Symmetries and Distances : two intriguing challenges in Mathematical Programming

Abstract : This thesis is mostly dedicated to study and discuss two important challenges existing not only in the field of Mathematical Programming: symmetries and distances. In the background we take a look into Semidefinite Programming (SDP) and its pertinency as one of the major tools employed nowadays to solve hard Mathematical Programs (MP). After the introductory Chapter 1, we discuss about symmetries in Chapter 2 and about distances in Chapter 5. In between them we present two short chapters that we actually prefer to call as entr’actes: their content is not necessarily worthy of publication yet, but they do provide a connection between the two seemingly separate Chapters 2 and 5, which are the ones containing the main contributions of this thesis. It is widely known that symmetric MPs are harder to solve to global optimality using Branch-and-Bound (B&B) type algorithms, given that the solution symmetry is reflected in the size of the B&B tree. It is also well-known that some of the solution symmetries are usually evident in the formulation, which makes it possible to attempt to deal with symmetries as a preprocessing step. Implementation-wise, one of the simplest approaches is to break symmetries by adjoining Symmetry-Breaking Constraints (SBC) to the formulation, thereby removing some symmetric global optima, then solve the reformulation with a generic solver. Sets of such constraints can be generated from each orbit of the action of the symmetries on the variable index set. It is unclear, however, whether and how it is possible to choose two or more separate orbits to generate SBCs which are compatible with each other (in the sense that they do not make all global optima infeasible). In Chapter 2 we discuss and test a new concept of Orbital Independence (OI) that clarifies this issue. The numerical experiences conducted using public MILPs and MINLPs emphasize the correctness and usefulness of the OI theory. Binary Quadratic Programming (BQP) is used to investigate symmetries and SDP in Entr'acte 3. Symmetric Binary Quadratic Programs having a certain symmetry structure are generated and used to exemplify the conditions under which the usage of SBCs is majoritarily advantageous. A preliminary discussion about the impact of symmetries and SBCs in the performance of SDP solvers is also carried out. The Euclidean Steiner Tree Problem is studied in Entr'acte 4. Two models (which are exact reformulations of an existing formulation) are derived, as well as SDP relaxations. A heuristic algorithm based on both the mathematical models and the OI principles presented in Chapter 2 is also proposed. As concerns these methods, preliminary results on a small set of well-known instances are provided. Finally and following up the Distance Geometry subject, in Chapter 5 we cope with the most fundamental problem arising in the field of Distance Geometry, the one of realizing graphs in Euclidean spaces: it asks to find a realization of an edge-weighted undirected graph in RK for some given K such that the positions for adjacent vertices respect the distance given by the corresponding edge weight. The Euclidean Distance Geometry Problem (EDGP) is of great importance since it has many applications to science and engineering. It is notoriously difficult to solve computationally, and most of the methods proposed so far either do not scale up to useful sizes, or unlikely identify good solutions. In fact, the need to constrain the rank of the matrix representing feasible solutions of the EDGP is what makes the problem so hard. Intending to overcome these issues, we propose a two-steps heuristic algorithm based on SDP (or more precisely based on the very recent Diagonally Dominant Programming paradigm) and the explicitly modeling of Rank Constraints. We provide extensive computational testing against randomly generated instances as well as against feasible realistic protein conformation instances taken from the Protein Data Bank to analyze our method.
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Gustavo Dias da Silva. Symmetries and Distances : two intriguing challenges in Mathematical Programming. Modeling and Simulation. Université Paris-Saclay, 2017. English. ⟨NNT : 2017SACLX008⟩. ⟨tel-01511408⟩

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