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<title>sageSLZ Notes</title> |
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</head>
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<body>
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<h1>sageSLZ Notes</h1> |
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<h2>The approximation interval problem</h2> |
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<p>When Sollya computes an approximation polynomial for a given function,
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degree and range, it also returns the approximation error (refered to, in the sequel, |
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as the |
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<span class="langElem">computedError</span> and the center of |
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the interval as well). |
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</p>
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<p>Very often, we want to solve a slightly different problem: for a given
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function, degree, lower bound and approximation error (referred to, in the |
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sequel, as the |
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<span class="langElem">targetError</span> ), compute the |
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approximation polynomial and the upper bound of the interval comprised between |
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it and the lower bound for which the approximation error stands. |
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</p>
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<p>The idea is to break a given initially “large” interval into a |
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collection of smaller intervals because for the given function-degree-error the |
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initial interval is too large.</p>
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<p>The most naive approach is to:</p> |
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<ol>
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<li>give it a try with the initial data (a very probably too high upper bound
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for the given <span class="langElem">targetError</span>);</li> |
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<li>when you get a too large error, reduce (e. g. divide by 2) the interval by
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lowering the upper bound until the computed error matches the target |
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error;</li>
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<li>this upper bound becomes the lower bound of the next interval, always
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using the orginal upper bound and repeat the process until you can cover |
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the all initial interval with the computed sub-interval.</li>
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</ol>
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<p>In step 2 you may have to compute several polynomials until the computed
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error is below or equal to the expected error. But you must not decrease the |
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interval width too agressively: there is no point in getting a too small |
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approximation error since it multiplies the number of interval (and |
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lengthens the subsequent manipulations that are made on each sub-interval).</p>
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<p>In step 3 you probably inutily compute polynomials for too large intervals
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and you could know it from the previous computations.</p>
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<p>The whole idea is to reduce the number of polynomial computations and get the
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maximum width (compatible with the target error).</p>
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<p>This process has different aspects:</p> |
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<ol>
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<li>for a given interval (be it too small or too large) and the given
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(computed) error, compute the new best upper bound;</li>
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<li>reuse the computation already done in previous steps.</li> |
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</ol>
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<p>We use two strategies.</p> |
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<p>The first is in the inner function that computes the first approximation
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polynomial. If the upper bound is too high, we shrink the interval by a non |
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linear (and complex) factor computed from the ratio beetwen the obtained |
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error and the target error.<br>The whole idea is not to end up with a too
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small interval.</p>
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<p>This only takes into account aspect 1</p>. |
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<p>The second strategy is in the outer function. In this function the
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computed intervals returned by the inner function give all correct |
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approximation errors. The computation of next upper bound will take into |
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account the value of the previous interval and that of the previous |
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computed error.</p>
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<p>If the errors ratio is lower than 2, we try the same interval length
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as given by the previous computation. If larger we increase the next |
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interval but by a small margin (log2(errorsRatio).</p>
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<p>In this case, aspects 1 and 2 are considered </p> |
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<p>For the moment, we do not resort to more complex forcasting techniques
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(using the derivative of the function?). This could be an option if the |
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present strategies did not output enough performance. </p>
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