Polynomial Time Algorithms for Multi-Type Branching Processes and Stochastic Context-Free Grammars

We show that one can approximate the least fixed point solution for a multivariate system of monotone probabilistic polynomial equations in time polynomial in both the encoding size of the system of equations and in log(1/\epsilon), where \epsilon > 0 is the desired additive error bound of the solution. (The model of computation is the standard Turing machine model.) We use this result to resolve several open problems regarding the computational complexity of computing key quantities associated with some classic and heavily studied stochastic processes, including multi-type branching processes and stochastic context-free grammars

The complexity of analyzing infinite-state Markov chains, Markov decision processes, and stochastic games (Invited talk)

In recent years, a considerable amount of research has been devoted to understanding the computational complexity of basic analysis problems, and model checking problems, for finitely-presented countable infinite-state probabilistic systems. In particular, we have studied recursive Markov chains (RMCs), recursive Markov decision processes (RMDPs) and recursive stochastic games (RSGs). These arise by adding a natural recursion feature to finite-state Markov chains, MDPs, and stochastic games. RMCs and RMDPs provide natural abstract models of probabilistic procedural programs with recursion, and they are expressively equivalent to probabilistic and MDP extensions of pushdown automata. Moreover, a number of well-studied stochastic processes, including multi-type branching processes, (discrete-time) quasi-birth-death processes, and stochastic context-free grammars, can be suitably captured by subclasses of RMCs. A central computational problem for analyzing various classes of recursive probabilistic systems is the computation of their (optimal) termination probabilities. These form a key ingredient for many other analyses, including model checking. For RMCs, and for important subclasses of RMDPs and RSGs, computing their termination values is equivalent to computing the least fixed point (LFP) solution of a corresponding monotone system of polynomial (min/max) equations. The complexity of computing the LFP solution for such equation systems is a intriguing problem, with connections to several areas of research. The LFP solution may in general be irrational. So, one possible aim is to compute it to within a desired additive error epsilon > 0. For general RMCs, approximating their termination probability within any non-trivial constant additive error < 1/2, is at least as hard as long-standing open problems in the complexity of numerical computation which are not even known to be in NP. For several key subclasses of RMCs and RMDPs, computing their termination values turns out to be much more tractable. In this talk I will survey algorithms for, and discuss the computational complexity of, key analysis problems for classes of infinite-state recursive MCs, MDPs, and stochastic games. In particular, I will discuss recent joint work with Alistair Stewart and Mihalis Yannakakis (in papers that appeared at STOC\u2712 and ICALP\u2712), in which we have obtained polynomial time algorithms for computing, to within arbitrary desired precision, the LFP solution of probabilistic polynomial (min/max) systems of equations. Using this, we obtained the first P-time algorithms for computing (within desired precision) the extinction probabilities of multi-type branching processes, the probability that an arbitrary given stochastic context-free grammar generates a given string, and the optimum (maximum or minimum) extinction probabilities for branching MDPs and context-free MDPs. For branching MDPs, their corresponding equations amount to Bellman optimality equations for minimizing/maximizing their termination probabilities. Our algorithms combine variations and generalizations of Newton\u27s method with other techniques, including linear programming. The algorithms are fairly easy to implement, but analyzing their worst-case running time is mathematically quite involved

Equilibria, Fixed Points, and Complexity Classes

Many models from a variety of areas involve the computation of an equilibrium or fixed point of some kind. Examples include Nash equilibria in games; market equilibria; computing optimal strategies and the values of competitive games (stochastic and other games); stable configurations of neural networks; analysing basic stochastic models for evolution like branching processes and for language like stochastic context-free grammars; and models that incorporate the basic primitives of probability and recursion like recursive Markov chains. It is not known whether these problems can be solved in polynomial time. There are certain common computational principles underlying different types of equilibria, which are captured by the complexity classes PLS, PPAD, and FIXP. Representative complete problems for these classes are respectively, pure Nash equilibria in games where they are guaranteed to exist, (mixed) Nash equilibria in 2-player normal form games, and (mixed) Nash equilibria in normal form games with 3 (or more) players. This paper reviews the underlying computational principles and the corresponding classes

Polynomial Time Algorithms for Branching Markov Decision Processes and Probabilistic Min(Max) Polynomial Bellman Equations

