Bahman Kalantari

Bahman Kalantari

We present new iterative algorithms for solving a square linear system $Ax=b$ in dimension $n$ by employing the {\it Triangle Algorithm} \cite{kal12}, a fully polynomial-time approximation scheme for testing if the convex hull of a finite set of points in a Euclidean space contains a given point. By converting $Ax=b$ into a convex hull problem and solving via the Triangle Algorithm, together with a {\it sensitivity theorem}, we compute in $O(n^2ε^{-2})$ arithmetic operations an approximate solution satisfying $\Vert Ax_ε- b \Vert \leq ερ$, where $ρ= \max \{\Vert a_1 \Vert,..., \Vert a_n \Vert, \Vert b \Vert \}$, and $a_i$ is the $i$-th column of $A$. In another approach we apply the Triangle Algorithm incrementally, solving a sequence of convex hull problems while repeatedly employing a {\it distance duality}. The simplicity and theoretical complexity bounds of the proposed algorithms, requiring no structural restrictions on the matrix $A$, suggest their potential practicality, offering alternatives to the existing exact and iterative methods, especially for large scale linear systems. The assessment of computational performance however is the subject of future experimentations.

Bahman Kalantari

An important quantity associated with a complex polynomial $p(z)$ is $\Vert p \Vert_\infty$, the maximum of its modulus over the unit disc $D$. We prove, $z_* \in D$ is a local maximum of $|p(z)|$ if and only if $a_*$ satisfies, $z_*=p(z_*)|p'(z_*)|/p'(z_*)|p(z_*)|$, i.e. it is proportional to its corresponding Newton direction. This explicit formula gives rise to novel iterative algorithms for computing $\Vert p \Vert_\infty$. We describe two such algorithms, including a Newton-like method and present some visualization of their performance.

Terence Coelho, Bahman Kalantari

We prove a complex polynomial of degree $n$ has at most $\lceil n/2 \rceil$ attractive fixed points lying on a line. We also consider the general case.

Bahman Kalantari

Many proofs of the fundamental theorem of algebra rely on the fact that the minimum of the modulus of a complex polynomial over the complex plane is attained at some complex number. The proof then follows by arguing the minimum value is zero. This can be done by proving that at any complex number that is not a zero of the polynomial we can exhibit a direction of descent for the modulus. In this note we present a very short and simple proof of the existence of such descent direction. In particular, our descent direction gives rise to Newton's method for solving a polynomial equation via modulus minimization and also makes the iterates definable at any critical point.

Fedor Andreev, Bahman Kalantari

Solving a quadratic equation $P(x)=ax^2+bx+c=0$ with real coefficients is known to middle school students. Solving the equation over the quaternions is not straightforward. Huang and So \cite{Huang} give a complete set of formulas, breaking it into several cases depending on the coefficients. From a result of the second author in \cite{kalQ}, zeros of $P(x)$ can be expressed in terms of the zeros of a real quartic equation. This drastically simplifies solving a quadratic equation. Here we also consider solving $P(x)=0$ iteratively via Newton and Halley methods developed in \cite{kalQ}. We prove a property of the Jacobian of Newton and Halley methods and describe several 2D polynomiography based on these methods. The images not only encode the outcome of the iterative process, but by measuring the time taken to render them we find the relative speed of convergence for the methods.

Bahman Kalantari

The subject of Polynomiography deals with algorithmic visualization of polynomial equations, having many applications in STEM and art, see [Kal04]. Here we consider the polynomiography of the partial sums of the exponential series. While the exponential function is taught in standard calculus courses, it is unlikely that properties of zeros of its partial sums are considered in such courses, let alone their visualization as science or art. The Monthly article Zemyan discusses some mathematical properties of these zeros. Here we exhibit some fractal and non-fractal polynomiographs of the partial sums while also presenting a brief introduction of the underlying concepts. Polynomiography establishes a different kind of appreciation of the significance of polynomials in STEM, as well as in art. It helps in the teaching of various topics at diverse levels. It also leads to new discoveries on polynomials and inspires new applications. We also present a link for the educator to get access to a demo polynomiography software together with a module that helps teach basic topics to middle and high school students, as well as undergraduates.

Bahman Kalantari, Eric Lee

We introduce a new iterative root-finding method for complex polynomials, dubbed {\it Newton-Ellipsoid} method. It is inspired by the Ellipsoid method, a classical method in optimization, and a property of Newton's Method derived in \cite{kalFTA}, according to which at each complex number a half-space can be found containing a root. Newton-Ellipsoid method combines this property, bounds on zeros, together with the plane-cutting properties of the Ellipsoid Method. We present computational results for several examples, as well as corresponding polynomiography. Polynomiography refers to algorithmic visualization of root-finding. Newton's method is the first member of the infinite family of iterations, the {\it basic family}. We also consider general versions of this ellipsoid approach where Newton's method is replaced by a higher-order member of the family such as Halley's method.