Our approach introduces the first general Learning-to-Optimize (L2O) framework designed for Mixed-Integer Nonlinear Programming (MINLP). As illustrated above, the approach consists of two core ...

Abstract: Optimization methods for long-horizon, dynamically feasible motion planning in robotics tackle challenging nonconvex and discontinuous optimization problems. Traditional methods often falter ...

Learn how to solve problems using linear programming. A linear programming problem involves finding the maximum or minimum value of an equation, called the objective functions, subject to a system of ...

Abstract: The Optimal Meter Placement (OMP) problem is traditionally addressed using heuristic algorithms or nonlinear programming, which often face challenges regarding solution reproducibility and ...

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...

Question 1: A Sudoku Solver We start by creating a Soduku solver that uses the same input and output format that we used in the first project. The first difference concerning the first project is that ...

I'm a professor in the Machine Learning Department at Carnegie Mellon. I am also affiliated with the Robotics Institute. I'm interested in multi-agent planning, reinforcement learning, ...