Bandit convex optimization is a special case of online convex optimization with partial information. In this setting, a player attempts to minimize a sequence of adversarially gen...
We consider the convex optimization problem minx{f(x) : gj(x) ≤ 0, j = 1, . . . , m} where f is convex, the feasible set K is convex and Slater’s condition holds, but the funct...
Convex optimization problems arising in applications, possibly as approximations of intractable problems, are often structured and large scale. When the data are noisy, it is of i...
Abstract. There is a natural norm associated with a starting point of the homogeneous selfdual (HSD) embedding model for conic convex optimization. In this norm two measures of the...
Separable convex optimization problems with linear ascending inequality and equality constraints are addressed in this paper. An algorithm that explicitly characterizes the optimum...
In this paper we consider the issue of energy efficiency in random access networks and show that optimizing transmission probabilities of nodes can enhance network performance in t...
Amir Mahdi Khodaian, Babak Hossein Khalaj, Mohamma...
This paper reviews the recent surge of interest in convex optimization in a context of pattern recognition and machine learning. The main thesis of this paper is that the design of...
Kristiaan Pelckmans, Johan A. K. Suykens, Bart De ...
We study recommendations in applications where there are temporal patterns in the way items are consumed or watched. For example, a student who has taken the Advanced Algorithms c...
Aditya G. Parameswaran, Georgia Koutrika, Benjamin...
— For wireless cellular and ad hoc networks with QoS constraints, we propose a suite of problem formulations that allocate network resources to optimize SIR, maximize throughput ...
David Julian, Mung Chiang, Daniel O'Neill, Stephen...
In a wide variety of imaging applications (especially medical imaging), we obtain a partial set or subset of the Fourier transform of an image. From these Fourier measurements, we...