MIT researchers developed a new approach that rethinks how a classic method, known as Bayesian optimization, can be used to solve problems with hundreds of variables. In tests on realistic engineering-style benchmarks, like power-system optimization, the approach found top solutions 10 to 100 times faster than widely used methods.
Their technique leverages a foundation model trained on tabular data that automatically identifies the variables that matter most for improving performance, repeating the process to hone in on better and better solutions. The researchers’ tabular foundation model does not need to be constantly retrained as it works toward a solution, increasing the efficiency of the optimization process.
The technique also delivers greater speedups for more complicated problems, so it could be especially useful in demanding applications like materials development or drug discovery. The research will be presented at the International Conference on Learning Representations.
Their technique leverages a foundation model trained on tabular data that automatically identifies the variables that matter most for improving performance, repeating the process to hone in on better and better solutions. The researchers’ tabular foundation model does not need to be constantly retrained as it works toward a solution, increasing the efficiency of the optimization process.
The technique also delivers greater speedups for more complicated problems, so it could be especially useful in demanding applications like materials development or drug discovery. The research will be presented at the International Conference on Learning Representations.
Researchers have refined the simplex method, a key algorithm for optimization, proving it can't be improved further and providing theoretical reasons for its efficiency.
A new paper demonstrates that the simplex method, a widely used optimization algorithm, is as efficient as it can be, and explains why it performs well in practice despite theoretical limitations.
This discussion explores the effectiveness of simulated annealing compared to random search for optimizing a set of 16 integer parameters. The author seeks to determine if simulated annealing provides a significant advantage over random search, despite the parameter space being too large for exhaustive search. Responses suggest plotting performance over time and highlight the ability of simulated annealing to escape local optima as its main strength.
