In this thesis we propose a new class of Linearly Constrained Convex Optimization methods based on the use of a generalization of Shepard's interpolation formula. We prove the properties of the surface such as the interpolation property at the boundary of the feasible region and the convergence of the gradient to the null space of the constraints at the boundary. We explore several descent techniques such as steepest descent, two quasi-Newton methods and the Newton's method. Moreover, we implement in the Matlab language several versions of the method, particularly for the case of Quadratic Programming with bounded variables. Finally, we carry out performance tests against Matab Optimization Toolbox methods for convex optimization and implementations of the standard log-barrier and active-set methods. We conclude that the steepest descent technique seems to be the best choice so far for our method and that it is competitive with other standard methods both in performance and empirical growth order.

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The problem of finding the convex hull of a set of points in the plane is one of the fundamental and well-studied problems in Computational Geometry. However, for a set of imprecise points, the convex hull problem has not been thoroughly investigated. By imprecise points, we refer to a region in the plane inside which one point may lie. We are particularly interested in finding a minimum perimeter convex hull of a set of imprecise points, where the imprecise points are modelled as line segments. Currently, the best known algorithm that solves the minimum perimeter convex hull problem has an exponential running time in the worst case. It is still unknown whether this problem is NP-hard. We explore several approximation algorithms for this problem. Finally we propose a constant factor approximation algorithm that runs in O(nlogn) time. / Thesis (Master, Computing) -- Queen's University, 2008-11-28 14:47:15.169

In 1948 Besicovitch proved that an affine image of a regular hexagon may be inscribed into an arbitrary planar convex body. We prove Besicovitch's result using a variational approach based on special approximation by triangles and generalize the Besicovitch theorem to a certain new class of hexagons. We survey the results on the Banach-Mazur distance between different classes of convex bodies. We hope that our generalization of the Besicovitch theorem may become useful for estimation of the Banach-Mazur distance between planar convex bodies. We examined our special approximation by triangles in some specific cases, and it showed a noticeable improvement in comparison with known general methods. We also consider the Banach-Mazur distance between a simplex and an arbitrary convex body in the three-dimensional case. Using the idea of an inscribed simplex of maximal volume, we obtain a certain related algebraic optimization problem that provides an upper estimate.

The main purpose of this thesis is to find the number of convex sets on a product of two totally ordered spaces. We will give formulas to find this number for specific cases and describe a process to obtain this number for all such spaces. In the first chapter we briefly discuss the motivation behind the work presented in this thesis. Also, the definitions and notation used throughout the paper are introduced here The second chapter starts with examining the product spaces of the form {1; 2; : : : ;n} × {1; 2}. That is, we begin by analyzing a two-row by n-column space for n > N. Three separate approaches are discussed, and verified, to find the total number of convex sets on the space. A general formula is presented to obtain this total for all n. In the third chapter we take the same {1; 2; : : : ;n} × {1; 2} spaces from Chapter 2 and consider all the scenarios for adding a second disjoint convex set to the space. Adding a second convex set gives a collection of two mutually disjoint sets. Again, a general formula is presented to obtain this total number of such collections for all n. The fourth chapter takes the idea from Chapter 2 and expands it to product spaces {1; 2; : : : ;n} × {1; 2; : : : ;m} consisting of more than two rows. Here the creation of convex sets having z rows from those having z − 1 rows is exploited to obtain a model that will give the total number of z-row convex sets on any n × m space, provided the set occupies z adjacent rows. Finally, the fifth chapter describes all possible scenarios for convex sets to be placed in the {1; 2; : : : ;n}×{1; 2; : : : ;m} space. This chapter then explains the process needed to acquire a count of all convex sets on any such space as well. Chapter 5 ends by walking through this process with a concrete example, breaking it down into each scenario. We conclude by briefly summarizing the results and specifying future work we would like to further investigate, in Chapter 6.

A consequence of the Hahn-Banach theorem is the classical bipolar theorem which states that the bipolar of a subset of a locally convex vector pace equals its closed convex hull. The space $\L$ of real-valued random variables on a probability space $\OF$ equipped with the topology of convergence in measure fails to be locally convex so that - a priori - the classical bipolar theorem does not apply. In this note we show an analogue of the bipolar theorem for subsets of the positive orthant $\LO$, if we place $\LO$ in duality with itself, the scalar product now taking values in $[0, \infty]$. In this setting the order structure of $\L$ plays an important role and we obtain that the bipolar of a subset of $\LO$ equals its closed, convex and solid hull. In the course of the proof we show a decomposition lemma for convex subsets of $\LO$ into a "bounded" and "hereditarily unbounded" part, which seems interesting in its own right. (author's abstract) / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

