Abstract: Techniques herein train a multilayer perceptron, sparsify edges of a graph such as the perceptron, and store edges and vertices of the graph. Each edge has weight. A computer sparsifies perceptron edges. The computer performs a forward-backward pass on the perceptron to calculate a sparse Hessian matrix. Based on that Hessian, the computer performs quasi-Newton perceptron optimization. The computer repeats this until convergence. The computer stores edges in an array and vertices in another array. Each edge has weight and input and output indices. Each vertex has input and output indices. The computer inserts each edge into an input linked list based on its weight. Each link of the input linked list has the next input index of an edge. The computer inserts each edge into an output linked list based on its weight. Each link of the output linked list comprises the next output index of an edge.

Abstract: Techniques herein train a multilayer perceptron, sparsify edges of a graph such as the perceptron, and store edges and vertices of the graph. Each edge has weight. A computer sparsifies perceptron edges. The computer performs a forward-backward pass on the perceptron to calculate a sparse Hessian matrix. Based on that Hessian, the computer performs quasi-Newton perceptron optimization. The computer repeats this until convergence. The computer stores edges in an array and vertices in another array. Each edge has weight and input and output indices. Each vertex has input and output indices. The computer inserts each edge into an input linked list based on its weight. Each link of the input linked list has the next input index of an edge. The computer inserts each edge into an output linked list based on its weight. Each link of the output linked list comprises the next output index of an edge.

Abstract: Techniques herein train a multilayer perceptron, sparsify edges of a graph such as the perceptron, and store edges and vertices of the graph. Each edge has weight. A computer sparsifies perceptron edges. The computer performs a forward-backward pass on the perceptron to calculate a sparse Hessian matrix. Based on that Hessian, the computer performs quasi-Newton perceptron optimization. The computer repeats this until convergence. The computer stores edges in an array and vertices in another array. Each edge has weight and input and output indices. Each vertex has input and output indices. The computer inserts each edge into an input linked list based on its weight. Each link of the input linked list has the next input index of an edge. The computer inserts each edge into an output linked list based on its weight. Each link of the output linked list comprises the next output index of an edge.

Abstract: Techniques herein are for sharing data structures. In embodiments, a computer obtains a directed object graph (DOG) containing objects and pointers interconnecting the objects. Each object pointer (OP) resides in a source object and comprises a memory address (MA) of a target object (TO). An original address space (OAS) contains the MA of the TO. The objects are not contiguous within the OAS. The DOG resides in original memory segment(s). The computer obtains an additional memory segment (AMS) beginning at a base address. The computer records the base address within the AMS. For each object in the DOG, the computer copies the object into the AMS at a respective address. For each OP in the DOG having the object as the TO of the MA of the OP, the computer replaces the MA of the OP with the respective address. AMS contents are provided in another address space.

Abstract: According to one technique, a modeling computer computes a Hessian matrix by determining whether an input matrix contains more than a threshold number of dense columns. If so, the modeling computer computes a sparsified version of the input matrix and uses the sparsified matrix to compute the Hessian. Otherwise, the modeling computer identifies which columns are dense and which columns are sparse. The modeling computer then partitions the input matrix by column density and uses sparse matrix format to store the sparse columns and dense matrix format to store the dense columns. The modeling computer then computes component parts which combine to form the Hessian, wherein component parts that rely on dense columns are computed using dense matrix multiplication and component parts that rely on sparse columns are computed using sparse matrix multiplication.

Abstract: Techniques herein are for sharing data structures. In embodiments, a computer obtains a directed object graph (DOG) containing objects and pointers interconnecting the objects. Each object pointer (OP) resides in a source object and comprises a memory address (MA) of a target object (TO). An original address space (OAS) contains the MA of the TO. The objects are not contiguous within the OAS. The DOG resides in original memory segment(s). The computer obtains an additional memory segment (AMS) beginning at a base address. The computer records the base address within the AMS. For each object in the DOG, the computer copies the object into the AMS at a respective address. For each OP in the DOG having the object as the TO of the MA of the OP, the computer replaces the MA of the OP with the respective address. AMS contents are provided in another address space.

