The special rights other than shares in cross-border mergers of limited liability companies within the European Union. A perspective from the Spanish Law

The present research aims to study the special rights other than shares in Spanish Law and the protection of their holders in cross-border mergers of limited liability companies within the European Union frame. Special rights other than shares are recognised as an independent legal category within legal systems of some EU Member States, such as Germany or Spain, through the implementation of the Third Directive 78/855/CEE concerning mergers of public limited liability companies. The above-cited Directive contains a special regime of protection for the holders of securities, other than shares, to which special rights are attached, consisting of being given rights in the acquiring company, at least equivalent to those they possessed in the company being acquired. This safeguard is to highlight the intimate connection between this type of rights and the company whose extinction determines the existence of those. Pursuant to the Directive 2005/56/CE on cross-border mergers of limited liability companies, each company taking part in these operations shall comply with the safeguards of members and third parties provided in their respective national law to which is subject. In this regard, the protection for holders of special rights other than shares shall be ruled by the domestic M&A regime. As far as Spanish Law are concerned, holders of these special rights are recognized a right of merger information, in the same terms as shareholders, as well as equal rights in the company resulting from the cross-border merger. However, these measures are not enough guarantee for a suitable protection, thus considering those holders of special rights as special creditors, sometimes it will be necessary to go to the general protection regime for creditors. In Spanish Law, it would involve the recognition of right to the merger opposition, whose exercise would prevent the operation was completed until ensuring equal rights.

Portfolio Approaches in Constraint Programming

Recent research has shown that the performance of a single, arbitrarily efficient algorithm can be significantly outperformed by using a portfolio of —possibly on-average slower— algorithms. Within the Constraint Programming (CP) context, a portfolio solver can be seen as a particular constraint solver that exploits the synergy between the constituent solvers of its portfolio for predicting which is (or which are) the best solver(s) to run for solving a new, unseen instance. In this thesis we examine the benefits of portfolio solvers in CP. Despite portfolio approaches have been extensively studied for Boolean Satisfiability (SAT) problems, in the more general CP field these techniques have been only marginally studied and used. We conducted this work through the investigation, the analysis and the construction of several portfolio approaches for solving both satisfaction and optimization problems. We focused in particular on sequential approaches, i.e., single-threaded portfolio solvers always running on the same core. We started from a first empirical evaluation on portfolio approaches for solving Constraint Satisfaction Problems (CSPs), and then we improved on it by introducing new data, solvers, features, algorithms, and tools. Afterwards, we addressed the more general Constraint Optimization Problems (COPs) by implementing and testing a number of models for dealing with COP portfolio solvers. Finally, we have come full circle by developing sunny-cp: a sequential CP portfolio solver that turned out to be competitive also in the MiniZinc Challenge, the reference competition for CP solvers.

Portfolio optimization in the energy market

Let’s put ourselves in the shoes of an energy company. Our fleet of electricity production plants mainly includes gas, hydroelectric and waste-to-energy plants. We also sold contracts for the supply of gas and electricity. For each year we have to plan the trading of the volumes needed by the plants and customers: better to fix the price of these volumes in advance with the so-called forward contracts, instead of waiting for the delivery months, exposing ourselves to price uncertainty. Here’s the thing: trying to keep uncertainty under control in a market that has never shown such extreme scenarios as in recent years: a pandemic, a worsening climate crisis and a war that is affecting economies around the world have made the energy market more volatile than ever. How to make decisions in such uncertain contexts? There is an optimization problem: given a year, we need to choose the optimal planning of volume trading times, to meet the needs of our portfolio at the best prices, taking into account the liquidity constraints given by the market and the risk constraints imposed by the company. Algorithms are needed for the generation of market scenarios over a finite time horizon, that is, a probabilistic distribution that allows a view of all the dates between now and the end of the year of interest. Algorithms are needed to solve the optimization problem: we have proposed more than one and compared them; a very simple one, which avoids considering part of the complexity, moving on to a scenario approach and finally a reinforcement learning approach.