We investigate the stochastic optimization problem of minimizing population risk, where loss defining risk is assumed to be weakly convex. Compositions Lipschitz convex functions with smooth maps are primary examples such losses. analyze estimation quality nonsmooth and nonconvex problems by their sample average approximations. Our main results establish dimension-dependent rates on subgradient...

We study a properly convex real projective manifold with (possibly empty) compact, strictly convex boundary, and which consists of a compact part plus finitely many convex ends. We extend a theorem of Koszul which asserts that for a compact manifold without boundary the holonomies of properly convex structures form an open subset of the representation variety. We also give a relative version fo...

Robinson introduced a quotient space of pairs unbounded convex sets which share their recession cone. In this paper minimal sets, i.e., representations elements Robinson's spaces, are investigated. The fact that pair having property translation is reduced proved. the case two-dimensional formula for finding an equivalent given, criterion minimality presented, reducibility all proved, and nontri...

We introduce a convex optimization modeling framework that transforms a convex optimization problem expressed in a form natural and convenient for the user into an equivalent cone program in a way that preserves fast linear transforms in the original problem. By representing linear functions in the transformation process not as matrices, but as graphs that encode composition of linear operators...

Some fixed point convergence properties are proved for compact and demicompact maps acting over closed, bounded and convex subsets of a real Hilbert space. We also show that for a generalized nonexpansive mapping in a uniformly convex Banach space the Ishikawa iterates converge to a fixed point. Finally, a convergence type result is established for multivalued contractive mappings acting on clo...

In this paper, we consider a type of cone-constrained convex program in finitedimensional space, and are interested in characterization of the solution set of this convex program with the help of the Lagrange multiplier. We establish necessary conditions for a feasible point being an optimal solution. Moreover, some necessary conditions and sufficient conditions are established which simplifies...

We here extend the well known Positive Real Lemma (also known as the Kalman-Yakubovich-Popov Lemma) to complex matrix-valued generalized positive rational function, when non-minimal realizations are considered. All state space realizations are partitioned into subsets, each is identified with a set of matrices satisfying the same Lyapunov inclusion. Thus, each subset forms a convex invertible c...

this paper addresses the problem of testing simple hypotheses about the mean of a bivariate normal distribution with identity covariance matrix against restricted alternatives. the lrts and their power functions for such types of hypotheses are derived. furthermore, through some elementary calculus, it is shown that the power function of the lrt satisfies certain monotonicity and symmetry prope...