A multiplicative weights update algorithm for MINLP
نویسندگان
چکیده
We discuss an application of the well-known Multiplicative Weights Update (MWU) algorithm to non-convex and mixed-integer nonlinear programming. We present applications to: (a) the distance geometry problem, which arises in the positioning of mobile sensors and in protein conformation; (b) a hydro unit commitment problem arising in the energy industry, and (c) a class of Markowitz’ portfolio selection problems. It turns out that, on top of giving a relative approximation guarantee, the MWU is competitive with a simple Multi-Start algorithm, specially on problems exhibiting many nonconvex terms.
منابع مشابه
The Multiplicative Weights Update Method: a Meta-Algorithm and Applications
Algorithms in varied fields use the idea of maintaining a distribution over a certain set and use the multiplicative update rule to iteratively change these weights. Their analysis are usually very similar and rely on an exponential potential function. We present a simple meta algorithm that unifies these disparate algorithms and drives them as simple instantiations of the meta algorithm.
متن کاملThe Multiplicative Weights Update Method: A Meta Algorithm and its Applications
Algorithms in varied fields use the idea of maintaining a distribution over a certain set and use the multiplicative update rule to iteratively change these weights. Their analyses are usually very similar and rely on an exponential potential function. In this survey we present a simple meta algorithm that unifies these disparate algorithms and drives them as simple instantiations of the meta a...
متن کاملLearning Linear Functions with Quadratic and Linear Multiplicative Updates
We analyze variations of multiplicative updates for learning linear functions online. These can be described as substituting exponentiation in the Exponentiated Gradient (EG) algorithm with quadratic and linear functions. Both kinds of updates substitute exponentiation with simpler operations and reduce dependence on the parameter that specifies the sum of the weights during learning. In partic...
متن کاملOn Sex, Evolution, and the Multiplicative Weights Update Algorithm
We consider a recent innovative theory by Chastain et al. on the role of sex in evolution [10]. In short, the theory suggests that the evolutionary process of gene recombination implements the celebrated multiplicative weights updates algorithm (MWUA). They prove that the population dynamics induced by sexual reproduction can be precisely modeled by genes that use MWUA as their learning strateg...
متن کاملPerformance of Multiplicative-weights-updates
Let us remember the mechanics of Multiplicative-Weights-Updates: At every time t, the learner maintains a weight vector wt ≥ 0 over the experts. Given the weight vector, the probability distribution over the experts is computed as pt = wt wt·1 . The weights are initialized at w1 = 1 n · 1. (Multiplicative-weights-update step.) Given the loss vector at time t the weights are updated as follows w...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- EURO J. Computational Optimization
دوره 5 شماره
صفحات -
تاریخ انتشار 2017