We study a financial model with one risk-free and one risky asset subject to liquidity risk and price impact. In this market, an investor may transfer funds between the two assets at any discrete time. Each purchase or sale policy decision affects the price of the risky asset and incurs some fixed transaction cost. The objective is to maximize the expected utility from terminal liquidation valu...

In the paper we consider the chance-constrained version of an affinely perturbed linear matrix inequality (LMI) constraint, assuming the primitive perturbations to be independent with light-tail distributions (e.g., bounded or Gaussian). Constraints of this type, playing a central role in chance-constrained linear/conic quadratic/semidefinite programming, are typically computationally intractab...

This paper presents the research work addressing optimal secure and protected Multicasting in wired and wireless Hierarchical Sensor Networks (HSN). The multicast nodes in a hierarchical set up are associated with” Importance values” that are normalized into probabilities. The security constraint imposed is associated with the concept of “Prefix-free paths” in the associated graph. The optimali...

In decision making problems where uncertainty plays a key role and decisions have to be taken prior to observing uncertainty, chance constraints are a strong modelling tool for defining safety of decisions. These constraints request that a random inequality system depending on a decision vector has to be satisfied with a high probability. The characteristics of the feasible set of such chance c...

Many clustering applications use the computationally efficient non-hierarchical clustering techniques such as k-means. However, less efficient hierarchical clustering is desirable as by creating a dendrogram the user can choose an appropriate value of k (the number of clusters) and in some domains cluster hierarchies (i.e. clusters within other clusters) naturally exist. In many situations apri...

We present a Bayesian CAD modeler for robotic applications. We address the problem of taking into account the propagation of geometric uncertainties when solving inverse geometric problems. The proposed method may be seen as a generalization of constraint-based approaches in which we explicitly model geometric uncertainties. Using our methodology, a geometric constraint is expressed as a probab...

For an inequality system defined by a possibly infinite family of proper functions (not necessarily lower semicontinuous), we introduce some new notions of constraint qualifications in terms of the epigraphs of the conjugates of these functions. Under the new constraint qualifications, we obtain characterizations of those reverse-convex inequalities which are consequence of the constrained syst...

The purpose of this work is the development of a fully monolithic solution algorithm for quasi-static phase-field fracture propagation. Phase-field fracture consists of two coupled partial differential equations and it is well known that the underlying energy functional is non-convex and sophisticated algorithms are required. For the incremental, spatially-discretized problem, an adaptive error...

Semi-supervised clustering of images has been an interesting problem for machine learning and computer vision researchers for decades. Pairwise constrained clustering is a popular paradigm for semi supervision that uses knowledge about whether two images belong to the same category (must-link constraint) or not (can’t-link constraint). Performance of constrained clustering algorithms can be imp...

In this paper, we present a novel constrained variational principle for simultaneous smoothing and estimation of the diffusion tensor field from diffusion weighted imaging (DWI). The constrained variational principle involves the minimization of a regularization term in an LP norm, subject to a nonlinear inequality constraint on the data. The data term we employ is the original Stejskal-Tanner ...