  Solving nonlinear SMT problems over real numbers has wide applications in
robotics and AI. While significant progress is made in solving quantifier-free
SMT formulas in the domain, quantified formulas have been much less
investigated. We propose the first delta-complete algorithm for solving
satisfiability of nonlinear SMT over real numbers with universal quantification
and a wide range of nonlinear functions. Our methods combine ideas from
counterexample-guided synthesis, interval constraint propagation, and local
optimization. In particular, we show how special care is required in handling
the interleaving of numerical and symbolic reasoning to ensure
delta-completeness. In experiments, we show that the proposed algorithms can
handle many new problems beyond the reach of existing SMT solvers.
