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A strategy choosing an action from the current full game state rather than from the entire prior history. In a finite reachability game, the first-entry rank in the winning fixed-point iteration gives such a strategy by choosing a successor of smaller rank. The state must include all information affecting legal moves, such as ownership of reversible assignments and the player to move.

Solve the finite state graph by winning-set fixed-point iteration, treating positions outside both winning regions as draws. A naive recursive minimax search over histories may run forever around a cycle. Store each ownership-sensitive state once, with a player-to-move flag and all legal successors.

Let $T_A$ and $T_B$ be terminal states where the propositional formula is true and false, respectively. Initialize $W_A^0=T_A$. Repeatedly add nonterminal positions as follows:For $W_B$, start at $T_B$ and exchange the players in the existential and universal conditions. Terminal states keep their original labels and are excluded from these predecessor rules, avoiding a vacuous all-successors test at a terminal loss. Each process stabilizes after at most $|V|$ strict growth steps; predecessor counters implement both regions via the winning-position attractor in an infinite game construction in $O(|V|+|E|)$ graph time.

The admission round supplies a decreasing rank. At a state controlled by the winning player, choose a successor of smaller rank; at an opposing state, every successor already has smaller rank. Hence these strategies force a finite terminal win. The two winning regions are disjoint. Outside both, each player can keep the play outside the opponent's winning region, so neither can force a terminal win against best defence. Under the convention in (h), assign those remaining positions value $0$; the other regions have values $+1$ and $-1$.

This is a finite reachability game calculation, with memoryless strategies read from successor choices. Merely calling the first back-edge encountered a draw is unsound: a cycle can have an exit through which one player can force a win.

The fixed-point iteration stays inside $[0,7]$ because $G$ maps that interval to itself. A fixed point satisfies $\xi =\sqrt{2+\xi }$, so $\xi \ge 0$ andOnly $\xi =2$ is admissible; $-1$ fails the unsquared equation. With $q=1/(2\sqrt{2})$, the mean value theorem gives the error boundInduction yields $|x_k-2|\le q^k|x_0-2|\le 5q^k\to 0$, since $0<q<1$. Therefore, for every allowed starting value,This estimate proves convergence directly, without assuming in advance that the sequence has a limit.

Set $H(x)=g(x)-x$. This function is continuous on $[a,b]$. Since $g$ takes values in $[a,b]$,The intermediate value theorem therefore supplies $\xi \in [a,b]$ with $H(\xi )=0$, equivalently $g(\xi )=\xi$. This includes the possibility that one endpoint is already a fixed point.

For uniqueness, the mean value theorem gives, for any $x,y\in [a,b]$,If both $\xi$ and $\eta$ are fixed points, thenSince $\gamma <1$, this forces $|\xi -\eta |=0$. Thus there exists exactly one fixed point. When $a=b$, the conclusion follows immediately from $g(a)=a$; otherwise the argument above applies. The contraction mapping estimate also proves convergence of the associated fixed-point iteration, as used next.

The polynomial factors as $p(x)=(3x-2{)}^2(3x+1)$, so $\alpha =2/3$ is a multiple root of multiplicity two. The Newton root-finding iteration isFor $x\ne 0,2/3$, cancelling a factor givesThis expression extends continuously to $g(2/3)=2/3$. Let $I=[1/3,1]$. On this closed interval,Thus $g$ is increasing, andBy the mean value theorem, $|g(x)-g(y)|\le (16/27)|x-y|$ for all $x,y\in I$. The contraction mapping theorem states that a contraction mapping from a nonempty complete metric space into itself has a unique fixed point, and every fixed-point iteration converges to it. The closed interval $I$ with ordinary Euclidean distance is complete, and $g(2/3)=2/3$, soIn fact $|x_k-2/3|\le (16/27{)}^k|x_0-2/3|$. The Newton iteration at a multiple root here has linear convergence, since $g^{\prime }(2/3)=1/2$. Thus the convergence assertion is true for the simplified fixed-point iteration, and for the usual Newton root-finding iteration that stops upon reaching an exact root of a polynomial. If one insists on applying the unsimplified quotient at every step, it is undefined for the allowed starting value $x_0=2/3$, since both $p$ and $p^{\prime }$ vanish there; that literal version of an infinite sequence requires the stated stopping convention or continuous extension.

Rearrange the polynomial equation as a fixed point equation:First establish existence and uniqueness of the target root of a polynomial. Since $f$ is a continuous function, $f(0)=-1<0$ and $f(1/7)=1/7^5>0$, the intermediate value theorem gives a root of a polynomial $\xi \in (0,1/7)$. Moreover $f^{\prime }(x)=5x^4+7>0$ for all real $x$, so $f$ is strictly increasing and this is its only real root of a polynomial.

For $x\in [0,1/7]$,so $g$ maps the closed interval into itself. AlsoThe mean value theorem therefore gives $|g(x)-g(y)|\le q|x-y|$ on the closed interval: $g$ is a contraction mapping. For any starting value $x_0\in [0,1/7]$, all iterates remain in that closed interval, and since $g(\xi )=\xi$,Thus the fixed-point iteration converges to the unique real root of a polynomial from every $x_0\in [0,1/7]$. This explicit error estimate proves the needed convergence directly.

For iterates approaching ${\mathbf{x}}_{\ast }$, write $e_k=\Vert {\mathbf{x}}_k-{\mathbf{x}}_{\ast }\Vert$. Exact order of convergence $p$ means $e_{k+1}/e_k^p\to C$ for a finite constant $C>0$, with nonzero errors. Thus quadratic convergence has $p=2$, and cubic convergence has $p=3$. The weaker eventual bound $e_{k+1}\le C{e}_k^p$ means at least order $p$. Finite termination is a separate case.^[ref]

The assertion about being always faster is false. For example, the fixed-point iterations with errors $e_{k+1}=e_k^2$ and $d_{k+1}=50d_k^3$ have exact quadratic convergence and cubic convergence, respectively. Starting both at $0.1$, their next errors are $0.01$ and $0.05$: the cubic convergence method is worse on that step, although both converge. Error constants, starting points, and work per step all matter. The higher order of convergence describes asymptotic behavior within a convergence region, not a uniform comparison of all computations.

Let $T(x)$ be the weighted average on the right, and define $F$ and $\Phi =\log F$ as in part (i). Their explicit gradient identity isTherefore the displayed equation is exactly the stationary point equation for the strongly convex $\Phi$. It has one solution, which is the unique minimizer from part (i). Reuse the BFGS iteration, withThe gradient is explicit, the weights can again be scaled by their largest exponent, and the usual full-memory BFGS assumptions give local superlinear convergence.

Simply setting $x_{n+1}=T(x_n)$ is not automatically a convergent fixed-point iteration. Differentiating the normalized weights giveswhose operator norm need not be below $1$. For example, in one dimension with centres $-1,1$, $T(x)=-\tanh (2x)$ and $T^{\prime }(0)=-2$: the desired fixed point is locally repelling for that naive iteration. If exact second derivatives are affordable, multivariable Newton iteration on $x-T(x)=0$ is another valid choice, with Jacobian matrix $I-J_T$ as given above and local quadratic convergence.