Matrix2

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1. final def !=(arg0: AnyRef): Boolean

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2. final def !=(arg0: Any): Boolean

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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5. final def ==(arg0: Any): Boolean

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6. def J[R, C, V](implicit ordR: Ordering[R], ordC: Ordering[C], ring: Ring[V], mj: MatrixJoiner2): Product[R, Unit, C, V]

7. def apply[R, C, V](t: TypedPipe[(R, C, V)], hint: SizeHint)(implicit arg0: Ordering[R], arg1: Ordering[C]): Matrix2[R, C, V]

8. final def asInstanceOf[T0]: T0

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9. def clone(): AnyRef

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10. final def eq(arg0: AnyRef): Boolean

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11. def equals(arg0: Any): Boolean

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12. def finalize(): Unit

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13. final def getClass(): Class[_]

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14. def hashCode(): Int

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15. final def isInstanceOf[T0]: Boolean

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16. final def ne(arg0: AnyRef): Boolean

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17. final def notify(): Unit

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18. final def notifyAll(): Unit

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19. def optimize[V](mf: Matrix2[Any, Any, V]): (BigInt, Matrix2[Any, Any, V])

    This function walks the input tree, finds basic blocks to optimize, i.

    This function walks the input tree, finds basic blocks to optimize, i.e. matrix product chains that are not interrupted by summations. One example: A*B*C*(D+E)*(F*G) => "basic blocks" are ABC, D, E, and FG

    + it now does "global" optimization - i.e. over optimize over basic blocks. In the above example, we'd treat (D+E) as a temporary matrix T and optimize the whole chain ABCTFG

    Not sure if making use of distributivity to generate more variants would be good. In the above example, we could also generate ABCDFG + ABCEFG and have basic blocks: ABCDFG, and ABCEFG. But this would be almost twice as much work with the current cost estimation.

20. def optimizeProductChain[V](p: IndexedSeq[Matrix2[Any, Any, V]], product: Option[(Ring[V], MatrixJoiner2)]): (BigInt, Matrix2[Any, Any, V])

    The original prototype that employs the standard O(n^3) dynamic programming procedure to optimize a matrix chain factorization.

    The original prototype that employs the standard O(n^3) dynamic programming procedure to optimize a matrix chain factorization.

    Now, it also "prefers" more spread out / bushy / less deep factorization which reflects more the Map/Reduce nature.

21. def read[R, C, V](t: TypedSource[(R, C, V)], hint: SizeHint)(implicit ordr: Ordering[R], ordc: Ordering[C]): Matrix2[R, C, V]

22. final def synchronized[T0](arg0: ⇒ T0): T0

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23. def toString(): String

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24. final def wait(): Unit

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25. final def wait(arg0: Long, arg1: Int): Unit

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26. final def wait(arg0: Long): Unit

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