Sampling Can Be Faster Than Optimization (1811.08413v2)

Abstract: Optimization algorithms and Monte Carlo sampling algorithms have provided the computational foundations for the rapid growth in applications of statistical machine learning in recent years. There is, however, limited theoretical understanding of the relationships between these two kinds of methodology, and limited understanding of relative strengths and weaknesses. Moreover, existing results have been obtained primarily in the setting of convex functions (for optimization) and log-concave functions (for sampling). In this setting, where local properties determine global properties, optimization algorithms are unsurprisingly more efficient computationally than sampling algorithms. We instead examine a class of nonconvex objective functions that arise in mixture modeling and multi-stable systems. In this nonconvex setting, we find that the computational complexity of sampling algorithms scales linearly with the model dimension while that of optimization algorithms scales exponentially.

Sampling Can Be Faster Than Optimization

The paper, "Sampling Can Be Faster Than Optimization," presents a detailed exploration of the computational complexities associated with Monte Carlo sampling algorithms and optimization algorithms in machine learning applications. Typically, the theoretical understanding of these methodologies has been limited to specific function settings like convex functions in optimization and log-concave functions in sampling. These settings traditionally favor optimization due to their efficient computational scalability. The authors propose that in nonconvex settings tied to mixture modeling and multi-stable systems, sampling algorithms can outperform optimization algorithms in terms of scalability with respect to model dimension.

Key Findings

• Nonconvex Function Framework: The paper discusses how certain nonconvex objective functions, particularly arising in Bayesian mixture models and statistical physics systems, can reverse the usual computational advantage held by optimization algorithms over sampling algorithms. For these nonconvex functions, the computational complexity of sampling algorithms scales linearly, while optimization scales exponentially with the dimensionality of the model.
• Polynomial Convergence in MCMC: The paper demonstrates that within nonconvex settings, the convergence rates of MCMC algorithms remain polynomially dependent on dimensionality, a stark contrast to the exponential behavior in optimization. MCMC methods achieve $\epsilon$ accuracy within $\widetilde{\mathcal{O}\left({d}/{\epsilon}\right)}$ or $$\widetilde{\mathcal{O}\left(d^2 \ln\left(1/\epsilon\right)\right)}$$ iterations, whereas optimization requires $$\widetilde{\Omega} \left( ({1}/{\epsilon})^d \right)$$ iterations.
• Log-Sobolev Inequality for Sampling: The authors introduced the application of a log-Sobolev inequality to derive sharp convergence rates for sampling processes by leveraging a weighted Sobolev space. The log-Sobolev constant for nonconvex functions helped establish that the global convergence in sampling is largely controlled by areas with substantial probability mass, unlike optimization driven by local function behavior.
• Optimization Complexity: The paper exemplifies why global minima detection is particularly challenging in nonconvex problems using a combinatorial argument rooted in mixture modeling. It is shown that optimization algorithms face a prohibitive exponential complexity barrier due to the many potential local minima induced by small regions of attraction.

Practical Implications

The insights and results from this work impact a wide range of practical domains beyond mixture modeling and statistical physics. Specifically, they could influence how machine learning practitioners approach inference tasks involving high-dimensional data, where accuracy and computational efficiency are paramount. By understanding the strengths of sampling in nonconvex scenarios, machine learning techniques may shift more towards these sampling techniques in complex, large-scale environments.

Future Directions

The findings suggest several potential avenues for research into the theoretical underpinnings of sampling and optimization:

• Exploration of General Nonconvex Settings: Further investigations into settings besides mixture models can reveal additional classes of problems where sampling may be advantageous.
• Algorithmic Lower Bounds: Developing lower bounds for MCMC algorithms analogous to those established for optimization algorithms could refine the computational understanding of sampling algorithms.
• Extension to Other Sampling Techniques: The framework established by the use of weighted Sobolev spaces could be applied to optimize and explore other sampling techniques beyond those discussed in the paper.

This research underscores the nontrivial relationship between sampling and optimization in nonconvex settings, challenging some traditional assumptions about algorithmic efficiency in machine learning and suggesting that under particular circumstances, MCMC sampling strategies offer feasible alternatives to conventional optimization approaches.