How do these approximation methods compare in terms of accuracy and computational efficiency?5 answersRational approximation and neural network-based approaches are efficient for function approximation. Rational approximation outperforms neural networks with the same decision variables, showing superior accuracy and computational efficiency, especially in low-dimensional optimization problems. On the other hand, meshless approximation methods, combining scattered and regular nodes, reduce computational complexity without significantly impacting solution accuracy. Additionally, approximate computing techniques like mixed precision, perforation, and relaxed synchronization offer higher performance and energy efficiency by accepting a controlled loss in accuracy, with error rates typically below 10% in various applications. These methods provide a spectrum of trade-offs between accuracy and computational efficiency, catering to different needs in diverse computational tasks.
How can an accurate exponential based Q-function approximation be achieved?4 answersAn accurate exponential-based Q-function approximation can be achieved by using various techniques proposed in the literature. One approach is to use Fejer's Rule of interpolatory integration in a modified manner, which provides a very generic, tractable, and extremely accurate approximation for the Gaussian Q function. Another method is to approximate the Q-function using one exponential function or the sum of two exponential functions, which have simple analytical forms and are suitable for further derivation of expressions in closed forms. Additionally, a new 4-term exponential quadratic approximator (EQA) for the error function can be used, which minimizes a sum-of-squares cost function with spline-type constraints and leads to an analytical formula for the key integral. These approaches offer accurate and efficient ways to approximate the Q-function for various applications in communication and machine learning systems.
How to do approximate query processing?5 answersApproximate query processing (AQP) can be done by creating compact representations of original tuples called bubbles. These bubbles are tunable in terms of compactness and statistics granularity, allowing for a trade-off between accuracy and performance. A first working solution for representing bubbles is through Bayesian networks and unsupervised deep learning models. Another approach is to use synopses and buffer them in a warehouse to reduce subsequent query execution costs. Adaptive AQP (AAQP) predicts future workloads and generates the best set of synopses to minimize execution time. AAQP utilizes Recurrent Neural Networks (RNN) trained with end-user sessions from historical workloads. Experimental evaluations show the effectiveness of AAQP in predicting future workloads and minimizing execution time.
When a hole at x=1 to write a rational function ??4 answersWhen writing a rational function with a hole at x=1, the location of the hole can be determined by the trivial zeros of Dirichlet L-functions. The Dirichlet L-function L(s, X) has trivial zeros at s = 0, -2, -4, -6,... when x(-1) = 1, and at s = -1, -3, -5, -7,... when x(-1) = -1. This result applies to all Dirichlet characters, primitive or not, and does not require the functional equation for L-functions. The representation k-1 L(s, X) = k-s E X(r)D(s, r/k) r=1 can be used to determine the location of the hole.
What are the main conclusions about rational functions?5 answersRational functions can be defined by string transducers and can also be captured by deterministic string transducers with lookahead. The class of rational functions can be learned in the limit with polynomial time and data when represented by string transducers with lookahead in the diagonal-minimal normal form. Noncommutative rational functions have applications in system theory and formal languages, and they first appeared in the context of rational and recognizable formal power series in noncommuting variables. Rational functions are the ratio of two complex polynomials without common roots, and they belong to the same class if one can be obtained from the other by postcomposition with a linear-fractional transformation. The number of classes of rational functions with a given degree and critical points can be calculated using the irreducible sl(2) representations and the orbits of critical points.
What is an approximation algorithm?2 answersخوارزمية التقريب هي طريقة تستخدم لإيجاد حل تقريبي لمشكلة عندما يكون العثور على الحل الدقيق غير ممكن من الناحية الحسابية. يتضمن إنشاء تقدير تقريبي للحل المطلوب يكون قريبًا بما يكفي ليتم اعتباره مقبولًا. غالبًا ما تستخدم هذه التقديرات التقريبية لمزيد من التقييمات أو التطبيقات. على سبيل المثال، في سياق مشاكل التحسين متعددة الأهداف، تتمثل الطريقة الشائعة في حساب تقريب للمجموعة غير المهيمنة باستخدام تقنيات مثل إنشاء متعدد السطوح أو تغطية صندوقية للمجموعة. في سياق العثور على المعلمات المعمارية المثلى للشبكات العصبية العميقة، يتم استخدام خوارزمية تقريب مع خطأ تقريبي للعثور على المجموعة المثلى من المعلمات. وبالمثل، في تصميم المنتج المعياري، تُستخدم خوارزمية التقريب متعدد الحدود لتعظيم الوحدات النمطية عن طريق تقسيم المنتج إلى وحدات. في سياق تقريب الموتر المتعامد منخفض الرتبة، تم إدخال خوارزمية تقريب معدلة لإنشاء حد أدنى تقريبي. أخيرًا، في سياق تقليل قدرة التسرب في الدوائر المتكاملة، يتم استخدام خوارزمية التقريب الثنائي الأولي لتعيين جهد عتبة البوابة وتقليل استهلاك الطاقة.