convex Mixed-Integer Nonlinear Programming problems, but the size of the reformulation is large and quickly becomes impractical as the number of scenarios grows. We apply the Branch-and-Cut algorithm to the Mid-Term Hydro Scheduling Problem, for which we propose a chance-constrained formulation. A computational study using data from ten hydro plants in Greece shows that the proposed methodology solves instances orders of magnitude faster than applying a general-purpose solver for Convex Mixed-Integer Nonlinear Problems to the deterministic reformulation, and scales much better with the number of scenarios

A new formulation of the European day-ahead electricity market problem and its algorithmic consequences

A new formulation of the optimization problem implementing European market rules for non- convex day-ahead electricity markets is presented, that avoids the use of complementarity constraints to express market equilibrium conditions, and also avoids the introduction of auxiliary binary variables to linearise these constraints. Instead, we rely on strong duality theory for linear or convex quadratic optimization problems to recover equilibrium constraints imposed by most of European power exchanges facing indivisible orders. When only so-called stepwise preference curves are considered to describe continuous bids, the new formulation allows to take full advantage of state-of-the-art solvers, and in most cases, an optimal solution together with market clearing prices can be computed for large-scale instances without any further algorithmic work. The new formulation also suggests a very competitive Benders-like decomposition procedure, which helps to handle the case of interpolated preference curves that yield quadratic primal and dual objective functions, and consequently a dense quadratic constraint. This procedure essentially consists in strengthening classical Benders cuts locally. Computational experiments on real data kindly provided by main European power exchanges (Apx-Endex, Belpex and Epex spot) show that in the linear case, both approaches are very efficient, while for quadratic instances, only the decomposition procedure is tractable and shows very good results. Finally, when most orders are block orders, and instances are combinatorially very hard, the new MILP approach is substantially more efficient