Modern organizations (e.g., hospitals, banks, social networks, search engines) hold large volumes of personal information, and rely heavily on auditing for enforcement of privacy policies. These audit mechanisms combine automated methods with human input to detect and punish violators. Since human audit resources are limited, and often not sufficient to investigate all potential violations, current state-of-the -art audit tools provide heuristics to guide human effort. However, numerous reports of privacy breaches caused by malicious insiders bring to question the effectiveness of these audit mechanisms. Our thesis is that effective audit resource allocation and punishment levels can be efficiently computed by modeling the audit process as a game between a rational auditor and a rational or worst-case auditee. We present several results in support of the thesis. In the worst-case adversary setting, we design a game model taking into account organizational cost of auditing and loss from violations. We propose the notion of low regret as a desired audit property and provide a regret minimizing audit algorithm that outputs an optimal audit resource allocation strategy. The algorithm improves upon prior regret bounds in the partial information setting. In the rational adversary setting, we enable punishments by the auditor, and model the adversary's utility as a trade-off between the benefit from violations and loss due to punishment when detected. Our Stackelberg game model generalizes an existing deployed security game model with punishment parameters. It applies to natural auditing settings with multiple auditors where each auditor is restricted to audit a subset of the potential violations. We provide novel polynomial time algorithms to approximate the non-convex optimization problem used to compute the Stackelberg equilibrium. The algorithms output optimal audit resource allocation strategy and punishment levels. We also provide a method to reduce the optimization problem size, achieving up to 5x speedup for realistic instances of the audit problem, and for the related security game instances.

In 1948 Besicovitch proved that an affine image of a regular hexagon may be inscribed into an arbitrary planar convex body. We prove Besicovitch's result using a variational approach based on special approximation by triangles and generalize the Besicovitch theorem to a certain new class of hexagons. We survey the results on the Banach-Mazur distance between different classes of convex bodies. We hope that our generalization of the Besicovitch theorem may become useful for estimation of the Banach-Mazur distance between planar convex bodies. We examined our special approximation by triangles in some specific cases, and it showed a noticeable improvement in comparison with known general methods. We also consider the Banach-Mazur distance between a simplex and an arbitrary convex body in the three-dimensional case. Using the idea of an inscribed simplex of maximal volume, we obtain a certain related algebraic optimization problem that provides an upper estimate.

In this thesis, algorithms for blind and non-blind motion deblurring of digital images are proposed. The non-blind algorithm is based on a convex program consisting of a data fitting term and a sparsity-promoting regularization term. The data fitting term is the squared l_2 norm of the residual between the blurred image and the latent image convolved with a known blur kernel. The regularization term is the l_1 norm of the latent image under a wavelet frame (framelet) decomposition. This convex program is solved with the first-order primal-dual algorithm proposed by Chambolle and Pock. The proposed blind deblurring algorithm is based on the work of Cai, Ji, Liu, and Shen. It works by embedding the proposed non-blind algorithm in an alternating minimization scheme and imposing additional constraints in order to deal with the challenging non-convex nature of the blind deblurring problem. Numerical experiments are performed on artificially and naturally blurred images, and both proposed algorithms are found to be competitive with recent deblurring methods. / Graduate / 0544 / tdanniels@gmail.com

A clique of a graph G is a set of pairwise adjacent nodes of G. Similarly, a biclique (U, V ) of a bipartite graph G is a pair of disjoint, independent vertex sets such that each node in U is adjacent to every node in V in G. We consider the problems of identifying the maximum clique of a graph, known as the maximum clique problem, and identifying the biclique (U, V ) of a bipartite graph that maximizes the product |U | · |V |, known as the maximum edge biclique problem. We show that finding a clique or biclique of a given size in a graph is equivalent to finding a rank one matrix satisfying a particular set of linear constraints. These problems can be formulated as rank minimization problems and relaxed to convex programming by replacing rank with its convex envelope, the nuclear norm. Both problems are NP-hard yet we show that our relaxation is exact in the case that the input graph contains a large clique or biclique plus additional nodes and edges. For each problem, we provide two analyses of when our relaxation is exact. In the first, the diversionary edges are added deterministically by an adversary. In the second, each potential edge is added to the graph independently at random with fixed probability p. In the random case, our bounds match the earlier bounds of Alon, Krivelevich, and Sudakov, as well as Feige and Krauthgamer for the maximum clique problem. We extend these results and techniques to the k-disjoint-clique problem. The maximum node k-disjoint-clique problem is to find a set of k disjoint cliques of a given input graph containing the maximum number of nodes. Given input graph G and nonnegative edge weights w, the maximum mean weight k-disjoint-clique problem seeks to identify the set of k disjoint cliques of G that maximizes the sum of the average weights of the edges, with respect to w, of the complete subgraphs of G induced by the cliques. These problems may be considered as a way to pose the clustering problem. In clustering, one wants to partition a given data set so that the data items in each partition or cluster are similar and the items in different clusters are dissimilar. For the graph G such that the set of nodes represents a given data set and any two nodes are adjacent if and only if the corresponding items are similar, clustering the data into k disjoint clusters is equivalent to partitioning G into k-disjoint cliques. Similarly, given a complete graph with nodes corresponding to a given data set and edge weights indicating similarity between each pair of items, the data may be clustered by solving the maximum mean weight k-disjoint-clique problem. We show that both instances of the k-disjoint-clique problem can be formulated as rank constrained optimization problems and relaxed to semidefinite programs using the nuclear norm relaxation of rank. We also show that when the input instance corresponds to a collection of k disjoint planted cliques plus additional edges and nodes, this semidefinite relaxation is exact for both problems. We provide theoretical bounds that guarantee exactness of our relaxation and provide empirical examples of successful applications of our algorithm to synthetic data sets, as well as data sets from clustering applications.