Adaptive solution prediction for combinatorial optimization

Using Prediction to Improve Combinatorial Optimization Search

This paper describes a statistical approach to improving the performance of stochastic search algorithms for optimization. Given a search algorithmA, we learn to predict the outcome of A as a function of state features along a search trajectory. Predictions are made by a function approximator such as global or locally-weighted polynomial regression; training data is collected by Monte-Carlo sim...

Considering Stochastic and Combinatorial Optimization

Here, issues connected with characteristic stochastic practices are considered. In the first part, the plausibility of covering the arrangements of an improvement issue on subjective subgraphs is studied. The impulse for this strategy is a state where an advancement issue must be settled as often as possible for discretionary illustrations. Then, a preprocessing stage is considered that would q...

An Optimization-Based Framework for Combinatorial Prediction Market Design

We build on ideas from convex optimization to create a general framework for the design of efficient prediction markets over very large outcome spaces.

Accelerating Optimization via Adaptive Prediction

We present a powerful general framework for designing data-dependent optimization algorithms, building upon and unifying recent techniques in adaptive regularization, optimistic gradient predictions, and problemdependent randomization. We first present a series of new regret guarantees that hold at any time and under very minimal assumptions, and then show how different relaxations recover exis...

Adaptive Penalty Methods for Genetic Optimization of Constrained Combinatorial Problems