Abstract: According to one technique, a modeling computer computes a Hessian matrix by determining whether an input matrix contains more than a threshold number of dense columns. If so, the modeling computer computes a sparsified version of the input matrix and uses the sparsified matrix to compute the Hessian. Otherwise, the modeling computer identifies which columns are dense and which columns are sparse. The modeling computer then partitions the input matrix by column density and uses sparse matrix format to store the sparse columns and dense matrix format to store the dense columns. The modeling computer then computes component parts which combine to form the Hessian, wherein component parts that rely on dense columns are computed using dense matrix multiplication and component parts that rely on sparse columns are computed using sparse matrix multiplication.

Abstract: Techniques herein are for sharing data structures between processes. A method involves obtaining a current memory segment that begins at a current base address within a current address space. The current memory segment comprises a directed object graph and a base pointer. The graph comprises object pointers and objects. For each particular object, determine whether a different memory segment contains an equivalent object that is equivalent to the particular object. If the equivalent object exists, for each object pointer having the particular object as its target object, replace the memory address of the object pointer with a memory address of the equivalent object that does not reside in the current memory segment. Otherwise, for each object pointer having the particular object as its target object, increment the memory address of the object pointer by an amount that is a difference between the current base address and the original base address.

Abstract: According to one technique, a modeling computer computes a Hessian matrix by determining whether an input matrix contains more than a threshold number of dense columns. If so, the modeling computer computes a sparsified version of the input matrix and uses the sparsified matrix to compute the Hessian. Otherwise, the modeling computer identifies which columns are dense and which columns are sparse. The modeling computer then partitions the input matrix by column density and uses sparse matrix format to store the sparse columns and dense matrix format to store the dense columns. The modeling computer then computes component parts which combine to form the Hessian, wherein component parts that rely on dense columns are computed using dense matrix multiplication and component parts that rely on sparse columns are computed using sparse matrix multiplication.

Abstract: Techniques herein are for sharing data structures between processes. A method involves obtaining a current memory segment that begins at a current base address within a current address space. The current memory segment comprises a directed object graph and a base pointer. The graph comprises object pointers and objects. For each particular object, determine whether a different memory segment contains an equivalent object that is equivalent to the particular object. If the equivalent object exists, for each object pointer having the particular object as its target object, replace the memory address of the object pointer with a memory address of the equivalent object that does not reside in the current memory segment. Otherwise, for each object pointer having the particular object as its target object, increment the memory address of the object pointer by an amount that is a difference between the current base address and the original base address.

Abstract: Techniques herein are for sharing data structures between processes. A method involves obtaining a current memory segment that begins at a current base address within a current address space. The current memory segment comprises a directed object graph and a base pointer. The graph comprises object pointers and objects. For each particular object, determine whether a different memory segment contains an equivalent object that is equivalent to the particular object. If the equivalent object exists, for each object pointer having the particular object as its target object, replace the memory address of the object pointer with a memory address of the equivalent object that does not reside in the current memory segment. Otherwise, for each object pointer having the particular object as its target object, increment the memory address of the object pointer by an amount that is a difference between the current base address and the original base address.

Abstract: Techniques herein train a multilayer perceptron, sparsify edges of a graph such as the perceptron, and store edges and vertices of the graph. Each edge has weight. A computer sparsifies perceptron edges. The computer performs a forward-backward pass on the perceptron to calculate a sparse Hessian matrix. Based on that Hessian, the computer performs quasi-Newton perceptron optimization. The computer repeats this until convergence. The computer stores edges in an array and vertices in another array. Each edge has weight and input and output indices. Each vertex has input and output indices. The computer inserts each edge into an input linked list based on its weight. Each link of the input linked list has the next input index of an edge. The computer inserts each edge into an output linked list based on its weight. Each link of the output linked list comprises the next output index of an edge.