We show that one can approximate the least fixed point solution for a multivariate system of monotone probabilistic max(min) polynomial equations, referred to as maxPPSs (and minPPSs, respectively), in time polynomial in both the encoding size of the system of equations and in log(1/epsilon), where epsilon > 0 is the desired additive error bound of the solution. (The model of computation is the standard Turing machine model.) We establish this result using a generalization of Newton's method which applies to maxPPSs and minPPSs, even though the underlying functions are only piecewise-differentiable. This generalizes our recent work which provided a P-time algorithm for purely probabilistic PPSs. These equations form the Bellman optimality equations for several important classes of infinite-state Markov Decision Processes (MDPs). Thus, as a corollary, we obtain the first polynomial time algorithms for computing to within arbitrary desired precision the optimal value vector for several classes of infinite-state MDPs which arise as extensions of classic, and heavily studied, purely stochastic processes. These include both the problem of maximizing and mininizing the termination (extinction) probability of multi-type branching MDPs, stochastic context-free MDPs, and 1-exit Recursive MDPs. Furthermore, we also show that we can compute in P-time an epsilon-optimal policy for both maximizing and minimizing branching, context-free, and 1-exit-Recursive MDPs, for any given desired epsilon > 0. This is despite the fact that actually computing optimal strategies is Sqrt-Sum-hard and PosSLP-hard in this setting. We also derive, as an easy consequence of these results, an FNP upper bound on the complexity of computing the value (within arbitrary desired precision) of branching simple stochastic games (BSSGs)

Efficient algorithms for infinite-state recursive stochastic models and Newton’s method

Some well-studied infinite-state stochastic models give rise to systems of nonlinear equations. These systems of equations have solutions that are probabilities, generally probabilities of termination in the model. We are interested in finding efficient, preferably polynomial time, algorithms for calculating probabilities associated with these models. The chief tool we use to solve systems of polynomial equations will be Newton’s method as suggested by [EY09]. The main contribution of this thesis is to the analysis of this and related algorithms. We give polynomial-time algorithms for calculating probabilities for broad classes of models for which none were known before. Stochastic models that give rise to such systems of equations include such classic and heavily-studied models as Multi-type Branching Processes, Stochastic Context- Free Grammars(SCFGs) and Quasi Birth-Death Processes. We also consider models that give rise to infinite-state Markov Decision Processes (MDPs) by giving algorithms for approximating optimal probabilities and finding policies that give probabilities close to the optimal probability, in several classes of infinite-state MDPs. Our algorithms for analysing infinite-state MDPs rely on a non-trivial generalization of Newton’s method that works for the max/min polynomial systems that arise as Bellman optimality equations in these models. For SCFGs, which are used in statistical natural language processing, in addition to approximating termination probabilities, we analyse algorithms for approximating the probability that a grammar produces a given string, or produces a string in a given regular language. In most cases, we show that we can calculate an approximation to the relevant probability in time polynomial in the size of the model and the number of bits of desired precision. We also consider more general systems of monotone polynomial equations. For such systems we cannot give a polynomial-time algorithm, which pre-existing hardness results render unlikely, but we can still give an algorithm with a complexity upper bound which is exponential only in some parameters that are likely to be bounded for the monotone polynomial equations that arise for many interesting stochastic models

Qualitative Multi-Objective Reachability for Ordered Branching MDPs

We study qualitative multi-objective reachability problems for Ordered Branching Markov Decision Processes (OBMDPs), or equivalently context-free MDPs, building on prior results for single-target reachability on Branching Markov Decision Processes (BMDPs). We provide two separate algorithms for "almost-sure" and "limit-sure" multi-target reachability for OBMDPs. Specifically, given an OBMDP, $\mathcal{A}$, given a starting non-terminal, and given a set of target non-terminals $K$ of size $k = |K|$, our first algorithm decides whether the supremum probability, of generating a tree that contains every target non-terminal in set $K$, is $1$. Our second algorithm decides whether there is a strategy for the player to almost-surely (with probability $1$) generate a tree that contains every target non-terminal in set $K$. The two separate algorithms are needed: we show that indeed, in this context, "almost-sure" $\not=$ "limit-sure" for multi-target reachability, meaning that there are OBMDPs for which the player may not have any strategy to achieve probability exactly $1$ of reaching all targets in set $K$ in the same generated tree, but may have a sequence of strategies that achieve probability arbitrarily close to $1$. Both algorithms run in time $2^{O(k)} \cdot |\mathcal{A}|^{O(1)}$, where $|\mathcal{A}|$ is the total bit encoding length of the given OBMDP, $\mathcal{A}$. Hence they run in polynomial time when $k$ is fixed, and are fixed-parameter tractable with respect to $k$. Moreover, we show that even the qualitative almost-sure (and limit-sure) multi-target reachability decision problem is in general NP-hard, when the size $k$ of the set $K$ of target non-terminals is not fixed.Comment: 47 page

Calibrating Generative Models: The Probabilistic Chomsky-Schützenberger Hierarchy

A probabilistic Chomsky–Schützenberger hierarchy of grammars is introduced and studied, with the aim of understanding the expressive power of generative models. We offer characterizations of the distributions definable at each level of the hierarchy, including probabilistic regular, context-free, (linear) indexed, context-sensitive, and unrestricted grammars, each corresponding to familiar probabilistic machine classes. Special attention is given to distributions on (unary notations for) positive integers. Unlike in the classical case where the "semi-linear" languages all collapse into the regular languages, using analytic tools adapted from the classical setting we show there is no collapse in the probabilistic hierarchy: more distributions become definable at each level. We also address related issues such as closure under probabilistic conditioning