Abstract: A method, system, and computer program product for interfacing an R language client with a separate database engine environment. The method commences by interpreting an R language code fragment to identify and select R language constructs and transforming the R language constructs into queries or other database language constructs to execute within the database engine environment. The method further implements techniques for transmitting marshalled results (resulting from the execution of the database language constructs) back to the R client environment. In some situations, the marshalled results include an XML schema or DTD or another metadata description of the structure of the results.

Abstract: According to one technique, a modeling computer computes a Hessian matrix by determining whether an input matrix contains more than a threshold number of dense columns. If so, the modeling computer computes a sparsified version of the input matrix and uses the sparsified matrix to compute the Hessian. Otherwise, the modeling computer identifies which columns are dense and which columns are sparse. The modeling computer then partitions the input matrix by column density and uses sparse matrix format to store the sparse columns and dense matrix format to store the dense columns. The modeling computer then computes component parts which combine to form the Hessian, wherein component parts that rely on dense columns are computed using dense matrix multiplication and component parts that rely on sparse columns are computed using sparse matrix multiplication.

Abstract: According to one aspect of the invention, target data comprising observations is received. A neural network comprising input neurons, output neurons, hidden neurons, skip-layer connections, and non-skip-layer connections is used to analyze the target data based on an overall objective function that comprises a linear regression part, the neural network's unregularized objective function, and a regularization term. An overall optimized first vector value of a first vector and an overall optimized second vector value of a second vector are determined based on the target data and the overall objective function. The first vector comprises skip-layer weights for the skip-layer connections and output neuron biases, whereas the second vector comprises non-skip-layer weights for the non-skip-layer connections.

Abstract: A method, system, and computer program product for interfacing an R language client with a separate database engine environment. The method commences by interpreting an R language code fragment to identify and select R language constructs and transforming the R language constructs into queries or other database language constructs to execute within the database engine environment. The method further implements techniques for transmitting marshalled results (resulting from the execution of the database language constructs) back to the R client environment. In some situations, the marshalled results include an XML schema or DTD or another metadata description of the structure of the results.

Abstract: According to one aspect of the invention, target data comprising observations is received. A neural network comprising input neurons, output neurons, hidden neurons, skip-layer connections, and non-skip-layer connections is used to analyze the target data based on an overall objective function that comprises a linear regression part, the neural network's unregularized objective function, and a regularization term. An overall optimized first vector value of a first vector and an overall optimized second vector value of a second vector are determined based on the target data and the overall objective function. The first vector comprises skip-layer weights for the skip-layer connections and output neuron biases, whereas the second vector comprises non-skip-layer weights for the non-skip-layer connections.

Abstract: A method of quota planning. In one embodiment, the method includes determining a top-down goal. The top-down goal indicates an expected amount of sales for a sales territory of a sales territory hierarchy. The method also includes generating a bottom-up recommendation for the sales territory and specifying a quota for the sales territory. The bottom-up recommendation is reconciled with the top-down goal, resulting in a quota that indicates an assigned amount of sales for the sales territory.

Abstract: A method, system, and computer program product for interfacing an R language client with a separate database engine environment. The method commences by interpreting an R language code fragment to identify and select R language constructs and transforming the R language constructs into queries or other database language constructs to execute within the database engine environment. The method further implements techniques for transmitting marshalled results (resulting from the execution of the database language constructs) back to the R client environment. In some situations, the marshalled results include an XML schema or DTD or another metadata description of the structure of the results.

Abstract: A method, system, and computer program product for interfacing an R language client with a separate database engine environment. The method commences by interpreting an R language code fragment to identify and select R language constructs and transforming the R language constructs into queries or other database language constructs to execute within the database engine environment. The method further implements techniques for transmitting marshaled results (resulting from the execution of the database language constructs) back to the R client environment. In some situations, the marshaled results include an XML schema or DTD or another metadata description of the structure of the results.